Skip to content
llm-speed

Muse-Spark-Open-816B-A42B-UD-00001-of-00005 on 3x A100 (80GB) + AMD EPYC 7742 64-Core Processor (128c) + 1008GB

MeasuredOn 2026-10-16, Muse-Spark-Open-816B-A42B-UD-00… IQ1_S ran at 25.2 tok/s (chat-short) on an A100 (80GB) with llama.cpp. Run r_fsd9qjmmna0.

Workload results

WorkloadBackendModeldecode tok/sprefill tok/sTTFTp50p95
chat-shortllama.cpp@0.7.0-dev (build 11675, commit 2851b3bcb)Muse-Spark-Open-816B-A42B-UD-00001-of-00005IQ1_S25.20tok/s88.57tok/s2,732ms40.5ms40.8ms
chat-longllama.cpp@0.7.0-dev (build 11675, commit 2851b3bcb)Muse-Spark-Open-816B-A42B-UD-00001-of-00005IQ1_S25.50tok/s288.7tok/s11,179ms39.3ms39.4ms
concurrent-decodellama.cpp@0.7.0-dev (build 11675, commit 2851b3bcb)Muse-Spark-Open-816B-A42B-UD-00001-of-00005IQ1_S26.30tok/s——38.6ms38.7ms
agent-tracellama.cpp@0.7.0-dev (build 11675, commit 2851b3bcb)Muse-Spark-Open-816B-A42B-UD-00001-of-00005IQ1_S22.79tok/s570.8tok/s3,896ms38.6ms41.5ms

Reproduce on your machine

Start with this model and workload selection. This command does not capture the original artifact, backend version or server settings:

Match quantization, context capacity, caching and runtime settings for a useful comparison. Duration varies by workload and hardware; the published client may differ from this run's version. How it's measured.

Share this run

Copy the short link, a Reddit-ready summary, or a README badge. Every number is the measured result on this page, and each badge links back to it.

llm-speed: 25.2 tok/s on A100 (80GB) (Muse-Spark-Open-816B-A42B-UD-00…)
https://llm-speed.com/r/r_fsd9qjmmna0

Related benchmarks

Provenance

Run ID
r_fsd9qjmmna0
Fingerprint hash
Public key
XKHj+0TuD4UXlEyLswvRqFUyyh9fxwIoq5ZyQ/bAnT8=
Received
2026-10-16 01:19:02