Qwen2.5 3B-Instruct / Metal native PP512 TG128

SOURCE: https://picklog.cc/blog/local-llm-mac-mini-16gb HARDWARE/RUNTIME: Mac mini Mac16,10, base Apple M4, 10-core GPU, 16 GB unified memory; macOS 26.4.1. Homebrew llama.cpp 0.4.0 build 10809; ggml 0.23.0. GPU quantity: 1 physical GPU. The first-party post explicitly dates measurements 2026-09-07. It reports three llama-bench repetitions on default Metal, all layers on GPU: PP512 556.85 ± 0.54 and TG128 47.37 ± 0.13 t/s. The site states its research/writing is done by an AI without a person at the keyboard; that authorship disclosure applies to these claims. The article claims raw JSONL/llama-bench JSON exist in its private research notes, but they are not linked or independently recovered here. Existing agent processes remained active (10.42 GiB baseline system RAM use), so this is not an idle-machine result. Separate CPU-only comparison: two repetitions, 4 threads, -ngl 0, PP512 52.19 ± 6.04 and TG128 34.17 t/s, not this GPU report. A separate real-generation answer reached 617 tokens at 46.60 t/s; 136 ms is model-load time, not TTFT. Its 3.28 GB peak process RSS, macmon power medians and system RAM growth describe a separate generation run, not native PP512/TG128 resident memory. The 1,929,903,264-byte published weight size matches the current pinned HF file Content-Length, but no historical measured hash is published. NATIVE METRIC: Native llama-bench tests are separate synthetic PP and TG passes, not one user request. PP counts all fixed synthetic prompt tokens; TG counts the fixed one-token decode loop, not a max-output allowance or natural language answer. The native timer is high_resolution_clock around test_prompt/test_gen, with final PP synchronization and synchronization in each TG iteration. Reported rates are means of per-repetition tokens/elapsed_time; native ± values are sample standard deviations, not total experimental uncertainty. Model load, warm-up and any prior-depth setup are outside the timed interval. No TTFT is measured or inferred. No benchmark was rerun. PARTICIPATING GPU QUANTITY: One physical Apple GPU, supported by this historical runtime Metal implementation selecting MTLCreateSystemDefaultDevice(), combined with the source-stated local Metal run. This is implementation-based physical-device proof, not a retained per-run enumeration. GPU core count is distinct from device quantity. RUNTIME PROOF: https://github.com/ggml-org/llama.cpp/blob/5266f24da75dc449bd56cbed7addb9c8e4a6a73e/tools/llama-bench/llama-bench.cpp ; https://github.com/ggml-org/llama.cpp/blob/5266f24da75dc449bd56cbed7addb9c8e4a6a73e/ggml/src/ggml-metal/ggml-metal-device.m ARTIFACT: https://huggingface.co/bartowski/Qwen2.5-3B-Instruct-GGUF/blob/f302c64a2269a69fb27b2f9473b362f5bb8e78d8/Qwen2.5-3B-Instruct-Q4_K_M.gguf . A pinned resolve HEAD returned 200 without downloading weight bytes. Current publisher/file identity and size are corroborated; the source does not provide the tested local file SHA256 or download revision. The source snapshot/hash is not the model hash. UNKNOWNS: CPU core count and benchmark thread count; full benchmark command/environment where not quoted; actual allocator-rounded context capacity; per-run duration samples; exact historical weight bytes; hardware/model resident-memory footprint for these native test windows. Unreported values remain unknown. RETRIEVAL LIMITATION: The benchmark claims above were captured from the original page through a public web reader before a later direct archival request returned HTTP 403. No local full-HTML archive was obtained. Exact historical runtime source and current pinned HF metadata were independently read from their public repositories. Post-restriction source excerpts are excluded from this report. LlamaPerf structured rates are rounded to 0.1 tokens/s to match form precision; exact native means and SDs are retained above.