Best LLM on Mac M4 Max (64–128 GB unified) in 2026

Choose the best LLM for Mac M4 Max depends on one central factor: the available unified memory, which serves simultaneously as system RAM and GPU VRAM. On a MacBook Pro M4 Max with 64 GB or 128 GB, you can access locally a class of models that consumer GPUs NVIDIA (RTX 4090 24 GB, RTX 5090 32 GB) cannot fit without disk offload. This guide compares the open-weights models that are genuinely usable on M4 Max in 2026, segmented by memory configuration, recommended quantization, license, and use case.

Understanding the M4 Max unified memory constraint

The M4 Max includes up to 128 GB of unified LPDDR5X memory shared between the CPU, 40-core GPU, and Neural Engine, with an advertised bandwidth of 546 GB/s per Apple. In practice on macOS, about 75% of this memory can be allocated to the GPU via sysctl iogpu.wired_limit_mb, or ~48 GB on a 64 GB model and ~96 GB on a 128 GB model.

The implications for the best LLM for Mac M4 Max :

• 64 GB unified : target Q4_K_M up to ~40 GB of weights, i.e. the 70B class.
• 128 GB unified : target Q4 up to ~80 GB, or Q8 for 30–40B models.
• Quantifications : Q4_K_M (4.5 effective bits), Q5_K_M (~5.5 bits), Q8_0 (8 bits), FP16 (16 bits). Multiply the parameter count by 0.55 / 0.7 / 1.1 / 2.2 GB/B, respectively, to estimate the footprint.

The runtime llama.cpp Metal remains the reference on Apple Silicon, followed by MLX from Apple for MoE architectures. For quantization details, see our GGUF quantization guide.

Top models for MacBook Pro M4 Max 64 GB

At 64 GB unified memory, the 70B class in Q4_K_M is the capacity/quality sweet spot. Recommended models from the catalog:

• Llama 3.3 70B Instruct (Meta, Llama 3.3 Community) — ~40 GB Q4 VRAM, 128K context. The best general-purpose compromise, strong instruction following, solid multilingual support. See llama-3.3-70b.
• DeepSeek R1 Distill Llama 70B (DeepSeek, Llama 3.3 Community + DeepSeek) — Q4 VRAM ~40 GB, 128K context. R1 distillation based on Llama 70B, with strong chain-of-thought reasoning in math and code. Details on deepseek-r1-distill-llama-70b.
• Qwen 2.5 72B Instruct (Alibaba, Qwen License) — ~42 GB Q4 VRAM, 131K context. Announced performance on par with Llama 3.1 405B across several benchmarks according to the Qwen 2.5 technical report.
• Llama 3.1 Nemotron 70B (NVIDIA, Llama 3.1 Community)—approximately 40 GB of Q4 VRAM. RLHF fine-tuning of Llama 3.1 70B by NVIDIA, with higher Arena-Hard scores than the base model.
• Tülu 3 70B (Allen AI, Llama 3.1 Community)—Q4 VRAM ~40 GB. Open post-training by AI2, transparent and reproducible.

For vision, Qwen 2.5 VL 72B (~42 GB Q4, 128K context) remains the most robust multimodal choice at this size. Compare it with Molmo 72B if visual grounding is the priority.

Tokens/sec observed on an M4 Max with 40 GPU cores using llama.cpp Metal and Q4_K_M: 7–10 tok/s when generating with a 70B model, subject to confirmation depending on loaded context and prompt length.

Top models for MacBook Pro M4 Max 128 GB

At 128 GB unified, you cross two thresholds: dense 100-130B models, and especially MoE (Mixture of Experts), where only a few experts are active per token, drastically reducing compute costs for a given memory footprint.

• gpt-oss 120B (OpenAI, Apache 2.0) — ~70 GB VRAM in Q4, 128K context. OpenAI's first open-weights model, dense, with competitive scores on MMLU and HumanEval. Profile: gpt-oss-120b.
• Llama 4 Scout 109B (Meta, Llama 4 Community) — ~65 GB Q4 VRAM, advertised context of up to 10M tokens (to be confirmed in practice on Metal). 17B active MoE, ideal for long context on an M4 Max with 128 GB.
• Mistral Small 4 (Mistral AI, Apache 2.0) — 119B, ~72 GB Q4 VRAM, 256K context. See mistral-small-4.
• Nemotron 3 Super 120B (NVIDIA, NVIDIA Open Model License) — Q4 VRAM ~72 GB, 128K context.
• Qwen3-Coder-Next 80B-A3B (Alibaba, Apache 2.0) — Q4 VRAM ~48 GB, 262K context. 3B active MoE, remarkable inference speed on M4 Max for code. Details: qwen3-coder-next.
• Hunyuan-A13B Instruct (Tencent) — 80B, Q4 VRAM ~48 GB. 13B active MoE parameters.
• dots.llm1 Instruct (Rednote, MIT) — 142B, Q4 VRAM ~85 GB, 32K context. Fits in Q4 on 128 GB with limited headroom.

The special case of large MoE models: Mixtral 8x22B Instruct (141B, Apache 2.0, Q4 VRAM ~82 GB, 64K context) remains a benchmark for efficiency—see mixtral-8x22b. To take it further, the Mistral Medium 3.5 128B (~74 GB Q4) or Qwen 3.5 122B-A10B (~73 GB Q4, 10B active MoE) are accessible.

