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  "description": "A supplementary export of visible, selected editorial content. Local mode is a publication preview, not a public index. This file is not a ranking directive.",
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      "value": "15 GPU models and 6,300 modeled cards across 3 US regions.",
      "basis": "Supplied catalog, not live stock",
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      "value": "One week = 7 days; one month = 30 days. USD-denominated fixed-term quotes.",
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      "id": "crypto",
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      "value": "Check the asset, network, amount, and order reference before any transfer. Payment confirmation and order fulfillment are separate steps.",
      "basis": "Service scope",
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      "source": "https://anchorgpu.com/docs/crypto-funding"
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      "url": "https://anchorgpu.com/blog",
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      "url": "https://anchorgpu.com/support",
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      "url": "https://anchorgpu.com/faq/hardware",
      "title": "GPU Rental & Hardware FAQ",
      "description": "Dedicated allocation, GPU choice, availability, node sizes, and provisioning.",
      "updatedAt": "2026-09-04",
      "family": "faq"
    },
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      "url": "https://anchorgpu.com/faq/accounts",
      "title": "Accounts & Access FAQ",
      "description": "Pseudonymous signup, recovery codes, SSH keys, sessions, and API keys.",
      "updatedAt": "2026-09-04",
      "family": "faq"
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      "url": "https://anchorgpu.com/faq/pricing",
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      "updatedAt": "2026-09-04",
      "family": "faq"
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    {
      "url": "https://anchorgpu.com/faq/crypto",
      "title": "Crypto Payments FAQ",
      "description": "Supported networks, confirmations, deposit quotes, ledger credits, and withdrawals.",
      "updatedAt": "2026-09-04",
      "family": "faq"
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      "url": "https://anchorgpu.com/faq/lifecycle",
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      "family": "faq"
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    {
      "url": "https://anchorgpu.com/faq/networking-storage",
      "title": "Networking, Storage & Images FAQ",
      "description": "Machine images, containers, local NVMe, public addresses, and bandwidth.",
      "updatedAt": "2026-09-04",
      "family": "faq"
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    {
      "url": "https://anchorgpu.com/faq/security",
      "title": "Security & Privacy FAQ",
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      "family": "faq"
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      "url": "https://anchorgpu.com/faq/refunds-used-hardware",
      "title": "Refunds & Used Hardware FAQ",
      "description": "Corrections, service credits, condition reports, delivery, warranty, and returns.",
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      "family": "faq"
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      "url": "https://anchorgpu.com/compare/nvidia-h200-sxm-vs-amd-mi300x",
      "title": "H200 SXM vs MI300X: memory headroom or CUDA continuity?",
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      "title": "H100 SXM vs H100 PCIe: same capacity, different system design",
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      "title": "H100 SXM vs A100 SXM: is Hopper worth the premium?",
      "description": "Compare H100 SXM and A100 SXM: 80 vs 80 GB memory, software requirements, fixed-term costs and workload tradeoffs.",
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      "updatedAt": "2026-09-04",
      "family": "docs"
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      "updatedAt": "2026-09-04",
      "family": "docs"
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      "url": "https://anchorgpu.com/docs/billing-ledger",
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      "family": "docs"
    },
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      "url": "https://anchorgpu.com/docs/crypto-funding",
      "title": "Deposits & withdrawals",
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      "family": "docs"
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      "family": "docs"
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      "family": "docs"
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      "family": "docs"
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      "url": "https://anchorgpu.com/docs/troubleshooting",
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      "description": "Resolve sign-in, SSH key, quote, balance and instance-state problems in the local workflow.",
      "updatedAt": "2026-09-04",
      "family": "docs"
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      "url": "https://anchorgpu.com/docs/serve-llm-with-vllm",
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      "updatedAt": "2026-09-04",
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      "url": "https://anchorgpu.com/docs/pytorch-fine-tuning",
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      "updatedAt": "2026-09-04",
      "family": "docs"
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      "url": "https://anchorgpu.com/blog/how-much-vram-do-you-need",
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      "url": "https://anchorgpu.com/blog/best-gpu-for-fine-tuning",
      "title": "Best GPU for fine-tuning: A100, H100, H200, or MI300X?",
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      "updatedAt": "2026-09-04",
      "family": "article"
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      "url": "https://anchorgpu.com/blog/gpu-for-llm-inference",
      "title": "Choosing a GPU for LLM inference in production",
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      "updatedAt": "2026-09-04",
      "family": "article"
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      "url": "https://anchorgpu.com/blog/gpu-for-ai-video",
      "title": "Which GPU should you rent for AI video?",
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      "updatedAt": "2026-09-04",
      "family": "article"
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    {
      "url": "https://anchorgpu.com/blog/renting-vs-buying-gpus",
      "title": "GPU rental vs buying: a 30-day cost framework",
      "description": "Compare useful capacity, financing, power, cooling, downtime, and resale value—not just the card price against one month of rent.",
      "updatedAt": "2026-09-04",
      "family": "article"
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      "path": "/blog/how-much-vram-do-you-need",
      "title": "How much GPU VRAM do you actually need?",
      "question": "How much GPU memory does a workload actually need?",
      "answer": "Budget model weights, runtime workspaces and the workload’s peak working memory. Inference adds a KV cache; training adds gradients, optimizer state and saved activations. Measure the complete request or training step at the intended context, batch and concurrency before choosing a card.",
      "highlights": [
        "A parameter count is not a complete memory requirement.",
        "Multiple GPUs do not automatically create one contiguous memory pool.",
        "Quantization, offload and sharding need workload-specific validation."
