vLLM(@vllm_project):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. 📝

2026.05.11 15:00

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. 📝

Eldar Kurtić@_EldarKurtic

2026.05.11 12:08

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: