vLLM
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参加 March 2024
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Great read from the @RedHat_AI team — a comprehensive investigation into TurboQuant in vLLM, with FP8 and BF16 as reference baselines: 4 models (30B to 200B+, decoder-only and MoE) and 5 benchmarks covering long-context retrieval and reasoning, all on the stable vLLM 0.20.2 release.
If you're considering TurboQuant for your workload, this is the data to start from.
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TurboQuant has drawn a lot of attention recently, but the accompanying evals didn't tell the full story.
So we ran what I believe is the first comprehensive study of TurboQuant: where it helps, where it falls short, and how it impacts accuracy, latency, and throughput.
Findings:
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