      ],
      "limitation": "Workload guidance, not an AnchorGPU benchmark or capacity guarantee. Validate the actual model, software and hardware configuration.",
      "updatedAt": "2026-09-04",
      "sources": [
        {
          "label": "vLLM — parallelism and scaling",
          "url": "https://docs.vllm.ai/en/latest/serving/parallelism_scaling/",
          "evidence": "official-documentation"
        },
        {
          "label": "PyTorch — FullyShardedDataParallel",
          "url": "https://docs.pytorch.org/docs/stable/fsdp.html",
          "evidence": "official-documentation"
        },
        {
          "label": "PyTorch — activation checkpointing",
          "url": "https://docs.pytorch.org/docs/stable/checkpoint.html",
          "evidence": "official-documentation"
        }
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      "url": "https://anchorgpu.com/blog/how-much-vram-do-you-need#short-answer"
    },
    {
      "path": "/blog/best-gpu-for-fine-tuning",
      "title": "Best GPU for fine-tuning: A100, H100, H200, or MI300X?",
      "question": "Which GPU should I choose for fine-tuning?",
      "answer": "Start with the smallest tested configuration that completes the full forward, backward and optimizer step. This modeled catalog lists A100 and H100 at 80 GB, H200 at 141 GB and MI300X at 192 GB. Compare software compatibility, measured training output and complete fixed-term cost, not theoretical speed alone.",
      "highlights": [
        "Qualify the optimizer, precision, batch and sequence length together.",
        "Test CUDA or ROCm dependencies before reserving a term.",
        "Prove checkpoint restore before scaling the run."
      ],
      "limitation": "Workload guidance, not an AnchorGPU benchmark or capacity guarantee. Validate the actual model, software and hardware configuration.",
      "updatedAt": "2026-09-04",
      "sources": [
        {
          "label": "PyTorch — distributed training overview",
          "url": "https://docs.pytorch.org/tutorials/beginner/dist_overview.html",
          "evidence": "official-documentation"
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        {
          "label": "PyTorch — peak tensor memory",
          "url": "https://docs.pytorch.org/docs/stable/generated/torch.cuda.max_memory_allocated.html",
          "evidence": "official-documentation"
        },
        {
          "label": "AMD — PyTorch on ROCm",
          "url": "https://rocm.docs.amd.com/en/docs-6.4.0/compatibility/ml-compatibility/pytorch-compatibility.html",
          "evidence": "official-documentation"
        },
        {
          "label": "NVIDIA — H200 memory specifications",
          "url": "https://www.nvidia.com/en-us/data-center/h200/",
          "evidence": "official-documentation"
        },
        {
          "label": "Modeled memory capacities in the catalog",
          "url": "https://anchorgpu.com/reference#catalog",
          "evidence": "catalog-model"
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      "url": "https://anchorgpu.com/blog/best-gpu-for-fine-tuning#short-answer"
    },
    {
      "path": "/blog/gpu-for-llm-inference",
      "title": "Choosing a GPU for LLM inference in production",
      "question": "How should I choose a GPU for LLM inference?",
      "answer": "Choose from a tested serving target: model revision, quantization, prompt length, output length, concurrency and latency. Include KV-cache memory and runtime overhead. Compare replicas with model sharding on the actual node topology, then calculate cost from successfully served output and the complete rental term.",
      "highlights": [
        "Model loading alone is not a capacity test.",
        "Report latency, throughput and failures together.",
        "Idle time remains part of a fixed-term rental’s cost."
      ],
      "limitation": "Workload guidance, not an AnchorGPU benchmark or capacity guarantee. Validate the actual model, software and hardware configuration.",
      "updatedAt": "2026-09-04",
      "sources": [
        {
          "label": "vLLM — quickstart and serving",
          "url": "https://docs.vllm.ai/en/latest/getting_started/quickstart/",
          "evidence": "official-documentation"
        },
        {
          "label": "vLLM — distributed serving and parallelism",
          "url": "https://docs.vllm.ai/en/latest/serving/parallelism_scaling/",
          "evidence": "official-documentation"
        },
        {
          "label": "vLLM — GPU installation requirements",
          "url": "https://docs.vllm.ai/en/latest/getting_started/installation/gpu/",
          "evidence": "official-documentation"
        }
      ],
      "url": "https://anchorgpu.com/blog/gpu-for-llm-inference#short-answer"
    },
    {
      "path": "/blog/gpu-for-ai-video",
      "title": "Which GPU should you rent for AI video?",
      "question": "What determines the GPU needed for AI video?",
      "answer": "The complete pipeline determines capacity: model and precision, resolution, generated frame count, conditioning inputs, denoising and VAE decoding. Measure both device memory and end-to-end time on a representative clip. Offload or chunking can reduce a memory peak but may increase time or affect output quality.",
      "highlights": [
        "Generated frame count is different from export FPS.",
        "The denoiser and VAE can peak at different stages.",
        "Check codec support on the exact GPU and software version."
