Best LLM on Mac M4 Pro (24-48 GB unified) 2026

Choosing the best LLM for a Mac M4 Pro depends primarily on the available unified memory: an M4 Pro with 24 GB won't run the same weights as an M4 Pro with 48 GB. Apple silicon shares RAM and VRAM through unified memory, providing access to 70B models in 4-bit quantization where a consumer card tops out at 24 GB. This guide compares the open-weight models that actually run on this configuration, details viable quantizations and observed speeds, and covers the software stack (Ollama, llama.cpp, MLX) to favor for strict self-hosting.

Memory constraints of a Mac M4 Pro for local inference

The M4 Pro offers 24 GB or 48 GB of unified memory, depending on the configuration ordered. macOS reserves approximately 4 to 8 GB for the system and applications, leaving approximately:

• Mac M4 Pro 24 GB : ~16–18 GB usable for the LLM
• Mac M4 Pro 48 GB : ~38–42 GB usable for the LLM

The memory bandwidth announced by Apple is 273 GB/s on the M4 Pro, according to the official Apple Silicon spec sheet, a major limiting factor for tokens/sec throughput since decoding inference is bandwidth-bound. The wired memory limit can be increased via sudo sysctl iogpu.wired_limit_mb to allow a larger model to reside entirely in GPU memory, a technique documented by the llama.cpp community.

70B models in Q4 on 48 GB M4 Pro

The 48 GB configuration opens access to 70B Q4_K_M models, the quality-to-footprint sweet spot of the Llama family and its derivatives. Serious candidates:

• Llama 3.3 70B Instruct : ~40 GB in Q4, Llama 3.3 Community license, 128k context. MMLU score reported by Meta on HuggingFace around 86 (to be confirmed depending on the evaluation protocol). Estimated speed: 7-10 tokens/sec decoding on an M4 Pro 48 GB via llama.cpp Metal.
• Qwen 2.5 72B Instruct : ~42 GB in Q4, Qwen license, 131k context. Excellent at coding and structured reasoning, it often outperforms Llama 3.3 on HumanEval according to Alibaba benchmarks.
• DeepSeek R1 Distill Llama 70B : ~40 GB in Q4, R1 distillation on Llama 3.3 architecture, 128k context. Optimized for chain-of-thought and AIME, it is the best reasoning compromise in this memory range.
• Llama 3.1 Nemotron 70B : ~40 GB in Q4, fine-tune NVIDIA focused on alignment and response quality.

At 48 GB, targeting Q4_K_M remains prudent: Q5 (~48–50 GB) works but saturates, risking swap and a collapse in throughput. To compare these models, see Llama 3.3 70B vs Qwen 2.5 72B.

MoE models: speed on 32–48 GB

Mixture-of-Experts architectures activate a fraction of the total parameters for each token, drastically reducing computation while retaining the total memory footprint. Two notable candidates for M4 Pro 48 GB:

• Qwen3-Coder-Next 80B-A3B : 80B parameters but only 3B active per token. ~48 GB in Q4, which is at the upper limit of the M4 Pro 48 GB; consider Q3_K_M (~36-38 GB) for more headroom. 262k context, Apache 2.0. The activation ratio is ideal on Apple Silicon, where memory bandwidth matters more than raw compute.
• Hunyuan-A13B Instruct : 80B with 13B active, ~48 GB in Q4. Tencent Hunyuan license (check the commercial-use terms before deployment). 262k context.

On these architectures, observed throughput can exceed 20 tokens/sec decoding on an M4 Pro 48 GB (estimated from benchmarks published by the MLX community), making the interactive experience much smoother than with a dense 70B.

24 GB configurations: aggressive quantization

A 24 GB M4 Pro can't fit any 70B model in Q4. Three strict self-hosting options:

1. Llama 3.3 70B Q2_K (~26 GB) with partial offloading to an SSD: possible but slow, with a noticeable loss in quality. Not recommended.
2. Dense 30–40B models in Q4 (outside the catalog above, see /catalogue for the 32B options).
3. Compact MoE models such as Qwen3-Coder-Next in Q3 (~36–38 GB) remain out of reach at 24 GB.

For this profile, the pragmatic recommendation is to target ≤16 GB Q4 models (7B–14B families), which fall outside the scope of this 70B+ focused article. See also our comparison best LLM for Mac M4 for entry-level configurations.

Recommended technical stack: Ollama, llama.cpp, MLX

Three runtimes dominate the Apple Silicon ecosystem:

• Ollama : simple packaging, model management, local HTTP API. Ideal for getting started. See our Ollama guide on Mac. The M4 Pro Ollama benefits from the automatic Metal backend. No cloud calls; the binary and weights reside locally.
• llama.cpp : underlying engine, fine-grained parameter control (-ngl, --mlock, KV cache size). The repository ggerganov/llama.cpp publishes optimized Metal binaries.
• MLX : native Apple framework, designed for unified memory. The weights quantized via mlx-lm make better use of the Apple hardware than generic GGUFs on certain models. Documentation github.com/ml-explore/mlx.

For a MacBook Pro M4 running an LLM daily, Ollama covers 90% of needs. For maximum performance or research, MLX becomes relevant.

FAQ

Q: Which quantization should you choose on an M4 Pro with 48 GB?

Q4_K_M is the standard for 70B models on 48 GB: a proven quality/footprint tradeoff, ~40 GB for Llama 3.3 or Qwen 2.5 72B. Q5 saturates memory. Q3_K_M frees up ~10 GB at the cost of noticeable degradation in code and complex reasoning.

Q: Can you run DeepSeek V3 or R1 671B on an M4 Pro with 48 GB?

No. DeepSeek V3 671B requires ~400 GB in Q4, or 8 to 10 times the memory of an M4 Pro 48 GB. Reserve these models for Mac Studio M4 Ultra 192-512 GB systems or multi-GPU servers.

Q: How many tokens/sec should I expect on an M4 Pro 48 GB with Llama 3.3 70B Q4?

Estimated decoding speed is 7 to 10 tokens/sec, depending on context length and runtime. Prompt processing is much faster (50-100 tokens/sec). Figures to be confirmed on your exact configuration.

Q: MLX or llama.cpp—which should you choose?

llama.cpp remains more mature and compatible with the universal GGUF ecosystem. MLX is better on some MoE models and offers native Apple integration. Test both on the target model before deciding.

Q: Is Qwen3-Coder-Next 80B-A3B viable on 48 GB?

At the limit. Q4 uses ~48 GB, leaving no headroom. Prefer Q3_K_M (~36-38 GB estimated), which leaves room for the long-context KV cache. The 3B active/80B total ratio is extremely favorable for throughput on Apple Silicon.

Conclusion

The best LLM for a Mac M4 Pro depends strictly on your unified memory: Llama 3.3 70B Q4 or Qwen 2.5 72B Q4 on 48 GB for balanced dense models, Qwen3-Coder-Next 80B-A3B Q3 for MoE speed, DeepSeek R1 Distill 70B for reasoning. At 24 GB, target a smaller model. Refine your choice based on your exact RAM and use case via the configurator or browse all models in the catalog.