Ornith 1.5 35B-A3B Q4_K_M on one Intel Arc Pro B70 — strict benchmark pending

Public headline Pending

The 131.460231 tok/s two-server observation and matched patch A/B evidence remain valid scoped measurements, but the runtime produced 0/12 identical complete natural-response hashes across fresh stock servers. Keep the mechanism and context evidence; require a stable, registered cross-server oracle before assigning a strict package headline. Diagnostic measurements remain in the guide and evidence, but none is presented as the package headline.

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Model: Ornith 1.5 35B-A3B
Publisher: Ornith AI
Checkpoint: ornith-ai/Ornith-1.5-35B-A3B-GGUF
Compression: Q4_K_M
Software: llama.cpp SYCL
Cards: 1× Intel Arc Pro B70 32 GiB
Model weight bytes: 21.7 GB
Operating systems: Linux
Delivery: native
Good for: general coding chat
Published: 2026-08-22
Clean-host replay: not yet

Still missing before this becomes an install guide

tested clean-host platform installation
beginner recovery flow

What to know

MoE rate reflects ~3B active parameters; not comparable to dense 35B expectations.
Canary battery is objective self-consistency only; no cross-model oracle.
Stock two-card layer split measured 102.01/102.20 tok/s versus 104.84/104.81 on one card (-2.6%): use one card for single-stream decode.
Fresh stock servers matched 0/12 complete realistic-suite hashes across processes on this runtime. A realistic same-process repeat also produced four hashes across eight requests; use same-frozen-binary door-off/on exactness and activation counts for patch validation, while the short exact-answer 8x canary remains a narrower check.
The aggregate concurrency profile is a raw-engine continuous-batching measurement, not an HTTP serving users/sec result. The optional multi-row research patch improves the four-sequence aggregate rate by a confirmed 2.21% but is not enabled by the single-user package launch.
Stage the GGUF on local or sufficiently fast direct-attached storage; optimized first-token setup rereads roughly 20.0 GB of expert tensors, making a 100 Mb/s NFS mmap unsuitable for cold benchmarking.

Measured performance profiles Lab-measured

Current twelve-feature raw decode over existing context depth Lab-measured

llama-bench raw engine rates using the exact twelve-feature source patch and accepted copy-offload setting, graph off, pp2048/tg128, flash attention on, F16 KV, and 5 repetitions at every displayed depth. No point is scaled and no missing depth is interpolated. evidence

Current twelve-feature raw pp2048 over existing context depth Lab-measured

Many people at once Lab-measured

Accepted-stack aggregate decode over concurrent sequences Lab-measured

Direct llama-batched-bench raw-engine continuous batching with independent pp1024 prompts, interleaved tg256 decode, c65536, flash attention on, F16 KV, one B70, and the exact accepted twelve-feature stack. This excludes HTTP, JSON, queueing, and server-scheduler overhead. Per-user values are arithmetic aggregate/users. No point is scaled, interpolated, or extrapolated. evidence

Optimization grade Pending

No optimization grade is calculated because this package has no promoted measured headline. Diagnostic or scoped measurements never seed a headline projection.