      ],
      "limitation": "Workload guidance, not an AnchorGPU benchmark or capacity guarantee. Validate the actual model, software and hardware configuration.",
      "updatedAt": "2026-09-04",
      "sources": [
        {
          "label": "Hugging Face Diffusers — Stable Video Diffusion",
          "url": "https://huggingface.co/docs/diffusers/main/api/pipelines/stable_diffusion/svd",
          "evidence": "official-documentation"
        },
        {
          "label": "Hugging Face Diffusers — reduce memory usage",
          "url": "https://huggingface.co/docs/diffusers/main/optimization/memory",
          "evidence": "official-documentation"
        },
        {
          "label": "NVIDIA Video Codec SDK 13.1 — NVENC application note",
          "url": "https://docs.nvidia.com/video-technologies/video-codec-sdk/13.1/nvenc-application-note/index.html",
          "evidence": "official-documentation"
        }
      ],
      "url": "https://anchorgpu.com/blog/gpu-for-ai-video#short-answer"
    },
    {
      "path": "/blog/renting-vs-buying-gpus",
      "title": "GPU rental vs buying: a 30-day cost framework",
      "question": "When does GPU rental make more sense than buying?",
      "answer": "Compare equivalent useful capacity over the same workload horizon. Ownership includes the complete host, running costs, operator time and uncertain resale value. Rental includes the whole fixed term and selected options, including idle time. Test lower utilization and a repair scenario before choosing; neither option is always cheaper.",
      "highlights": [
        "A bare-card price is not comparable to a complete rented node.",
        "Use measured whole-system electricity consumption.",
        "Cost examples in this guide are hypothetical, not binding offers."
      ],
      "limitation": "Workload guidance, not an AnchorGPU benchmark or capacity guarantee. Validate the actual model, software and hardware configuration.",
      "updatedAt": "2026-09-04",
      "sources": [
        {
          "label": "EIA — kilowatts and kilowatthours",
          "url": "https://www.eia.gov/tools/glossary/index.php?id=Electric",
          "evidence": "official-documentation"
        },
        {
          "label": "NVIDIA — A100 board specifications and maximum TDP",
          "url": "https://www.nvidia.com/en-us/data-center/a100/",
          "evidence": "official-documentation"
        }
      ],
      "url": "https://anchorgpu.com/blog/renting-vs-buying-gpus#short-answer"
    },
    {
      "path": "/docs/quickstart",
      "title": "Prepare your first node request",
      "question": "How do I prepare a GPU node request?",
      "answer": "Create an account, save the recovery codes, add a public SSH key, then review the GPU, region, image, term, options, and total price. Submit the configuration only after checking every field; an order is confirmed only when the connected payment and provisioning services return confirmed states. The current workflow saves configuration and lifecycle records but does not provision physical hardware or transfer funds.",
      "highlights": [
        "Keep private keys and recovery codes out of support messages.",
        "Review the fixed period and selected options.",
        "A saved instance record is not a reachable physical server."
      ],
      "limitation": "AnchorGPU provides catalog and configuration reference material. Crypto payments are received on the addresses shown at checkout; provisioning and availability are confirmed by the operator for each order.",
      "updatedAt": "2026-09-04",
      "sources": [
        {
          "label": "Product implementation notes · 1. Create your account",
          "url": "https://anchorgpu.com/docs/quickstart#account",
          "evidence": "product-documentation"
        },
        {
          "label": "Product implementation notes · 2. Save an SSH public key",
          "url": "https://anchorgpu.com/docs/quickstart#key",
          "evidence": "product-documentation"
        },
        {
          "label": "Product implementation notes · 3. Review funding requirements",
          "url": "https://anchorgpu.com/docs/quickstart#balance",
          "evidence": "product-documentation"
        },
        {
          "label": "Product implementation notes · 4. Review and submit the request",
          "url": "https://anchorgpu.com/docs/quickstart#deploy",
          "evidence": "product-documentation"
        }
      ],
      "url": "https://anchorgpu.com/docs/quickstart#short-answer"
    },
    {
      "path": "/docs/ssh-access",
      "title": "SSH keys & access",
      "question": "What is required to connect to a GPU node over SSH?",
      "answer": "A real node needs your public SSH key installed and a verified reachable endpoint. Keep the private key on your own device and verify the host identity before connecting. The configuration interface saves supported public keys but does not provision a remote SSH host.",
      "highlights": [
        "Never upload a private key.",
        "A saved public key does not prove a machine exists.",
        "Use the endpoint of the actual provisioned allocation."
