Unverified50% confidenceFactExact time
In vLLM, it is GPUModelRunner that actually performs physical GPU memory allocation, while Worker plays more of a resource orchestration role.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
9/10/2026
First Seen
Valid until: 12/9/2026
Sources
Related Entities
Related Claims
UnverifiedvLLM adopts a scheduling-execution separation architecture, where the scheduling side is responsible for request management, sequence scheduling, and logical resource allocation, while the execution side is responsible for actual model inference and physical VRAM management.71% similarVerifiedvLLM achieves efficient KV Cache management through PagedAttention technology, improving GPU memory utilization several times over.70% similarUnverifiedThe actual physical VRAM allocation occurs on vLLM's Worker side, while the logical block pool and block table are established on the scheduler side.68% similarUnverifiedvLLM employs continuous batching technology to allow requests from different users to be processed in parallel within the same GPU batch, improving throughput while enabling finer memory boundary management between requests.68% similarUnverifiedModern large models serving external users typically rely on inference frameworks such as vLLM, SGLang, and TensorRT-LLM, running on GPU cloud providers like AWS, CoreWeave, and Lambda.66% similar
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