VRAM
Memory local to an accelerator. Workload capacity includes more than model weights: activations, buffers, cached state and framework allocations can also consume it.
KV cache
Key and value tensors retained by autoregressive serving for live sequences. Model architecture, cache precision, token count and concurrency affect the required capacity.
Quantization
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.
Tensor parallelism
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.
DistributedDataParallel (DDP)
A PyTorch training approach that runs model replicas across processes and synchronizes gradients. Replication does not combine device memories into one contiguous pool.
Fully Sharded Data Parallel (FSDP)
Distributed training that shards model parameters, gradients and optimizer state. Memory savings come with communication, coordination and checkpoint requirements.
CUDA
NVIDIA’s parallel-computing platform and programming model. A CUDA-dependent workload still requires compatible hardware, driver, runtime and libraries.
ROCm
AMD’s software stack for GPU computing. Support must be checked for the exact GPU, operating system, framework build and workload dependencies.
Training checkpoint
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.
Variational autoencoder (VAE)
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.
Fixed rental term
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.
Idempotency key
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.
Definitions explain the concept; they do not guarantee that a specific workload, library version or topology is supported by an AnchorGPU allocation.