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Best LLM on Mac with 128 GB of unified memory in 2026

Q: MacBook Pro M5 Max 128 GB: really 128 GB in a laptop?

Yes, and it's the only laptop on the market at this tier. The 14- and 16-inch MacBook Pro models go up to 128 GB of unified memory with both M5 Max and M4 Max, but only with the 40-core GPU variant—which on M5 Max is also the only one with 614 GB/s (460 GB/s on the 32-core variant). Llama 70B Q8 + 128k context on the go, without the cloud. See the MacBook Pro M5 Max page.

◆ Mac — Local AI on your Mac, done right — MLX, Ollama, LM Studio on Apple Silicon · $24 · or all kits $49 →

Ranking updated on 09/10/2026

128 GB of unified memory is the premium AI workstation tier—and it is no longer limited to desktops: the 14/16-inch MacBook Pro reaches it with both the M5 Max and M4 Max, provided you choose the 40-core GPU variant (614 GB/s on the M5 Max, versus 460 GB/s on the 32-core variant). Llama 70B in Q8 (~75 GB), 150B MoE in Q4, 200k context for enterprise RAG.

Offers and alternatives for local AI

Compare prices for MacBook Pro M5 Pro — 24 GB / 1 TB from our partner retailers (verified product pages):

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Ranking

🇺🇸 Laguna XS.2

Poolside · 33B parameters · Apache 2.0 · 131,072 tokens ctx

MoE 33B/3B active parameters, Apache 2.0, specializing in agentic coding. 68.2% SWE-Bench Verified, 128k ctx. Runs on a 36 GB Mac. Released April 28, 2026.

Why this ranking MoE 33B/3B active parameters, Apache 2.0, specializing in agentic coding. 68.2% SWE-Bench Verified, 128k ctx. Runs on a 36 GB Mac. Released April 28, 2026.

ollama run laguna-xs.2

On Apple M5 Max (128 GB)

FP16

66 GB · 100 tok/s

🇨🇳 GLM 4.7 Flash

Zhipu AI · 31B parameters · MIT · 128,000 tokens ctx

GLM-4.7-Flash (MoE 31B, ~3B active): the best code/VRAM ratio in the 30B class. MIT, 128k ctx, very fast on 3090/4090.

Why this ranking GLM-4.7-Flash (MoE 31B, ~3B active): the best code/VRAM ratio in the 30B class. MIT, 128k ctx, very fast on 3090/4090.

ollama run glm-4.7-flash

On Apple M5 Max (128 GB)

FP16

62 GB · 100 tok/s

🇺🇸 Granite 4.0 H-Small 32B-A9B

IBM · 32B parameters · Apache 2.0 · 128,000 tokens ctx

Mamba-2 + MoE 32B/9B hybrid. ~70% less RAM in long contexts. Apache 2.0.

Why this ranking Mamba-2 + MoE 32B/9B hybrid. ~70% less RAM in long contexts. Apache 2.0.

ollama run granite4:small-h

On Apple M5 Max (128 GB)

FP16

64 GB · 75 tok/s

🇨🇳 Qwen 3.6 35B-A3B

Alibaba · 35B parameters · Apache 2.0 · 262,000-token context

MoE with 35B/3B active parameters for agentic coding. 73.4% SWE-Bench. Release: April 16, 2026.

Why this ranking MoE with 35B/3B active parameters for agentic coding. 73.4% SWE-Bench. Release: April 16, 2026.

ollama run qwen3.6:35b-a3b

On Apple M5 Max (128 GB)

FP16

70 GB · 60 tok/s

🇨🇳 Qwen 3 30B-A3B

Alibaba · 30B parameters · Apache 2.0 · 131,072 tokens ctx

MoE 30B/3B active hybrid thinking. MMLU 81.4, AIME24 80.4. 100+ languages.

