What we measured Lab-measured
Conventional 99-interval median, cold 12-prompt suite, up to 512-token responses, cache-zero, at 262144 configured capacity with q8_0 K/V, vision mmproj, and MTP draft loaded; active prompts were 48–78 tokens and the second fresh-server run measured 26.640510. Speculation-assisted and labeled as such; target-only tg128 at depth 0 is 22.64 tok/s (raw engine). proof file
target-only = no draft model assisting · cache-zero = a fresh start, nothing pre-computed · 99-interval median = the middle rate across 99 measured stretches of a long answer · tg128 = a raw 128-token generation benchmark, not the headline suite · MTP = multi-token prediction, a small draft the main model verifies
- Model
- Qwen3.8 27B flagship package
- Publisher
- Alibaba / Qwen (GGUF by unsloth)
- Checkpoint
- unsloth/Qwen3.8-27B-GGUF
- Compression
- UD-Q5_K_S (shipped) / UD-Q4_K_XL (alternative)
- Software
- llama.cpp SYCL
- Cards
- 1× Intel Arc Pro B70 32 GiB
- Model weight bytes
- 18.7 GB
- Operating systems
- Linux
- Delivery
- native
- Good for
- long context vision general coding
- Published
- 2026-08-22
- Clean-host replay
- not yet
Still missing before this becomes an install guide
- tested clean-host platform installation
- broader multimodal quality beyond the objective smoke
- deep-context validation beyond canaries on the multi-chunk prefill path
What to know
- 256K KV requires q8_0 K/V on one card; f16 KV limits context to roughly 96K at these quants.
- MTP-assisted rate varies with content (p10 24.08 vs median 26.67); target-only numbers published alongside.
- Vision verified by objective two-question smoke (dominant color, corner color) on a deterministic test image; broader multimodal quality untested.
- Above ~1024-token prompts this package relies on multi-chunk prefill paths that the lab's separate vLLM lane found corruption-prone; the llama.cpp path here passed its canaries but has not had equivalent deep validation at depth.
Measured performance profiles Lab-measured
Raw decode over existing context depth Lab-measured
Raw pp2048 over existing context depth Lab-measured
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.
How much faster could this get? No projection
This package's measured workload does not map cleanly onto a single model + compression + card-count shape, so no like-for-like projection is shown. The measured numbers above stand on their own.