      ],
      "limitation": "AnchorGPU provides catalog and configuration reference material. Crypto payments are received on the addresses shown at checkout; provisioning and availability are confirmed by the operator for each order.",
      "updatedAt": "2026-09-04",
      "sources": [
        {
          "label": "Product implementation notes · Generate an Ed25519 key",
          "url": "https://anchorgpu.com/docs/ssh-access#generate",
          "evidence": "product-documentation"
        },
        {
          "label": "Product implementation notes · Register the complete public key",
          "url": "https://anchorgpu.com/docs/ssh-access#register",
          "evidence": "product-documentation"
        },
        {
          "label": "Product implementation notes · Use and revoke a key",
          "url": "https://anchorgpu.com/docs/ssh-access#access",
          "evidence": "product-documentation"
        }
      ],
      "url": "https://anchorgpu.com/docs/ssh-access#short-answer"
    },
    {
      "path": "/docs/billing-ledger",
      "title": "Billing & your ledger",
      "question": "How is the AnchorGPU balance calculated?",
      "answer": "The balance is the sum of signed ledger entries. A confirmed rental would charge the complete 7- or 30-day configuration, including options and applicable node discounts. Quote, deployment, renewal, and withdrawal mutations use replay safeguards, and corrections require new entries rather than rewriting history. Until payment reconciliation is connected, displayed records are not evidence of settled funds.",
      "highlights": [
        "A quote alone does not credit or debit the balance.",
        "Stopping does not pause a fixed paid term.",
        "Displayed credits and charges are configuration records, not proof of settled customer transactions."
      ],
      "limitation": "AnchorGPU provides catalog and configuration reference material. Crypto payments are received on the addresses shown at checkout; provisioning and availability are confirmed by the operator for each order.",
      "updatedAt": "2026-09-04",
      "sources": [
        {
          "label": "Product implementation notes · Fixed terms, not hourly metering",
          "url": "https://anchorgpu.com/docs/billing-ledger#terms",
          "evidence": "product-documentation"
        },
        {
          "label": "Product implementation notes · Know what is included",
          "url": "https://anchorgpu.com/docs/billing-ledger#options",
          "evidence": "product-documentation"
        },
        {
          "label": "Product implementation notes · Read the account ledger",
          "url": "https://anchorgpu.com/docs/billing-ledger#ledger",
          "evidence": "product-documentation"
        }
      ],
      "url": "https://anchorgpu.com/docs/billing-ledger#short-answer"
    },
    {
      "path": "/docs/crypto-funding",
      "title": "Deposits & withdrawals",
      "question": "Can I fund the local wallet with real cryptocurrency?",
      "answer": "Not yet. The checkout contains real receiving addresses, but payment matching and confirmation are not connected. Do not send funds without a confirmed payment request linked to your order. An address or conversion alone is not a payment request.",
      "highlights": [
        "Never send funds unless an order identifies a verified production destination.",
        "An asset and its network are one payment choice.",
        "A repeated confirmation must not create a second credit."
      ],
      "limitation": "AnchorGPU provides catalog and configuration reference material. Crypto payments are received on the addresses shown at checkout; provisioning and availability are confirmed by the operator for each order.",
      "updatedAt": "2026-09-04",
      "sources": [
        {
          "label": "Product implementation notes · Create a deposit quote",
          "url": "https://anchorgpu.com/docs/crypto-funding#quote",
          "evidence": "product-documentation"
        },
        {
          "label": "Product implementation notes · Credit your local account",
          "url": "https://anchorgpu.com/docs/crypto-funding#credit",
          "evidence": "product-documentation"
        },
        {
          "label": "Product implementation notes · Record a withdrawal request",
          "url": "https://anchorgpu.com/docs/crypto-funding#withdraw",
          "evidence": "product-documentation"
        }
      ],
      "url": "https://anchorgpu.com/docs/crypto-funding#short-answer"
    },
    {
      "path": "/docs/instance-lifecycle",
      "title": "Manage an instance",
      "question": "What is the difference between stopping, renewing and releasing?",
      "answer": "Stopping is reversible within an eligible allocation and does not extend its paid term. Renewal adds the next fixed period and creates a corresponding charge. Release ends the allocation permanently in the product model. The configuration workflow stores these lifecycle states but does not control physical hardware.",
      "highlights": [
        "Review auto-renew before the next term.",
        "A low balance can prevent renewal.",
        "Back up required artifacts before a real allocation ends."
      ],
      "limitation": "AnchorGPU provides catalog and configuration reference material. Crypto payments are received on the addresses shown at checkout; provisioning and availability are confirmed by the operator for each order.",
      "updatedAt": "2026-09-04",
      "sources": [
        {
          "label": "Product implementation notes · From running to released",
          "url": "https://anchorgpu.com/docs/instance-lifecycle#states",
          "evidence": "product-documentation"
        },
        {
          "label": "Product implementation notes · Renew manually or automatically",
          "url": "https://anchorgpu.com/docs/instance-lifecycle#renewal",
          "evidence": "product-documentation"
        },
        {
          "label": "Product implementation notes · Expiry and the 72-hour grace window",
          "url": "https://anchorgpu.com/docs/instance-lifecycle#expiry",
          "evidence": "product-documentation"
        }
      ],
      "url": "https://anchorgpu.com/docs/instance-lifecycle#short-answer"
    },
    {
      "path": "/docs/machine-images",
      "title": "Choose a machine image",
      "question": "Does choosing a GPU image guarantee software compatibility?",
      "answer": "No. A catalog image is a starting configuration, not proof that a particular model or extension will run. Match the GPU vendor, architecture, driver and CUDA or ROCm build, then test the complete workload. The configuration interface validates catalog choices but does not boot a container or machine.",
      "highlights": [
        "Record exact versions and container tags.",
        "Qualify custom operators and quantization kernels.",
        "Do not treat a catalog label as an installed environment."
