CPU Inference for LLMs: i9-14900K vs Ryzen 9 7950X vs Apple M4

Most LLM benchmarks focus on GPUs, but CPU inference is surprisingly viable for smaller models. This head-to-head covers the i9-14900K, Ryzen 9 7950X, and Apple M4 for local AI workloads.

Why CPU Inference Matters

Not everyone has a discrete GPU, and some workflows benefit from CPU inference: long-running batch jobs that don't need real-time speed, multi-modal pipelines where the CPU handles preprocessing, and Apple Silicon Macs where the CPU shares unified memory with the GPU.

llama.cpp's BLAS-optimized CPU kernels with AVX-512 can push 8–15 tokens/sec on a modern CPU at Q4 for a 7B model — usable for many tasks. Complete per-chip benchmarks are tracked at LLMHardware.io's CPU inference page.

Benchmark Results: 7B Q4_K_M, llama.cpp

CPU	Tokens/sec	RAM Bandwidth	Power
Apple M4 (10-core)	38 t/s	120 GB/s	20W
Ryzen 9 7950X	14 t/s	83 GB/s	170W
i9-14900K	11 t/s	89 GB/s	200W
Apple M4 Max (14-core GPU)	92 t/s	546 GB/s	35W

Apple Silicon Advantage

The M4's unified memory architecture fundamentally changes the comparison. When using the Metal GPU backend in llama.cpp (-ngl 99), the M4 MacBook Pro outperforms a Ryzen 9 7950X by 6x while consuming 1/8th the power.

For pure CPU-only (no GPU offload), the M4's high memory bandwidth still gives it a 2.7x lead over the Ryzen 7950X despite the latter's much higher TDP.

Optimizing CPU Inference on x86

# Build llama.cpp with AVX-512 (Ryzen 7000 / Intel 12th+)
cmake -B build -DLLAMA_AVX512=ON -DLLAMA_AVX512_VBMI=ON
cmake --build build --config Release -j

# Run with optimal thread count (= P-core count)
./llama-cli -m model.gguf -t 16 --ctx-size 4096   -p "Your prompt here" -n 256

On Intel 13th/14th gen, using only P-cores (-t 8 for i9-14900K) usually outperforms using all 32 threads due to memory bandwidth contention from E-cores.