Is the M4 Max better than the M3 Max for AI?

Yes, meaningfully so. The M4 Max increases memory bandwidth from 400 GB/s to 546 GB/s — a 36% improvement. Since LLM inference is memory-bandwidth-bound, this translates directly to faster tokens per second. The M4 Max also supports LPDDR5X memory, which contributes to the bandwidth gain. For new purchases, the M4 Max is the clear choice.

Should I upgrade from M2 to M4 for AI work?

If you have an M2 Max or M2 Ultra and run large models, the M4 Max is a worthwhile upgrade primarily for bandwidth. The M2 Max and M3 Max share the same 400 GB/s bandwidth, so the M4 Max at 546 GB/s is the first meaningful speed increase since the M1 Ultra. If you have an M2 Ultra at 800 GB/s, the upgrade case is weaker unless you want the M4's other improvements.

Can the M3 Max run Llama 3 70B?

Yes. The M3 Max at 64GB runs Llama 3 70B comfortably at Q4 quantization (around 38GB). Performance is similar to the M2 Max at the same memory tier — both at 400 GB/s bandwidth. Expect 7-10 tokens per second at Q4, slightly slower than the M4 Max at 8-12 tok/s.

What is the best Apple Silicon chip for AI in 2026?

The M4 Max at 64GB or 128GB is the best consumer Apple Silicon chip for AI in 2026. It offers the highest bandwidth (546 GB/s) in a laptop form factor and the largest memory pool without going to the M4 Ultra. The M4 Ultra at 192GB is the top of the range but is only available in Mac Pro at significant cost.

Does the M3 chip use 3nm? Does that help AI performance?

Yes, M3 chips use TSMC's 3nm process (N3B), an improvement from the M2's 5nm (N4P). This improves power efficiency and enables Dynamic Caching on the GPU, which helps memory-intensive workloads. However, the memory bandwidth stayed the same between M2 Max and M3 Max (both 400 GB/s), so real-world LLM performance differences are modest.

March 27, 2026apple-silicon, mac, hardware, comparison

Apple Silicon for AI: M4 vs M3 vs M2 Comparison

How do M2, M3, and M4 compare for running AI models locally? A practical breakdown of bandwidth, memory tiers, LLM performance, and which generation is worth buying for local AI workloads in 2026.

Apple Silicon has evolved rapidly across three generations: M2 (2022), M3 (2023), and M4 (2024-2025). For AI workloads specifically, the differences matter — but not always in the ways the spec sheets suggest. This guide breaks down what changed generation-by-generation and which chip makes sense for running models locally today.

Generation at a Glance

Chip	Process	Max Memory	Bandwidth	Best LLM (Max config)
M2 Max 32GB	5nm	96GB (Ultra)	400 GB/s	Llama 3 70B Q4
M2 Ultra 128GB	5nm	192GB	800 GB/s	Llama 3 70B FP16
M3 Max 64GB	3nm	128GB (Ultra)	400 GB/s	Llama 3 70B Q6
M3 Ultra 192GB	3nm	192GB	819 GB/s	Llama 3 405B Q2
M4 Max 64GB	3nm 2nd gen	128GB	546 GB/s	Llama 3 70B Q8
M4 Max 128GB	3nm 2nd gen	128GB	546 GB/s	Any model under 70B at FP16

The single most important number for AI inference is memory bandwidth. Every generation of Max chip doubles the base MacBook Pro bandwidth, and the Ultra configurations double it again by linking two dies.

M2: The Baseline

The M2 generation (2022) established the template for Apple Silicon AI performance. The M2 Max at 32GB and 96GB configs brought serious memory bandwidth to a laptop — 400 GB/s — that beat every consumer NVIDIA GPU of its era in memory capacity, if not raw compute speed.

M2 Max strengths for AI:

32GB fits most 13B and 30B models at Q8
96GB (M2 Max upgrade via unified 96GB) handles 70B at Q4-Q6
Excellent llama.cpp compatibility via Metal backend
Still very capable in 2026 — no model under 70B Q4 will stress it

M2 Ultra (128GB) was a breakthrough: At 800 GB/s with 128GB of unified memory, the M2 Ultra was the first consumer chip that could run Llama 3 70B at Q8 without offloading. It remains competitive today — the bandwidth advantage over the M4 Max (800 vs 546 GB/s) means the M2 Ultra can actually be faster for large models.

M2 limitation: The 5nm process runs warmer and draws more power than M3 or M4 under sustained AI load. Thermal throttling during long generation runs is more noticeable than on later chips.

M3: Incremental, Not Revolutionary

The M3 generation (2023) moved to TSMC's 3nm process and introduced Dynamic Caching on the GPU — a feature that allocates GPU memory more efficiently for workloads with varying memory demands. For AI, this helps with image generation workflows that use ControlNet, LoRA stacks, or high-resolution upscaling.

What changed from M2 to M3:

	M2 Max	M3 Max
Process	5nm	3nm
Max bandwidth	400 GB/s	400 GB/s
GPU dynamic cache	No	Yes
Power efficiency	Baseline	~20% better
Max memory	96GB	128GB

The bandwidth stayed the same. For LLM inference — which is almost entirely bandwidth-limited — the M3 Max does not noticeably outperform the M2 Max at equivalent memory configurations. The practical difference in tokens per second is in the noise.