      ],
      "limitation": "AnchorGPU provides catalog and configuration reference material. Crypto payments are received on the addresses shown at checkout; provisioning and availability are confirmed by the operator for each order.",
      "updatedAt": "2026-09-04",
      "sources": [
        {
          "label": "Product implementation notes · Start with the workload",
          "url": "https://anchorgpu.com/docs/machine-images#selection",
          "evidence": "product-documentation"
        },
        {
          "label": "Product implementation notes · Images in the catalog",
          "url": "https://anchorgpu.com/docs/machine-images#catalog",
          "evidence": "product-documentation"
        },
        {
          "label": "Product implementation notes · Keep workloads reproducible",
          "url": "https://anchorgpu.com/docs/machine-images#reproducibility",
          "evidence": "product-documentation"
        }
      ],
      "url": "https://anchorgpu.com/docs/machine-images#short-answer"
    },
    {
      "path": "/docs/api-reference",
      "title": "API reference",
      "question": "What can the local AnchorGPU API do?",
      "answer": "The API supports account sessions, saved keys, configuration quotes, and persisted lifecycle records. Use a separate API key for each client, keep secrets server-side, and use idempotency keys for supported monetary and deployment requests. Payment and provisioning adapters are not connected, so responses do not confirm settled funds or physical GPU allocation.",
      "highlights": [
        "Private endpoints require authentication.",
        "Never put an API secret in a public URL.",
        "An API response from the configuration service is not proof of production availability."
      ],
      "limitation": "AnchorGPU provides catalog and configuration reference material. Crypto payments are received on the addresses shown at checkout; provisioning and availability are confirmed by the operator for each order.",
      "updatedAt": "2026-09-04",
      "sources": [
        {
          "label": "Product implementation notes · Authentication",
          "url": "https://anchorgpu.com/docs/api-reference#authentication",
          "evidence": "product-documentation"
        },
        {
          "label": "Product implementation notes · Request and retry rules",
          "url": "https://anchorgpu.com/docs/api-reference#requests",
          "evidence": "product-documentation"
        },
        {
          "label": "Product implementation notes · Implemented endpoints",
          "url": "https://anchorgpu.com/docs/api-reference#endpoints",
          "evidence": "product-documentation"
        },
        {
          "label": "Product implementation notes · Handle errors explicitly",
          "url": "https://anchorgpu.com/docs/api-reference#errors",
          "evidence": "product-documentation"
        }
      ],
      "url": "https://anchorgpu.com/docs/api-reference#short-answer"
    },
    {
      "path": "/docs/troubleshooting",
      "title": "Troubleshooting",
      "question": "Where should I start when a deployment fails?",
      "answer": "Check the saved configuration, compatible image, available demo balance and required public SSH key. Review the specific response before retrying, and preserve the idempotency key when repeating the same operation. Real driver, network and workload troubleshooting requires access to the actual provisioned host.",
      "highlights": [
        "Do not share passwords, keys or recovery codes in diagnostics.",
        "Quotes can become stale before confirmation.",
        "The local edition cannot diagnose physical GPU health."
      ],
      "limitation": "AnchorGPU provides catalog and configuration reference material. Crypto payments are received on the addresses shown at checkout; provisioning and availability are confirmed by the operator for each order.",
      "updatedAt": "2026-09-04",
      "sources": [
        {
          "label": "Product implementation notes · I cannot sign in",
          "url": "https://anchorgpu.com/docs/troubleshooting#signin",
          "evidence": "product-documentation"
        },
        {
          "label": "Product implementation notes · My deposit did not change the balance",
          "url": "https://anchorgpu.com/docs/troubleshooting#balance",
          "evidence": "product-documentation"
        },
        {
          "label": "Product implementation notes · Deployment is unavailable or rejected",
          "url": "https://anchorgpu.com/docs/troubleshooting#deployment",
          "evidence": "product-documentation"
        },
        {
          "label": "Product implementation notes · Start or renewal fails",
          "url": "https://anchorgpu.com/docs/troubleshooting#lifecycle",
          "evidence": "product-documentation"
        }
      ],
      "url": "https://anchorgpu.com/docs/troubleshooting#short-answer"
    },
    {
      "path": "/docs/serve-llm-with-vllm",
      "title": "Serve an LLM with vLLM",
      "question": "What is the safest first vLLM serving test?",
      "answer": "Pin the model and serving stack, verify GPU support, and launch a single-GPU pilot bound to loopback with an API key. Test the real prompt lengths and concurrency before selecting parallelism. Record memory, latency and errors; exposing a production endpoint requires separate network and security configuration.",
      "highlights": [
        "Confirm the model’s chat template.",
        "Distinguish replicas from tensor parallelism.",
        "The commands require real compatible hardware; the local site does not run them."
