What is the best local LLM for a MacBook Pro M4 Max?

Depends on your RAM: 36 GB → Qwen 3.6 27B at Q6 (~28 GB), 48 GB → Qwen 3.6 27B at Q8 (~30 GB), 64 GB → Llama 3.3 70B at Q5 with 16K context, 96 GB → dual-model setup or 70B at Q6, 128 GB → Mistral Small 4 (119B-A6B) at Q5 or full multi-model routing. For OpenClaw production reliability at any RAM tier, gpt-oss 20B at the highest fitting quant is the safest pick.

What is the best local LLM for coding on an M4 Max?

Laguna XS 2.1 (Poolside, July 2 2026) is the best local agentic-coding pick on the M4 Max. It is a 33B MoE with only 3B active params and scores 70.9% on SWE-Bench Verified. At Q8 (~33 GB) it fits 48 GB and larger variants; use Q4 (~20 GB) on a 36 GB M4 Max. Because only 3B params are active per token, it generates at 30-45 tokens/sec despite the 33B total size.

M4 Max vs RTX 4090 for local LLMs?

4090 wins on raw tokens/sec for 24GB-and-under models (1008 GB/s vs ~410-546 GB/s bandwidth on M4 Max). M4 Max wins on (a) silence, (b) portability, (c) no electricity bill spike, and (d) unified memory above 24 GB. If you want 64+ GB to run 70B models, M4 Max wins outright — a 4090 can't fit them at any quant.

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Hardware July 23, 2026

Best Local LLM for MacBook Pro M4 Max (July 2026): 36 to 128GB Picks

The MacBook Pro M4 Max is Apple's flagship laptop chip for local AI. 36-128 GB unified memory at ~410-546 GB/s bandwidth means you can run Qwen 3.6 27B at Q8 (premium quality), Llama 3.3 70B at Q5 (with 64+ GB), or dual-model OpenClaw routing without breaking a sweat — silent, no fan noise, no electricity spike.

M4 Max OpenClaw setup?

See our AI training options. We'll wire OpenClaw + Ollama for your specific MacBook Pro RAM tier in 30 min.

Updated July 23, 2026: Laguna XS 2.1 (Poolside, July 2) is the new agentic-coding pick. It fits every M4 Max tier — Q4 (~20 GB) on 36 GB, Q8 (~33 GB) on 48 GB+.

Bottom Line by RAM Variant

Your M4 Max	Best Pick	OpenClaw Pick
36 GB	Qwen 3.6 27B (Q6_K) — ~30 GB	gpt-oss 20B (Q5)
48 GB	Qwen 3.6 27B (Q8_0) — ~30 GB	gpt-oss 20B (Q8)
64 GB	Llama 3.3 70B (Q5_K_M) — ~50 GB	gpt-oss 20B (Q8) + Qwen 3.6 27B (Q5) dual
96 GB	Llama 3.3 70B (Q6_K) — ~60 GB	GLM-5.1 32B (Q8) for autonomy
128 GB	Mistral Small 4 (119B-A6B) at Q5 — ~80 GB	gpt-oss 120B (Q4)

For agentic coding at any tier, Laguna XS 2.1 (Q8 ~33 GB) is the best local pick — 33B MoE, 3B active, 70.9% SWE-Bench Verified.

Top Picks for M4 Max (36-128 GB unified, ~410-546 GB/s bandwidth)

1. Qwen 3.6 27B (Q6/Q8) — best at any M4 Max tier

The April 22 release at Q6 (~22 GB) runs comfortably on 36 GB+. At Q8 (~30 GB) it fits 48 GB+. Near-FP16 quality with the model that beat the 397B Qwen 3.5 MoE on agentic coding.

ollama pull qwen3.6:27b-q8_0  # for 48GB+
ollama pull qwen3.6:27b-q6_K  # for 36GB
openclaw config set agents.defaults.models.chat ollama/qwen3.6:27b-q8_0

Expected speed on M4 Max: 20-30 tokens/sec depending on quant.

2. Laguna XS 2.1 (Q8_0) — best agentic coding at any tier

Poolside’s July 2 release. A 33B MoE with only 3B active params, so it generates at small-model speed. It scores 70.9% on SWE-Bench Verified. At Q8 (~33 GB) it fits 48 GB+ M4 Max variants; use Q4 (~20 GB) on the 36 GB tier. Native 256K context — cap it lower on 36 GB to keep headroom. Speed: 30-45 tok/sec because of the sparse MoE.

ollama pull laguna-xs-2.1  # Q8 ~33GB for 48GB+
openclaw config set agents.defaults.models.chat ollama/laguna-xs-2.1

3. Llama 3.3 70B (Q5_K_M) — for 64GB+ variants

About 50 GB at Q5_K_M with 16K context. Premium 70B-class quality. Speed: 12-18 tok/sec on M4 Max.

ollama pull llama3.3:70b-instruct-q5_K_M

4. gpt-oss 20B (Q8_0) — best for OpenClaw production at any tier

About 22 GB at Q8. Cleanest tool-call JSON. Fits even 36 GB M4 Max comfortably.

5. GLM-5.1 32B (Q5_K_M or Q8_0) — best for autonomous runs

Zhipu’s purpose-tuned model for multi-hour agent loops. Q5 (~26 GB) fits 36 GB+. Q8 (~38 GB) fits 48 GB+.

6. Dual-model setup (64+ GB tier)

openclaw config set agents.defaults.models.chat ollama/qwen3.6:27b-q8_0
openclaw config set agents.defaults.models.agent ollama/gpt-oss:20b-q8_0
openclaw config set agents.defaults.keep_alive 1h

Total: ~52 GB hot. Leaves room for context + macOS.

OpenClaw Setup on M4 Max

ollama pull qwen3.6:27b-q8_0
ollama pull gpt-oss:20b-q8_0
openclaw config set agents.defaults.models.chat ollama/qwen3.6:27b-q8_0
openclaw config set agents.defaults.models.agent ollama/gpt-oss:20b-q8_0
openclaw config set agents.defaults.context_limit 65536
openclaw config set agents.defaults.keep_alive 1h

Common Mistakes on M4 Max

Forgetting macOS uses 6-10 GB. Treat 36 GB as 26-30 GB available, 48 GB as 38-42 GB, etc.
Running 128K context with 27B Q8. KV cache eats 20+ GB. Cap at 64K.
Trying to push 70B on the 36GB variant. Q4 70B needs 42 GB just for model weights — not enough headroom. Stay with Qwen 3.6 27B at Q6.
Comparing tok/sec to a 4090 and feeling slow. M4 Max bandwidth is roughly half — that’s the trade for silent + portable + 36-128 GB unified.

🛒 The exact MacBook to buy

If you're still picking, these are the configurations that hit the sweet spots above.

MINIMUM · 24 GB

Apple MacBook Pro M-series 24 GB entry — runs Qwen 3.6 27B at Q4 cleanly. Below M4 Max tier but a great entry.

Check current price on Amazon → RECOMMENDED · M4 Max 48 GB+

MacBook Pro M-series 48 GB+ Hits the M4 Max sweet spot — runs Qwen 3.6 27B at Q8, GLM-5.1 32B, or 70B with the 64 GB variant.

Check current price on Amazon →

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