Muse-Glimmer-30B-ctx48k-kvq8 on RTX A4000 (16GB)
MeasuredOn 2026-10-22, Muse-Glimmer-30B-ctx48k-kvq8 UD-IQ3_XXS ran at 21.7 tok/s (chat-short) on an RTX A4000 (16GB) with llama.cpp. Run r_x8mqtlf166j.
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-ctx48k-kvq8UD-IQ3_XXS | 21.71tok/s | 273.7tok/s | 563ms | 46.1ms | 47.3ms |
| chat-long | llama.cpp@0.7.0-dev (build 11803, commit a37cc4610) | Muse-Glimmer-30B-ctx48k-kvq8UD-IQ3_XXS | 20.09tok/s | 691.7tok/s | 4,538ms | 49.7ms | 51.7ms |
| concurrent-decode | llama.cpp@0.7.0-dev (build 11803, commit a37cc4610) | Muse-Glimmer-30B-ctx48k-kvq8UD-IQ3_XXS | 20.75tok/s | — | — | 48.2ms | 49.5ms |
| agent-trace | llama.cpp@0.7.0-dev (build 11803, commit a37cc4610) | Muse-Glimmer-30B-ctx48k-kvq8UD-IQ3_XXS | 19.35tok/s | 1,668.3tok/s | 1,138ms | 49.3ms | 53.3ms |
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_x8mqtlf166jRelated benchmarks
- More Muse-Glimmer-30B-ctx48k-kvq8 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_x8mqtlf166j
- Fingerprint hash
- Public key
- dzVGpxqAK5OXNww5ztLdZ4+Qe/puHZxWdv/96DjoTYg=
- Received
- 2026-10-22 13:56:19