On an arm64 CPU with no GPU, llama.cpp 0.5.0 served Qwen3-1.7B as a Q8_0 GGUF at 72.7 tokens/s per request and 236.9 tokens/s with 8 concurrent requests. vLLM 0.30 in BF16 reached 10.1 and 68.3 tokens/s and used 11.4 GiB of RAM against 7.0 GiB.
Without a GPU, on an 18-core arm64 CPU with llama.cpp v0.4.1, Maple-Preview generated 172.84 tokens/s on 6 threads: twice MiniCPM5-2B and 3.6 times Spark-X2.5-4B. The trade-off is 5.79 GiB of RAM and reasoning it cannot switch off. MiniCPM5 fits in 3.27 GiB and Spark writes the best Spanish. All three made facts up.
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