AI Decision System
Replayable, offline-verifiable records for AI-assisted go/no-go decisions: experiment promotions, typed model answers and human-approved actions.
RTX 2080 Ti 듀얼+NVLink용 vLLM 런타임 Qwen 27B 로컬 추론 제공 200tok/s 싱글 요청 디코드 FP8 가중치 지원
The definitive vLLM runtime for dual RTX 2080 Ti 22GB + NVLink, delivering Qwen 27B local inference with maximum 200+ tok/s single-request decode with support of FP8 weight ( Join Discord :https://dis
개발 도구Python · RustGitHub @weicj직접 설치운영자 미확인
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The definitive vLLM runtime for dual RTX 2080 Ti 22GB + NVLink, delivering Qwen 27B local inference with maximum 200+ tok/s single-request decode with support of FP8 weight ( Join Discord :https://discord.gg/VFqVVySdMS )
저장소 README에서 가져옴
README 전체를 불러오는 중입니다. 아래는 저장된 발췌입니다.
The definitive vLLM inference runtime for dual RTX 2080 Ti and other SM75 GPUs,
including Tesla T10/T40/T4, TITAN RTX, and Quadro RTX 6000/8000.
This hardware-focused fork preserves the SM75-specific source changes, launcher
profiles, and validation evidence needed to reproduce these Turing inference
stacks. It is
based on upstream vLLM; retain both the upstream license and attribution to
github.com/weicj when redistributing a derivative.
For usage feedback, feature requests, and community discussion, join the
Discord community.README 전문 보기 ↗지금 README 전체를 불러올 수 없습니다. 원문 링크에서 읽을 수 있습니다. 아래는 저장된 발췌입니다.
The definitive vLLM inference runtime for dual RTX 2080 Ti and other SM75 GPUs,
including Tesla T10/T40/T4, TITAN RTX, and Quadro RTX 6000/8000.
This hardware-focused fork preserves the SM75-specific source changes, launcher
profiles, and validation evidence needed to reproduce these Turing inference
stacks. It is
based on upstream vLLM; retain both the upstream license and attribution to
github.com/weicj when redistributing a derivative.
For usage feedback, feature requests, and community discussion, join the
Discord community.README 전문 보기 ↗