What we measured Lab-measured
Pooled arithmetic mean across two fresh canonical full-256 three-prompt runs; individual prompts and full natural completions can be slower. proof file
- Model
- Muse-Glimmer 30B UD-Q8_K_XL target plus BF16 DFlash assistant
- Publisher
- Meta Models
- Checkpoint
- unsloth/Muse-Glimmer-30B-GGUF
- Compression
- UD-Q8_K_XL / BF16 draft
- Software
- llama.cpp SYCL
- Cards
- 4× Intel Arc Pro B70 32 GiB
- Model weight bytes
- 37.4 GB
- Operating systems
- Linux
- Delivery
- native
- Good for
- general coding structured output
- Published
- 2026-08-22
- Clean-host replay
- not yet
Still missing before this becomes an install guide
- tested platform installer
- original download-time complete draft input identity
- independent clean-host replay
- beginner recovery flow
- decode, prefill, and TTFT context sweep
Measured performance profiles Not published
No qualified structured context or depth profile is published for this package. Diagnostic evidence may still be linked under “What to know” or in the full guide; nothing is estimated in its place — the clearly labeled projection block below is the current best guess.
Many people at once Not published
No qualified multi-user aggregate profile is published for this exact package. Diagnostic or unsupported boundaries may still appear under “What to know” or in the full guide. Nothing is interpolated or promoted from a different model, quantization, runtime, or card count; the projection below remains clearly labeled as projected.
How much faster could this get? Projected — not measured
The ML Bottleneck physics engine projects what stock software, a tuned run, and the physical ceiling look like for this exact model, compression, card count, and software. The grade is optimization headroom against the tuned-run target, not model quality.
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