vLLM Integrates Transformers Backend for Native Inference Speed
LysandreJik · x · 2026-07-10
The vLLM project has integrated the Transformers modeling backend, allowing it to run as fast as native vLLM. This means developers only need to add a model once in Transformers to achieve 100% native inference performance across any environment and hardware, truly realizing "train once, infer anywhere."
Related event: vLLM Transformers Backend Achieves Native Performance(4 posts)→
More from Infra
- AI chip startup Tenstorrent raises $1.5B at $6.2B valuation, more than doubling — Beth_Kindig · 2026-10-27
- Anthropic discloses up to $120.5B SpaceX compute deal, ~$3.1B monthly at full capacity — XFreeze · 2026-10-27
- Satire: swapping Raft for an LLM-style decision model tanks write throughput 99.98% — irombie · 2026-10-27
- Cadence joins the domain-specific AI model camp for chip design — karlfreund · 2026-10-27
- Qdrant builds Universal I/O to run vector search mmap code on object storage — qdrant_engine · 2026-10-27
- Nebius secures $3.5B Citi financing facility for GPU purchases and AI data centers — Beth_Kindig · 2026-10-26