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Embedding jobs consume accelerator, CPU, memory, and storage in long batches, delaying chat prefill, decode, retrieval, and first-token response.

Runtimes reserve reusable GPU blocks after requests to avoid slow future allocations, so process memory can stay high without an active tensor leak.

Batching raises total accelerator utilization by combining requests, but waiting and mixed workloads can increase first-token and per-user latency.

Separating model artifacts from mutable runtime state makes upgrades, rollback, permissions, backups, cleanup, and failure recovery more predictable.

A model can fail despite enough total free VRAM when the allocator cannot assemble the block layout required for weights, KV cache, and workspaces.

Model caching shortens different parts of the request path, so warm replies can be fast even when a cold load or new prompt remains slow.

Multiple AI services can reserve overlapping memory budgets, forcing smaller batches, preemption, model eviction, CPU offload, or out-of-memory failures.

Cold start depends on how model weights are stored and read, not only SSD speed: layout controls metadata work, read parallelism, copies, and cache reuse.