Apple M4 Pro
Mid-tier M4 — 273 GB/s bandwidth, up to 48GB.
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Sub-scores sum to 502 / 1000. Headline = 502 × 0.70 (Estimated-confidence discount) = 351. This is an algorithmic performance-tier score — distinct from, and often lower than, the editorial “Our verdict” below, which weighs value and real-world fit (especially for hardware we haven’t measured yet). How scoring works →
Extrapolated from 273 GB/s bandwidth — 38.2 tok/s estimated. No measured benchmarks yet.
Plain-English: Workable at 32B, comfortable at 14B and below — coding agent feels deliberate; vision models supported.
Verdicts extrapolated from catalog VRAM + bandwidth + ecosystem flags. Hover any chip for the rationale. Want measured numbers? Submit your own run with runlocalai-bench --submit.
What it does well
The Apple M4 Pro is the mid-tier MacBook Pro 14"/16" + Mac mini M4 Pro chip and the right Apple Silicon pick for buyers who don't need the full M4 Max but want more capability than base M4. 12 CPU cores (8 performance + 4 efficiency) + 20 GPU cores + 16-core Neural Engine + up to 48 GB unified memory at 273 GB/s bandwidth. The 48 GB unified memory ceiling is enough for 14B FP16 with comfortable context, smaller MoE models, 32B Q4 with limited context, multi-model agentic stacks fitting 32 GB. MLX and llama.cpp Metal both run M4 Pro first-class. For laptop AI buyers who want 30B-class workloads but don't pay for 70B-FP16 capability, M4 Pro is the right balance — typical Mac mini M4 Pro configurations land at $2,000-$2,500 with 24-48 GB unified memory.
Where it breaks
- No CUDA — full stop. Same fundamental constraint as all Apple Silicon.
- Bandwidth ceiling. 273 GB/s is meaningfully below M4 Max's 546 GB/s and well below discrete-GPU laptop bandwidth (RTX 5090 Mobile at ~1 TB/s). For memory-bound decode, M4 Pro is firmly mid-tier.
- Memory ceiling at 48 GB. M4 Max in MacBook Pro 16 goes to 128 GB. M4 Pro caps at 48 GB. For 70B FP16 / 235B-class workloads, M4 Pro doesn't fit.
- GPU core count is half M4 Max. 20 cores vs 40 cores — meaningful gap on compute-bound workloads.
- Day-zero new model support is uneven. llama.cpp Metal usually has new architectures within hours; MLX takes days-to-weeks.
Ideal model range
- Sweet spot: 7B-14B FP16 inference at ~30-50 tok/s decode with 32K context.
- Sweet spot: 32B Q4-Q5 with 16K context — fits 48 GB comfortably.
- Sweet spot: Smaller MoE inference (Qwen 3 30B-A3B at Q4-Q5) — fits 48 GB with reasonable speed.
- Sweet spot: Multi-model agentic loops fitting 32 GB total — 14B + 7B + embedding + speculative decoder.
- Sweet spot: Mac mini M4 Pro form factor — silent, low-power, low-footprint compute.
- Stretch: 70B Q3/Q4 partial-offload (slow but functional with 48 GB unified).
- Bad fit: 70B FP16, 235B+ models, CUDA-required workflows.
Bad use cases
- 70B+ FP16 workloads. Pick M4 Max in MacBook Pro 16 (128 GB).
- CUDA-locked stacks. Pick discrete-GPU laptop.
- Maximum decode throughput. Discrete laptop GPUs win on bandwidth.
- Cost-floor laptop AI. Base M4 (no Pro) at $999 is cheaper but with 16 GB ceiling.
- Production serving. Wrong tier.
Verdict
Buy this (in MacBook Pro 14"/16" or Mac mini M4 Pro form) if you want Apple Silicon at the 30B-class capability tier, you don't need 70B FP16 capability, you value unified memory + silence + battery life, and your stack is MLX / llama.cpp Metal compatible. M4 Pro is the right balance for the "serious local AI on Apple Silicon without the M4 Max premium" segment.
Skip this if you target 70B FP16 (pick M4 Max with 128 GB unified), you need CUDA (pick discrete-GPU laptop), you're cost-floor (base M4 at $999 is cheaper for 7B-14B-class work), or you want maximum throughput (RTX 4070 Mobile or higher discrete GPU laptops win).
How it compares
- vs Apple M4 Max → M4 Max has 2× GPU cores + 2× memory bandwidth (546 vs 273 GB/s) + up to 128 GB memory ceiling at +$1,200-1,500 chip premium. The strict upgrade for serious 70B-class local AI.
- vs Apple M4 (base) → Base M4 has 10 GPU cores + 16 GB memory ceiling at $999 chip MSRP. M4 Pro is +20-30% performance + 3× memory ceiling at +$500 premium.
- vs Apple M2 Pro → Prior-gen at lower bandwidth + memory ceiling. M4 Pro is the strict architectural upgrade.
- vs Razer Blade 16 (RTX 5090 Mobile, 24 GB CUDA) → Razer Blade 16 has CUDA + Blackwell + dramatically more decode throughput at +$2,000. M4 Pro wins on battery life, silence, ecosystem maturity (MLX), Mac integration. Pick by ecosystem.
- vs AMD Ryzen AI 9 HX 370 → HX 370 has Windows + AMD ecosystem at $1,599 retail. M4 Pro has Apple Silicon + MLX + better battery. Pick by OS preference.
Overview
Mid-tier M4 — 273 GB/s bandwidth, up to 48GB.
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Specs
| VRAM | 0 GB |
| System RAM (typical) | 48 GB |
| Power draw (peak) | 60 W |
| Released | 2024 |
| Backends | Metal MLX |
Hardware worth comparing
The closest alternatives by price, memory bandwidth, and form factor, plus a step up and down — so you can frame the buying decision against real options.
Curated head-to-heads against specific cards — the buyer-decision shape that crosses VRAM bands.
M4 Pro on a Mac mini is the silent always-on AI box for many operators. The guides below cover the Mac-mini and Mac-budget buyer decisions.
Frequently asked
Does Apple M4 Pro support CUDA?
Where next?
Reviewed by RunLocalAI Editorial. See our editorial policy for how we research and verify hardware specifications.