Where M3 wins:

Image generation with complex pipelines (Dynamic Caching helps)
Sustained workloads (better thermal performance, less throttling)
128GB config unavailable on M2 Max (only reachable via M2 Ultra)
Power efficiency matters for long generation runs on battery

M3 Max 64GB model recommendations:

Llama 3 70B at Q4-Q6 — Core use case. ~7-10 tok/s
Qwen 3 30B at Q8 — Excellent quality/speed balance
Flux Dev at FP16 — Full 33GB model loads natively
DeepSeek R1 at Q4 — Fits comfortably

M4: The Bandwidth Jump

The M4 generation (2024-2025) is where Apple Silicon makes a meaningful leap for AI. The M4 Max moves from 400 GB/s to 546 GB/s — a 36% increase in memory bandwidth. It also moves to LPDDR5X memory and a second-generation 3nm process (N3E), improving power efficiency further.

What changed from M3 to M4:

	M3 Max	M4 Max
Process	3nm (N3B)	3nm gen 2 (N3E)
Bandwidth	400 GB/s	546 GB/s
Memory type	LPDDR5	LPDDR5X
Neural Engine	16-core	16-core
FP16 compute	~48 TFLOPS	~54 TFLOPS

For LLM inference at the same model and quantization, the M4 Max is roughly 30-35% faster than the M3 Max. Where an M3 Max generates 7-9 tok/s for Llama 3 70B Q4, the M4 Max generates 8-12 tok/s.

M4 Max 64GB model recommendations:

Llama 3 70B at Q6-Q8 — Benefits from extra bandwidth headroom
Llama 3 405B at Q2 — Possible on 128GB config
Flux Dev at FP16 — Native load with faster generation than M3
Wan Video 14B — Video generation without offloading
DeepSeek R1 at Q4-Q6 — Higher quant levels available

The Memory Tier Decision

Across all three generations, the memory tier matters more than the generation for what models you can run:

24GB (M2 Pro, M3 Pro, M4 Pro): Matches an RTX 4090 in capacity but with lower bandwidth (200-273 GB/s). Runs models up to 13B at high quality, 30B at Q4. Misses the unified memory advantage that makes Apple Silicon compelling.

32-64GB (M2 Max, M3 Max, M4 Max): The sweet spot for local AI. Loads all 70B models at Q4-Q6. Loads Flux Dev at FP16. Video generation without offloading. This is the minimum tier worth considering for serious AI work.

96-128GB (M2 Max 96GB, M3 Max 128GB, M4 Max 128GB): Runs 70B models at Q8 or FP16. Loads models that require 80-110GB. The M4 Max 128GB is the most capable single-die Apple chip available in 2026.

192-256GB (M2 Ultra, M3 Ultra, M4 Ultra): For research-grade workloads. Runs Llama 3 405B at Q4. Loads any open-weight model in existence. Only available in Mac Pro/Mac Studio at $6,000+.

Generation-by-Generation: Which Should You Buy?

If you are buying new in 2026: Get the M4 Max. The bandwidth improvement is real, the efficiency is better, and the 128GB config is available. There is no reason to buy M3 or M2 new.

If you have an M3 Max: The upgrade to M4 Max delivers ~30% faster inference. Worthwhile if inference speed is a bottleneck, not urgent otherwise.

If you have an M2 Max (32-96GB): The M4 Max is a meaningful upgrade — both for bandwidth and efficiency. If you run large models daily, the performance difference will be noticeable.

If you have an M2 Ultra (128GB): This is the interesting case. The M2 Ultra at 800 GB/s is actually faster than the M4 Max at 546 GB/s. Upgrading loses bandwidth unless you go to M4 Ultra (which adds 128GB capacity). Consider upgrading only if the M4's other improvements (efficiency, newer Neural Engine) are priorities.

Framework Recommendations by Generation

The software ecosystem has improved significantly across the M2-M4 lifespan:

MLX (Apple's own framework) — Available for all three generations, but model coverage has grown substantially since M2. In 2026, MLX supports virtually all major LLM architectures. Always prefer MLX when your model is supported — it extracts the most from Apple Silicon's architecture.

llama.cpp with Metal — Universal fallback, works on M2, M3, M4. Performance on M4 Max is excellent with recent Metal optimizations. Use this for any GGUF model MLX doesn't support.

Diffusers with MPS — Image and video generation via Hugging Face. Works across all generations, with performance scaling with bandwidth. ComfyUI also runs well on all three via MPS.

Quick Reference: What Runs on What

Model	M2 Max 32GB	M3 Max 64GB	M4 Max 64GB	M4 Max 128GB
Llama 3 8B Q8	Yes	Yes	Yes	Yes
Llama 3 70B Q4	No (32GB)	Yes	Yes	Yes
Llama 3 70B Q8	No	Yes (tight)	Yes	Yes
Llama 3 70B FP16	No	No	No	Yes
Flux Dev FP16	No	Yes	Yes	Yes
Wan Video 14B	No	Yes	Yes	Yes
DeepSeek R1 Q4	No	Yes	Yes	Yes
Llama 3 405B Q2	No	No	No	Yes

For the M2 Max at 32GB, most 70B models require the 96GB configuration. Check exact requirements at /macs/m4-max-64gb or use the hardware calculator for your specific configuration.