vLLM adds hardware-agnostic layers: within 3.4% of native throughput on H100 while keeping portability
PyTorch · x · 2026-09-23
A new PyTorch Foundation blog by contributors from IBM, Meta, and Hugging Face details vLLM's architectural shift:
- Context: Frontier performance work is moving vLLM away from fullgraph torch.compile, risking users on out-of-tree accelerators, older GPUs, and exotic models.
- Solution: A new set of "HW agnostic" layers balances frontier speed with portability.
- Results: On NVIDIA H100, total token throughput is within 3.4% of the native implementation (geometric mean across three recent models).
- Motivation: Frontier open-weight architectures are diverging fast — DeepSeek V4 and Kimi K3 achieve million-token context via entirely different approaches, shipping bespoke layers and kernels.
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