      ],
      "limitation": "AnchorGPU provides catalog and configuration reference material. Crypto payments are received on the addresses shown at checkout; provisioning and availability are confirmed by the operator for each order.",
      "updatedAt": "2026-09-04",
      "sources": [
        {
          "label": "vLLM GPU installation",
          "url": "https://docs.vllm.ai/en/stable/getting_started/installation/gpu/",
          "evidence": "official-documentation"
        },
        {
          "label": "vLLM parallelism and scaling",
          "url": "https://docs.vllm.ai/en/latest/serving/parallelism_scaling/",
          "evidence": "official-documentation"
        },
        {
          "label": "vLLM quickstart and authentication",
          "url": "https://docs.vllm.ai/en/latest/getting_started/quickstart/",
          "evidence": "official-documentation"
        },
        {
          "label": "vLLM online serving",
          "url": "https://docs.vllm.ai/en/latest/serving/online_serving/",
          "evidence": "official-documentation"
        },
        {
          "label": "Product implementation notes · Define the serving contract",
          "url": "https://anchorgpu.com/docs/serve-llm-with-vllm#contract",
          "evidence": "product-documentation"
        },
        {
          "label": "Product implementation notes · Start with one GPU when it fits",
          "url": "https://anchorgpu.com/docs/serve-llm-with-vllm#layout",
          "evidence": "product-documentation"
        },
        {
          "label": "Product implementation notes · Qualify the complete environment",
          "url": "https://anchorgpu.com/docs/serve-llm-with-vllm#environment",
          "evidence": "product-documentation"
        },
        {
          "label": "Product implementation notes · Launch a private endpoint",
          "url": "https://anchorgpu.com/docs/serve-llm-with-vllm#launch",
          "evidence": "product-documentation"
        },
        {
          "label": "Product implementation notes · Check the API before load testing",
          "url": "https://anchorgpu.com/docs/serve-llm-with-vllm#verify",
          "evidence": "product-documentation"
        },
        {
          "label": "Product implementation notes · Measure sustained capacity",
          "url": "https://anchorgpu.com/docs/serve-llm-with-vllm#capacity",
          "evidence": "product-documentation"
        },
        {
          "label": "Product implementation notes · Resolve the common failure modes",
          "url": "https://anchorgpu.com/docs/serve-llm-with-vllm#failure",
          "evidence": "product-documentation"
        }
      ],
      "url": "https://anchorgpu.com/docs/serve-llm-with-vllm#short-answer"
    },
    {
      "path": "/docs/pytorch-fine-tuning",
      "title": "Plan a reproducible PyTorch run",
      "question": "How do I qualify a PyTorch training configuration?",
      "answer": "Use the real model, data and optimizer, warm up the run, and measure a complete forward, backward and optimizer step. Compare peak tensor allocation with total device use, then test validation, checkpoint save and restore. Scale only after identifying whether memory, computation, communication or data loading is the constraint.",
      "highlights": [
        "A forward pass is not a complete training-memory test.",
        "DDP and FSDP solve different scaling problems.",
        "Record framework, driver and data revisions with the result."
      ],
      "limitation": "AnchorGPU provides catalog and configuration reference material. Crypto payments are received on the addresses shown at checkout; provisioning and availability are confirmed by the operator for each order.",
      "updatedAt": "2026-09-04",
      "sources": [
        {
          "label": "PyTorch distributed overview",
          "url": "https://docs.pytorch.org/tutorials/beginner/dist_overview.html",
          "evidence": "official-documentation"
        },
        {
          "label": "PyTorch peak allocated memory",
          "url": "https://docs.pytorch.org/docs/stable/generated/torch.cuda.max_memory_allocated.html",
          "evidence": "official-documentation"
        },
        {
          "label": "PyTorch torchrun",
          "url": "https://docs.pytorch.org/docs/stable/elastic/run",
          "evidence": "official-documentation"
        },
        {
          "label": "PyTorch activation checkpointing",
          "url": "https://docs.pytorch.org/docs/stable/checkpoint.html",
          "evidence": "official-documentation"
        },
        {
          "label": "PyTorch Distributed Checkpoint",
          "url": "https://docs.pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html",
          "evidence": "official-documentation"
        },
        {
          "label": "PyTorch on ROCm",
          "url": "https://rocm.docs.amd.com/projects/install-on-linux/en/docs-6.4.0/install/3rd-party/pytorch-install.html",
          "evidence": "official-documentation"
        },
        {
          "label": "Product implementation notes · Prove one complete step before scaling",
          "url": "https://anchorgpu.com/docs/pytorch-fine-tuning#pilot",
          "evidence": "product-documentation"
        },
        {
          "label": "Product implementation notes · Measure a representative optimization step",
          "url": "https://anchorgpu.com/docs/pytorch-fine-tuning#memory",
          "evidence": "product-documentation"
        },
        {
          "label": "Product implementation notes · Choose DDP or sharding for the right reason",
          "url": "https://anchorgpu.com/docs/pytorch-fine-tuning#parallelism",
          "evidence": "product-documentation"
        },
        {
          "label": "Product implementation notes · Launch a single-node pilot",
          "url": "https://anchorgpu.com/docs/pytorch-fine-tuning#launch",
          "evidence": "product-documentation"
        },
        {
          "label": "Product implementation notes · Select hardware from the pilot result",
          "url": "https://anchorgpu.com/docs/pytorch-fine-tuning#hardware",
          "evidence": "product-documentation"
        },
        {
          "label": "Product implementation notes · Test restore, not just save",
          "url": "https://anchorgpu.com/docs/pytorch-fine-tuning#checkpoint",
          "evidence": "product-documentation"
        },
        {
          "label": "Product implementation notes · Budget for the whole pipeline",
          "url": "https://anchorgpu.com/docs/pytorch-fine-tuning#term",
          "evidence": "product-documentation"
        }
      ],
      "url": "https://anchorgpu.com/docs/pytorch-fine-tuning#short-answer"
    },
    {
      "path": "/compare/nvidia-h100-sxm-vs-nvidia-a100-sxm",
      "title": "H100 SXM vs A100 SXM: is Hopper worth the premium?",
      "question": "H100 SXM vs A100 SXM: is Hopper worth the premium?",
      "answer": "Start with A100 as the lower-cost 80 GB baseline. Choose H100 when an application-level pilot shows a useful Hopper advantage.",
      "highlights": [
        "Switching between these models does not increase the 80 GB capacity.",
        "FP8-capable hardware does not prove that your model, kernels or quality target can use FP8.",
        "Multi-GPU scaling requires an explicit parallelism strategy and a verified host fabric."
