Oct 13, 2026
Summary
Public third-party measurements by Hack-Log, published 2026-10-06. Source: https://note.com/hacklog_stealth/n/nc6dfde6e4ee0?hl=en
Mac mini Apple M4, 16 GB unified memory, macOS 26.4. System unified capacity is not dedicated VRAM or a measured working set. GPU core count 10 comes from the same author's 2026-10-09 Mac mini M4/16 GB companion: https://note.com/hacklog_stealth/n/n97927ee87cac?hl=en . CPU cores and threads are unpublished.
One participating physical GPU is supported by the campaign's Metal startup excerpt, identifying MTL0 (Apple M4), and the exact new runtime's one-physical-device implementation. At llama.cpp b11429 revision d81235049384534c167caea52b85a694f6103d14, the Metal backend selects the system-default physical GPU; additional Metal devices would be virtual views. llama-bench's default GPU-layer count is -1, which this runtime maps to all model layers plus output. The article presents both models as the same local Metal old/new comparison. A per-model 0.8B startup trace and full 0.8B command are not published. Quantity evidence is therefore campaign hardware/backend plus pinned implementation, not a count assumed from the chip name alone. Sources: https://github.com/ggml-org/llama.cpp/blob/d81235049384534c167caea52b85a694f6103d14/ggml/src/ggml-metal/ggml-metal-device.m ; https://github.com/ggml-org/llama.cpp/blob/d81235049384534c167caea52b85a694f6103d14/tools/llama-bench/llama-bench.cpp ; https://github.com/ggml-org/llama.cpp/blob/d81235049384534c167caea52b85a694f6103d14/src/llama-model.cpp .
Measured model label: Qwen3.5-0.8B, Q8_0 GGUF, 764 MiB according to the benchmark. Tested publisher, repository, revision and file hash are unknown. The benchmark does not show the exact 0.8B filename. The author's next-day inventory lists a file named Qwen3.5-0.8B-Q8_0.gguf but explicitly says its origin was not recorded: https://note.com/hacklog_stealth/n/n955380d3c86a . This is a provenance caveat, not evidence that a specific publisher produced the tested file. Reference identity only: https://huggingface.co/Qwen/Qwen3.5-0.8B/tree/2fc06364715b967f1860aea9cf38778875588b17 . That official post-trained-model repository is a base-model reference for the reported Qwen3.5-0.8B identity; it is not the tested Q8_0 artifact, and its revision must not be represented as a tested GGUF revision.
New runtime: llama.cpp 0.6.0, official macOS arm64 build 11429, Metal, flash attention enabled. Native source values: prompt processing 2122 tokens/s; single-stream generation 90.1 tokens/s. These are the article's llama-bench rates, not end-to-end throughput, prompt-length/TTFT proxies or derived values. Same campaign baseline: Homebrew llama.cpp 0.4.1 build 10964, native prompt processing 2049 tokens/s and generation 87.3 tokens/s.
The source says measurements were made old/new/old/new and the two invocations per runtime averaged. Its printed single-stream command is specifically for the 4B model and specifies prompt 512, generation 64, depths 0/4096 and 3 internal repetitions. The source does not print an equivalent 0.8B command or restate those lengths. Therefore 0.8B actual prompt/output counts, internal repetition count, context capacity and depth remain unknown. Do not copy the 4B configuration into those fields. Raw repetitions, spread, detailed timestamps, sampling, KV precision, CPU threads and thermal state are unpublished.
Separate 0.8B batched generation totals, old/new tokens/s: 1 stream = 87.2/87.7; 4 streams = 183.5/224.2; 8 streams = 195.7/322.4. These are aggregate generation rates over all simultaneous streams, not per-stream TG. The article describes this batched experiment as reading a 512-token prompt and producing 32 tokens per stream, and prints a 4B command with context 8192 and unified KV. The exact 0.8B batched command and actual counts are absent, so those settings are contextual method descriptions only, not independently bound 0.8B run fields. The author omitted the 2-stream comparison because two results disagreed, 103.5 versus 146.3 tokens/s; no old/new mapping is assigned to those diagnostic values.
No measured TTFT is published. Leave TTFT blank; do not calculate it from prompt throughput. This report contains the 0.8B model only; the 4B campaign has a separate report. No new benchmarks were run for this report.