Muse-Glimmer-30B-ctx64k on RTX A4000 (16GB)
MeasuredOn 2026-10-22, Muse-Glimmer-30B-ctx64k UD-IQ3_XXS ran at 21.5 tok/s (chat-short) on an RTX A4000 (16GB) with llama.cpp. Run r_j9yaui8ovvy.
Workload results
| Workload | Backend | Model | decode tok/s | prefill tok/s | TTFT | p50 | p95 |
|---|---|---|---|---|---|---|---|
| chat-short | llama.cpp@0.7.0-dev (build 11803, commit a37cc4610) | Muse-Glimmer-30B-ctx64kUD-IQ3_XXS | 21.50tok/s | 274.6tok/s | 561ms | 46.6ms | 47.5ms |
| chat-long | llama.cpp@0.7.0-dev (build 11803, commit a37cc4610) | Muse-Glimmer-30B-ctx64kUD-IQ3_XXS | 20.77tok/s | 681.3tok/s | 4,608ms | 48.0ms | 49.9ms |
| concurrent-decode | llama.cpp@0.7.0-dev (build 11803, commit a37cc4610) | Muse-Glimmer-30B-ctx64kUD-IQ3_XXS | 20.64tok/s | — | — | 48.3ms | 51.0ms |
| agent-trace | llama.cpp@0.7.0-dev (build 11803, commit a37cc4610) | Muse-Glimmer-30B-ctx64kUD-IQ3_XXS | 19.51tok/s | 1,655.9tok/s | 1,009ms | 48.6ms | 52.4ms |
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.
https://llm-speed.com/r/r_j9yaui8ovvyRelated benchmarks
- More Muse-Glimmer-30B-ctx64k benchmarks — every backend and rig that has run this model.
- More RTX A4000 (16GB) LLM benchmarks — every model measured on this hardware.
Provenance
- Run ID
- r_j9yaui8ovvy
- Fingerprint hash
- Public key
- TJSJdeih6G64IN505YFJOM7PAb5pEf2VZd4htUdqTHc=
- Received
- 2026-10-22 13:31:35