      ],
      "limitation": "Manufacturer specifications describe the accelerator, not verified AnchorGPU host topology. Catalog cost differences are not performance measurements.",
      "updatedAt": "2026-09-04",
      "sources": [
        {
          "label": "NVIDIA H100 specifications",
          "url": "https://www.nvidia.com/en-us/data-center/h100/",
          "evidence": "official-documentation"
        },
        {
          "label": "NVIDIA A100 specifications",
          "url": "https://www.nvidia.com/en-us/data-center/a100/",
          "evidence": "official-documentation"
        },
        {
          "label": "Modeled price inputs, not measured performance",
          "url": "https://anchorgpu.com/reference#catalog",
          "evidence": "catalog-model"
        }
      ],
      "url": "https://anchorgpu.com/compare/nvidia-h100-sxm-vs-nvidia-a100-sxm#short-answer"
    },
    {
      "path": "/compare/nvidia-h100-sxm-vs-nvidia-h100-pcie",
      "title": "H100 SXM vs H100 PCIe: same capacity, different system design",
      "question": "H100 SXM vs H100 PCIe: same capacity, different system design",
      "answer": "PCIe is the lower-cost Hopper choice for independent work. SXM is a candidate for closely coupled workloads only when the delivered topology supports the intended strategy.",
      "highlights": [
        "Confirm which requested GPUs share the same CPU and NUMA domain.",
        "Ask which pairs have NVLink and whether the machine contains NVSwitch, a two-card bridge or PCIe-only communication.",
        "Verify the GPU, network adapter and local storage placement for your communication pattern."
      ],
      "limitation": "Manufacturer specifications describe the accelerator, not verified AnchorGPU host topology. Catalog cost differences are not performance measurements.",
      "updatedAt": "2026-09-04",
      "sources": [
        {
          "label": "NVIDIA H100 specifications",
          "url": "https://www.nvidia.com/en-us/data-center/h100/",
          "evidence": "official-documentation"
        },
        {
          "label": "NVIDIA H100 PCIe product brief",
          "url": "https://www.nvidia.com/content/dam/en-zz/Solutions/gtcs22/data-center/h100/PB-11133-001_v01.pdf",
          "evidence": "official-documentation"
        },
        {
          "label": "Modeled price inputs, not measured performance",
          "url": "https://anchorgpu.com/reference#catalog",
          "evidence": "catalog-model"
        }
      ],
      "url": "https://anchorgpu.com/compare/nvidia-h100-sxm-vs-nvidia-h100-pcie#short-answer"
    },
    {
      "path": "/compare/nvidia-h200-sxm-vs-amd-mi300x",
      "title": "H200 SXM vs MI300X: memory headroom or CUDA continuity?",
      "question": "H200 SXM vs MI300X: memory headroom or CUDA continuity?",
      "answer": "Prefer H200 for CUDA-dependent continuity. Evaluate MI300X when a validated ROCm path and larger single-accelerator memory reduce your application’s complexity.",
      "highlights": [
        "Inventory custom CUDA extensions, attention kernels, quantization kernels and collective operations.",
        "Validate model loading, a representative request or step, numerical output and checkpoint restore.",
        "Measure the KV cache or training state alongside model weights; capacity is not just parameter count."
