Muse Glimmer

Source: https://github.com/HDZucht/Mac-Mini-M6-LLM-benchmarks Raw measured rows: https://github.com/HDZucht/Mac-Mini-M6-LLM-benchmarks/blob/3990a8e0fa5f2762911689439ce12e1c28d4c989/data/speed_2026-10-03.jsonl Hardware/method/model labels: https://github.com/HDZucht/Mac-Mini-M6-LLM-benchmarks/blob/cb99870e05aa9f5783c5c6987ddd9154834123ab/README.md Loaded context metadata: https://github.com/HDZucht/Mac-Mini-M6-LLM-benchmarks/blob/main/data/loaded_context_2026-10-03.jsonl Harness: https://github.com/HDZucht/Mac-Mini-M6-LLM-benchmarks/blob/main/bench_speed.py Original author: HDZucht / Hans-Dieter Zucht, with repository-credited automated measurements by Argus. Campaign dated October 3–4, 2026; these exact records are in the October 3 raw file. No local reproduction was performed. Headline: 8.6 tokens/s is the median of three English-prose runs (Ruhr economy). It is client-observed streaming decode throughput, not a pooled rate, overall mean, peak, native kernel result or prompt-processing rate. Five workload medians and all 15 runs are preserved separately below. Hardware: Apple Mac mini M6 (Mac18,5), 12 CPU cores (2 Super, 4 Performance, 6 Efficiency), 12 GPU cores, 32 GB unified memory, macOS 27.0.1. One machine, one model loaded alone. Model storage: ORICO 2 TB external NVMe in an ASMedia ASM246x enclosure, source-reported 40 Gb/s PCIe 4.0 x4. Apple automatic fan control. Per-run temperature, clock, power, swap, exact GPU offload and peak resident memory were not logged. Exact measured API alias: meta/muse-glimmer. Display model: Meta Muse Glimmer. Muse Glimmer identity is corroborated by the exact current LM Studio alias; the original table omits parameter count and format, so both historical fields remain blank. Source Format-column label: blank/unreported. Exact measured quantization: unknown. Measured weight format: unknown; source label is not a confirmed weight container. Application: LM Studio. Backend: unreported for this alias. Exact app/backend versions are unknown. Do not turn QAT, MLX or Splash labels into an assumed Q4 quantization. The current exact LM Studio alias identifies Muse Glimmer as the nominal 30B model and links the verified GGUF collection. The observed tree has a Q4_K_M target file, but the benchmark did not save that filename or quantization. No measured quant is inferred. Current alias corroboration (not original run metadata): https://www.lmstudio.ai/models/meta/muse-glimmer Verified public related package/collection: https://huggingface.co/lmstudio-community/Muse-Glimmer-30B-GGUF/tree/main Measured Hugging Face repository, filename, revision and hash are unrecorded. Public related-package links above prove availability only. Loaded context for this exact alias is 32,768 tokens. Requested load context was 32,768. Loaded capacity is not actual prompt length or a filled-context test. Actual input token counts are absent. Native prefill throughput/duration are unknown; keep PP and new-input-token fields blank. Method: one stream, max_tokens 600, temperature 0.7, reasoning_effort="none", three runs per prompt. A short Hallo warm-up precedes the measured prompts. The script includes emitted content and reasoning chunks; requested reasoning-off does not establish that reasoning was disabled in every backend/model. The harness computes (tokens - 1) / (stream_end - first_nonempty_content_time). It uses API completion_tokens when available and nonzero; otherwise it falls back to the number of nonempty streamed chunks. Raw records contain tokens, chunks, TTFT and generated text, but not the original usage object, finish_reason or total duration. Treat tokens as recorded output counts, with this usage/chunk fallback caveat. Chunk counts need not equal tokenizer token counts. Rates are the saved rounded client rates, not recalculated from TTFT. TTFT is end-to-end client latency, not native prefill duration. Prompts repeat identically across three repetitions; no nonce or explicit cache clear appears in the harness. Prefix-cache state/cached-token counts are unknown. These are not verified cold-prefill runs. A record of 600 equals the requested cap numerically, but finish_reason is not available. KV dtype, batching, microbatching, thread counts and exact GPU allocation are unreported. All 15 runs follow, in source repetition order. Fields: decode tokens/s; recorded output count; nonempty chunks; client TTFT seconds. German prose, Rhine geology (wissen_de), median 8.7 tokens/s: Run 0: 8.8 tokens/s; 600 output count; 597 chunks; TTFT 0.92 s. Run 1: 8.7 tokens/s; 600 output count; 597 chunks; TTFT 0.61 s. Run 2: 8.6 tokens/s; 600 output count; 597 chunks; TTFT 0.62 s. German prose, bead-based immunoassay (beads_de), median 8.7 tokens/s: Run 0: 8.7 tokens/s; 600 output count; 591 chunks; TTFT 0.93 s. Run 1: 8.6 tokens/s; 600 output count; 591 chunks; TTFT 0.67 s. Run 2: 8.7 tokens/s; 600 output count; 591 chunks; TTFT 0.64 s. English prose, Ruhr economy (prosa_en), median 8.6 tokens/s: Run 0: 8.6 tokens/s; 600 output count; 597 chunks; TTFT 0.93 s. Run 1: 8.6 tokens/s; 600 output count; 591 chunks; TTFT 0.62 s. Run 2: 8.6 tokens/s; 600 output count; 597 chunks; TTFT 0.63 s. Python ISO-8601 duration parser (code_py), median 8.6 tokens/s: Run 0: 8.6 tokens/s; 600 output count; 597 chunks; TTFT 1.06 s. Run 1: 8.6 tokens/s; 600 output count; 591 chunks; TTFT 0.62 s. Run 2: 8.6 tokens/s; 600 output count; 591 chunks; TTFT 0.62 s. JSON antigen/disease extraction (json_extr), median 8.6 tokens/s: Run 0: 8.8 tokens/s; 600 output count; 597 chunks; TTFT 1.05 s. Run 1: 8.6 tokens/s; 563 output count; 553 chunks; TTFT 0.62 s. Run 2: 8.6 tokens/s; 600 output count; 597 chunks; TTFT 0.63 s. Quality scope: The README reports the JSON task passed 0 of 3 automatic checks for this model. Raw JSON output counts are 600, 563 and 600; the 563-token run ends in a JSON object following reasoning. Therefore do not state that every run hit 600 or that no run emitted a complete JSON object. The author-scored failure is retained without regrading. Prose/code quality was not generally graded. Throughput is not a quality score.