Qwen3 0.6B

User reports 297.4 t/s generation and 4993 t/s prompt processing. Source evidence (verbatim): Source: https://arxiv.org/html/2607.00501v1 # Original M4 Pro 24GB Qwen3 0.6B four-bit llama.cpp baseline measurements Original Base Compute research paper, July 1, 2026, section 4 and Tables 1–2. Selected run is the authors' llama.cpp baseline, not BaseRT. Hardware: Apple M4 Pro 24GB unified memory, 16 GPU cores. Chassis, CPU core count and macOS version unreported; do not infer Mac mini. Model Qwen3-0.6B, GGUF weights, generic Q4 / 4-bit label only. Exact quantization recipe, tested artifact repository and revision unknown. Verified upstream model reference only: https://huggingface.co/Qwen/Qwen3-0.6B ; this safetensors repository is not the tested GGUF. Runtime llama.cpp Metal, source-labelled b9630 (June 2026), default settings. Full commit and command unreported. Generation 297.4 tokens/s over 128 autoregressive generated tokens (TG128). Prefill 4993 tokens/s at 512 prompt tokens (PP512). These are separate synthetic tests, not a combined 512-input/128-output request. Cache depth, KV precision, repetition count, warmup, concurrency and speculation unreported. Resident usage and weight-file size unreported. Standard deviations are omitted by the authors, who say they are generally below 3% for decode and 10% for prefill; these are not per-row error bars. Other runtime, prompt-length and quantization rows are distinct configurations, not averaged here.