      ],
      "limitation": "Manufacturer specifications describe the accelerator, not verified AnchorGPU host topology. Catalog cost differences are not performance measurements.",
      "updatedAt": "2026-09-04",
      "sources": [
        {
          "label": "NVIDIA H200 specifications",
          "url": "https://www.nvidia.com/en-us/data-center/h200/",
          "evidence": "official-documentation"
        },
        {
          "label": "AMD MI300X specifications",
          "url": "https://www.amd.com/en/products/accelerators/instinct/mi300/mi300x.html",
          "evidence": "official-documentation"
        },
        {
          "label": "PyTorch on ROCm",
          "url": "https://rocm.docs.amd.com/projects/install-on-linux/en/docs-6.4.0/install/3rd-party/pytorch-install.html",
          "evidence": "official-documentation"
        },
        {
          "label": "Modeled price inputs, not measured performance",
          "url": "https://anchorgpu.com/reference#catalog",
          "evidence": "catalog-model"
        }
      ],
      "url": "https://anchorgpu.com/compare/nvidia-h200-sxm-vs-amd-mi300x#short-answer"
    }
  ],
  "terms": [
    {
      "id": "vram",
      "term": "VRAM",
      "definition": "Memory local to an accelerator. Workload capacity includes more than model weights: activations, buffers, cached state and framework allocations can also consume it.",
      "related": "https://anchorgpu.com/blog/how-much-vram-do-you-need",
      "source": "https://docs.pytorch.org/docs/stable/notes/cuda.html#cuda-memory-management",
      "url": "https://anchorgpu.com/glossary#vram"
    },
    {
      "id": "kv-cache",
      "term": "KV cache",
      "definition": "Key and value tensors retained by autoregressive serving for live sequences. Model architecture, cache precision, token count and concurrency affect the required capacity.",
      "related": "https://anchorgpu.com/blog/gpu-for-llm-inference",
      "source": "https://docs.vllm.ai/en/latest/serving/parallelism_scaling/",
      "url": "https://anchorgpu.com/glossary#kv-cache"
    },
    {
      "id": "quantization",
      "term": "Quantization",
      "definition": "Representing some model values with reduced numerical precision. Real memory use includes format metadata and runtime workspaces; supported kernels and output quality must be checked.",
      "related": "https://anchorgpu.com/blog/how-much-vram-do-you-need",
      "source": "https://docs.vllm.ai/en/latest/features/quantization/",
      "url": "https://anchorgpu.com/glossary#quantization"
    },
    {
      "id": "tensor-parallelism",
      "term": "Tensor parallelism",
      "definition": "Distributing parts of model tensor operations across accelerators. It can spread a model across cards but introduces communication and requires an explicitly supported serving or training strategy.",
      "related": "https://anchorgpu.com/docs/serve-llm-with-vllm",
      "source": "https://docs.vllm.ai/en/latest/serving/parallelism_scaling/",
      "url": "https://anchorgpu.com/glossary#tensor-parallelism"
    },
    {
      "id": "data-parallelism",
      "term": "DistributedDataParallel (DDP)",
      "definition": "A PyTorch training approach that runs model replicas across processes and synchronizes gradients. Replication does not combine device memories into one contiguous pool.",
      "related": "https://anchorgpu.com/docs/pytorch-fine-tuning",
      "source": "https://docs.pytorch.org/tutorials/beginner/dist_overview.html",
      "url": "https://anchorgpu.com/glossary#data-parallelism"
    },
    {
      "id": "fsdp",
      "term": "Fully Sharded Data Parallel (FSDP)",
      "definition": "Distributed training that shards model parameters, gradients and optimizer state. Memory savings come with communication, coordination and checkpoint requirements.",
      "related": "https://anchorgpu.com/docs/pytorch-fine-tuning",
      "source": "https://docs.pytorch.org/docs/stable/fsdp.html",
      "url": "https://anchorgpu.com/glossary#fsdp"
    },
    {
      "id": "cuda",
      "term": "CUDA",
      "definition": "NVIDIA’s parallel-computing platform and programming model. A CUDA-dependent workload still requires compatible hardware, driver, runtime and libraries.",
      "related": "https://anchorgpu.com/docs/machine-images",
      "source": "https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html",
      "url": "https://anchorgpu.com/glossary#cuda"
    },
    {
      "id": "rocm",
      "term": "ROCm",
      "definition": "AMD’s software stack for GPU computing. Support must be checked for the exact GPU, operating system, framework build and workload dependencies.",
      "related": "https://anchorgpu.com/compare/nvidia-h200-sxm-vs-amd-mi300x",
      "source": "https://rocm.docs.amd.com/en/latest/",
      "url": "https://anchorgpu.com/glossary#rocm"
    },
    {
      "id": "checkpoint",
      "term": "Training checkpoint",
      "definition": "Saved state used to resume or reproduce training. Depending on the run, this includes the model, optimizer, schedule, random state and progress through the data. Verify restoration, not just writing.",
      "related": "https://anchorgpu.com/docs/pytorch-fine-tuning",
      "source": "https://docs.pytorch.org/tutorials/beginner/saving_loading_models.html",
      "url": "https://anchorgpu.com/glossary#checkpoint"
    },
    {
      "id": "vae",
      "term": "Variational autoencoder (VAE)",
      "definition": "In a diffusion pipeline, a component that maps between latent representations and viewable outputs. Decoding can have a separate memory peak from denoising; supported chunking or tiling requires quality checks.",
      "related": "https://anchorgpu.com/blog/gpu-for-ai-video",
      "source": "https://huggingface.co/docs/diffusers/main/optimization/memory",
      "url": "https://anchorgpu.com/glossary#vae"
    },
    {
      "id": "fixed-term",
      "term": "Fixed rental term",
      "definition": "In this catalog, a week is exactly 7 days and a month exactly 30 days. Stopping a modeled allocation does not pause the paid period. GPU prices and selected options form the complete quote.",
      "related": "https://anchorgpu.com/docs/billing-ledger",
      "source": "https://anchorgpu.com/pricing",
      "url": "https://anchorgpu.com/glossary#fixed-term"
    },
    {
      "id": "idempotency",
      "term": "Idempotency key",
      "definition": "An identifier that lets a supported operation recognize a repeated request, so the same action is not applied twice. Reuse it for a retry of the same operation, not a different purchase or configuration.",
      "related": "https://anchorgpu.com/docs/api-reference",
      "source": "https://anchorgpu.com/docs/api-reference",
      "url": "https://anchorgpu.com/glossary#idempotency"
    }
  ]
}