01 // Inference benchmarks
Single-stream decode · llama.cpp
# env llama.cpp b4732 · 4096 ctx · batch=1 · prompt=512 · temp=0.0 · median of 5 runs
01b // Performance across quantization
vs. nearest competitors
How tok/s scales from FP16 → Q8 → Q4 compared to GPUs in a similar price/VRAM range.
02 // Hardware specs
ArchitectureM4 Pro
Process nodeTSMC 3nm
Memory48 GB
Memory bandwidth273 GB/s
FP16 compute15 TFLOPS
INT8 compute30 TOPS
TDP70 W
PCIeUnified
Form factorLaptop
CoolingActive
03 // Model fit
Approximate VRAM required to load weights + 4096 ctx KV cache.
+ STRENGTHS
- ✓48GB VRAM is enough for 70B-class models at Q8
- ✓273 GB/s memory bandwidth · top tier in its class
- ✓Strong tooling: FP16, Q8, Q4, MLX all officially supported
− TRADE-OFFS
- −Draws 70W under load — plan PSU and thermals accordingly
- −Limited to laptop chassis
- −Mac-only — CUDA tooling won't run
related research
Research behind Apple M4 Pro inference tradeoffs
These papers explain the quantization, cache, bandwidth, and runtime constraints that matter before buying this GPU for local AI.
Local AI inference papers
llama.cpp, Apple Silicon, constrained GPUs, offload, and one-box inference.
Open KV cache optimization papers
Cache quantization, compression, reuse, and long-context memory pressure.
Open LLM quantization research
GPTQ, AWQ, GGUF, FP4, NF4, and what low-bit formats mean for VRAM fit.
Open