apple
SOC
96GB unified
enthusiast

Apple M3 Max

M3 Max — 400 GB/s bandwidth, up to 128GB.

Released 2023
Our verdict
By Fredoline Eruo·Last verified May 6, 2026
8.5/10
What it does well

Unified memory architecture. A 64 GB M3 Max runs Llama 3.3 70B at Q4 entirely in fast memory — no system-RAM offload, no partial-offload speed cliff, just steady 12–18 tok/s. The 96 GB and 128 GB configurations open up Llama 4 Scout / DeepSeek V3 territory that no consumer NVIDIA card touches. MLX is a clean, fast runner that takes advantage of the architecture.

Where it breaks
  • Tokens/sec on 7–32B class trails NVIDIA — same model, 4090 is 2–3× faster. Apple wins on accessibility, not raw throughput.
  • MLX is Apple-only — your model investments don't transfer to NVIDIA without re-quantizing.
  • High-memory configs are expensive — a 128 GB M3 Max MacBook Pro pushes past $7,000.
Ideal model range
  • Sweet spot: Llama 3.3 70B / R1 Distill Llama 70B at Q4 — 12–18 tok/s, no offload concerns. The reason you bought it.
  • Stretch (96–128 GB configs): Llama 4 Scout, DeepSeek V3 at Q3/Q4, Llama 4 Maverick with quantization compromises.
  • Comfortable: 32B-class at MLX-native quants with low latency, multimodal models (Gemma 3, Pixtral) with strong vision-language.
Bad use cases
  • Maximum tokens-per-second — NVIDIA wins for any model that fits a 4090.
  • Coder autocomplete — Apple's tok/s on Qwen 2.5 Coder 32B is half a 4090's; latency in the editor matters.
  • Cloud-equivalent throughput per dollar — for high-volume inference, NVIDIA-on-cloud is better $/output.
Verdict

Buy this if you want a portable 70B-capable rig, you already use macOS for development, or you want the easiest path to running models that don't fit a 4090. Skip this if you prioritize raw throughput, run models that fit a 24 GB CUDA card, or do agent-loop / autocomplete workloads where latency matters.

How it compares
  • vs RTX 4090 → 4090 is faster on models that fit 24 GB; M3 Max wins on 70B-class with no offload hassle. Different jobs.
  • vs M2 Ultra (192 GB Mac Studio) → M2 Ultra has more memory bandwidth and unified-memory ceiling — better for Llama 4 Maverick / DeepSeek V3 territory.
  • vs M4 Max → M4 Max is the architectural successor with materially better tokens/sec at the same memory config; pick M4 Max if available.
  • vs Linux + RX 7900 XTX → AMD wins on price for 32B-class; M3 Max wins on 70B-class accessibility and out-of-box experience.
Why this rating

8.5/10 — for users who want to run 70B-class models without hardware acrobatics, Apple Silicon with 64+ GB unified memory is genuinely the easiest path. Slower than CUDA on equivalent VRAM but no offload tax. Loses points on raw tokens-per-second vs NVIDIA + on price for the high-memory configurations.

Overview

M3 Max — 400 GB/s bandwidth, up to 128GB.

Specs

VRAM0 GB
System RAM (typical)96 GB
Power draw95 W
Released2023
Backends
Metal
MLX

Frequently asked

Does Apple M3 Max support CUDA?

No — Apple M3 Max uses Apple Metal and MLX, not CUDA. Most local-AI tools support Metal natively.

Reviewed by RunLocalAI Editorial. See our editorial policy for how we research and verify hardware specifications.