Skip to content
llm-speed

Muse-Glimmer-30B-ctx8k-kvq8 on RTX A4000 (16GB)

MeasuredOn 2026-10-22, Muse-Glimmer-30B-ctx8k-kvq8 UD-IQ3_XXS ran at 21.7 tok/s (chat-short) on an RTX A4000 (16GB) with llama.cpp. Run r_b8y4oh7e0-z.

RTX A4000 (16GB)RTX A4000 (16GB)
suite suite-v1
cli 0.0.9
signedEE8idBXliy…
Embed badge@llmspeedsubmitted Oct 22, 2026

Workload results

WorkloadBackendModeldecode tok/sprefill tok/sTTFTp50p95
chat-shortllama.cpp@0.7.0-dev (build 11803, commit a37cc4610)Muse-Glimmer-30B-ctx8k-kvq8UD-IQ3_XXS21.67tok/s271.4tok/s567ms46.0ms48.2ms
chat-longllama.cpp@0.7.0-dev (build 11803, commit a37cc4610)Muse-Glimmer-30B-ctx8k-kvq8UD-IQ3_XXS20.13tok/s695.4tok/s4,514ms49.7ms51.2ms
concurrent-decodellama.cpp@0.7.0-dev (build 11803, commit a37cc4610)Muse-Glimmer-30B-ctx8k-kvq8UD-IQ3_XXS20.66tok/s——48.3ms50.1ms
agent-tracellama.cpp@0.7.0-dev (build 11803, commit a37cc4610)Muse-Glimmer-30B-ctx8k-kvq8UD-IQ3_XXS20.21tok/s1,658.2tok/s1,003ms49.1ms52.1ms

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: 21.7 tok/s on RTX A4000 (16GB) (Muse-Glimmer-30B-ctx8k-kvq8)
https://llm-speed.com/r/r_b8y4oh7e0-z

Related benchmarks

Provenance

Run ID
r_b8y4oh7e0-z
Fingerprint hash
Public key
EE8idBXliyGA6SKXCtYBqb5PSV4HPGjynZ4pTqY3+IE=
Received
2026-10-22 13:43:55