235B+ models such as Qwen 3 235B-A22B (~142 GB Q4) or Qwen 3 VL 235B-A22B exceed allocatable memory even in Q4 on 128 GB. You need to drop to Q3_K_M or even Q2_K with quality degradation, or stay within the ≤120B class.

Licenses and commercial use

The choice of best LLM for Mac M4 Max must include the license if the use is professional:

• Apache 2.0 : Mistral Small 4, gpt-oss 120B, Mixtral 8x22B, Qwen3-Coder-Next, Snowflake Arctic. Free for commercial use.
• MIT : dots.llm1, DeepSeek R1, MiMo V2.5. Very permissive.
• Llama 3.x / 4 Community : Llama 3.3 70B, Llama 4 Scout, Nemotron 70B. Restriction on services with > 700M MAU.
• Qwen License : Qwen 2.5 72B and VL 72B. Permissive with specific clauses; read the text.
• NVIDIA Open Model License : Nemotron 3 Super 120B. Conditions specific to NVIDIA.

For a license-by-license comparison, see our open-weight licensing guide.

Benchmarks and real-world use cases

The public scores to remember (verifiable at HuggingFace Open LLM Leaderboard and model cards):

• Code (HumanEval, LiveCodeBench) : Qwen3-Coder-Next 80B and DeepSeek R1 Distill Llama 70B are the best local candidates on M4 Max.
• Reasoning (MMLU, GPQA, AIME) : DeepSeek R1 Distill 70B and gpt-oss 120B lead on 128 GB.
• Long context (RULER, LongBench) : Llama 4 Scout 109B and Mistral Small 4 thanks to their native 256K+ context windows.
• Multilingual (MGSM, Multilingual MMLU) : Qwen 2.5 72B and Mistral Small 4 are solid in French.

Typical use cases on M4 Max 128 GB:

• Local agentic coding assistant : Qwen3-Coder-Next 80B-A3B + VS Code extension via a local llama.cpp server.
• Long-context document analysis : Llama 4 Scout 109B for ingesting large PDFs.
• Private RAG search : gpt-oss 120B Q4 + local vector database.
• Dataset distillation / synthesis : Llama 3.3 70B in a batch loop.

To avoid reinventing the wheel, also see our Llama 3.3 vs. Qwen 2.5 72B comparison and the gpt-oss vs Mistral Small 4 comparison.

Recommended runtimes on macOS Apple Silicon

• llama.cpp Metal : universal, GGUF, support for K and IQ quantizations, the most mature.
• MLX and MLX-LM : Apple framework, best raw performance on some MoEs, see ml-explore/mlx.
• LM Studio : graphical interface for llama.cpp and MLX, good for getting started.
• Ollama : llama.cpp wrapper, easy to install locally. (No cloud call recommended here—see self-hosting guide.)

FAQ

Q: What is the largest usable model on a MacBook Pro M4 Max 128 GB?

With Q4_K_M and system headroom, the practical limit is around 80–90 GB of model size, meaning the 120B dense class (gpt-oss 120B, Mistral Small 4, Nemotron 3 Super 120B) or 140B MoE models such as dots.llm1 and Mixtral 8x22B. Beyond that, you need to drop to Q3 or Q2, with a noticeable quality loss.

Q: Is the M4 Max 64 GB enough for professional coding use?

Yes for most tasks. Llama 3.3 70B and Qwen 2.5 72B in Q4 fit comfortably with ~24 GB remaining for the OS and IDE. For coding specifically, Qwen3-Coder-Next 80B-A3B (~48 GB Q4) also works and offers higher MoE speed.

Q: Which quantization should I choose between Q4_K_M, Q5_K_M, and Q8_0?

Q4_K_M is the production standard: < 2% perplexity loss vs. FP16 on most models. Q5_K_M offers a slight quality gain for 25% more memory. Q8_0 is justified only for models under 32B where memory is not the limiting factor. FP16 remains reserved for fine-tuning.

Q: What generation speed should you expect on an M4 Max?

Estimated on 40 GPU cores, llama.cpp Metal, short context: 7–10 tok/s on a dense 70B Q4, 15–25 tok/s on an 80B-A3B Q4 MoE, 4–6 tok/s on a 120B Q4. To be confirmed depending on macOS version, loaded context size, and batch size. MoEs have a clear advantage.

Q: Does Llama 4 Scout 109B really use 10M of context on Mac?

The architecture allows it, but in practice the usable window is limited by KV-cache memory, which grows linearly with context. On an M4 Max 128 GB, expect 200K-500K tokens to be realistically usable before saturation, subject to confirmation based on cache quantization.

Q: MLX or llama.cpp for the best LLM on a Mac M4 Max?

llama.cpp for maturity, GGUF compatibility, and its ecosystem (Ollama, LM Studio). MLX for certain recent MoEs where the Apple framework makes better use of unified memory bandwidth. Test both on your target model: the differences range from -10% to +30% tokens/sec.

Conclusion

Le best LLM for Mac M4 Max depends on your configuration: Llama 3.3 70B or Qwen 2.5 72B on 64 GB, gpt-oss 120B or Llama 4 Scout 109B on 128 GB, Qwen3-Coder-Next 80B-A3B for coding. Unified memory makes the M4 Max the most capable laptop platform for 70–120B local inference in 2026. Refine your choice based on licensing, context window, and target benchmark via our configurator or browse the entire catalog.