Why this ranking MoE 30B/3B active hybrid thinking. MMLU 81.4, AIME24 80.4. 100+ languages.

ollama run qwen3:30b-a3b

On Apple M5 Max (128 GB)

FP16

62 GB · 100 tok/s

🇺🇸 Nemotron Cascade 2 30B-A3B

NVIDIA · 30B parameters · NVIDIA Open Model License · 128,000 tokens ctx

MoE with 30B/3B active: thinking mode + instruct. Gold medalist at IMO 2025 and IOI 2025. Fast inference thanks to the 3B active parameters, with 30B-level reasoning capabilities. Released April 2026.

Why this ranking MoE with 30B/3B active: thinking mode + instruct. Gold medalist at IMO 2025 and IOI 2025. Fast inference thanks to the 3B active parameters, with 30B-level reasoning capabilities. Released April 2026.

ollama run nemotron-cascade-2

On Apple M5 Max (128 GB)

FP16

60 GB · 80 tok/s

🇨🇳 Qwen3-Coder 30B-A3B

Alibaba · 30B parameters · Apache 2.0 · 262,144-token context

MoE 30B (3.3B active parameters) specialized in agentic coding. Very fast locally, native 256k ctx, the benchmark for 16–24 GB via Ollama.

Why this ranking MoE 30B (3.3B active parameters) specialized in agentic coding. Very fast locally, native 256k ctx, the benchmark for 16–24 GB via Ollama.

ollama run qwen3-coder:30b

On Apple M5 Max (128 GB)

FP16

61 GB · 100 tok/s

🇨🇳 Qwen 3 VL 30B-A3B

Alibaba · 30B parameters · Apache 2.0 · 262,144-token context

Vision MoE with 30B/3B active. Vision sweet spot Qwen 3. 256k ctx.

Why this ranking Vision MoE with 30B/3B active. Vision sweet spot Qwen 3. 256k ctx.

ollama run qwen3-vl:30b

On Apple M5 Max (128 GB)

FP16

62 GB · 100 tok/s

Comparison table

Rank	Model	Params	Q4 VRAM	Context	License	On Apple M5 Max (128 GB)
#1	Laguna XS.2	33B	19 GB	131 072	Apache 2.0	100 tok/s · FP16
#2	GLM 4.7 Flash	31B	19 GB	128 000	MIT	100 tok/s · FP16
#3	Granite 4.0 H-Small 32B-A9B	32B	19 GB	128 000	Apache 2.0	75 tok/s · FP16
#4	Qwen 3.6 35B-A3B	35B	21 GB	262 000	Apache 2.0	60 tok/s · FP16
#5	Qwen 3 30B-A3B	30B	19 GB	131 072	Apache 2.0	100 tok/s · FP16
#6	Nemotron Cascade 2 30B-A3B	30B	17 GB	128 000	NVIDIA Open Model License	80 tok/s · FP16
#7	Qwen3-Coder 30B-A3B	30B	19 GB	262 144	Apache 2.0	100 tok/s · FP16
#8	Qwen 3 VL 30B-A3B	30B	19 GB	262 144	Apache 2.0	100 tok/s · FP16

The Mac kit

Local AI on your Mac, fully explored: unified memory, MLX vs. GGUF, the right model for your chip, Ollama and LM Studio tuned for Apple Silicon.

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Free memo

Which coding model should you run on YOUR machine?

Get the memo VRAM → best coding model → Ollama command (one screen, copy and paste). Then switch to the Copilote Local kit for a setup that actually works.

The Local Copilot kit — the Ollama + Cline + Aider configs are ready to paste, with tuned Modelfiles, troubleshooting, and lifetime online access →

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Your card → the best coding model to run locally, and the exact Ollama command:

Your VRAM	Typical GPUs / Macs	Recommended coding model	Command Ollama
8 GB	RTX 4060 / 3060 · M1-M2 16 GB	Qwen 3.5 9B (Q4, 6.6 GB — 256k context)	`ollama run qwen3.5:9b`
12 GB	RTX 3060 12 GB / 4070 / 5070	Qwen 3.5 9B (Q8, 11 GB) or Gemma 4 12B (7.6 GB)	`ollama run qwen3.5:9b-q8_0`
16 GB	RTX 5070 Ti / 4080 / 5080 · RX 9070 XT · M4 24 GB	Devstral 24B (Q4, 14 GB) — coding-agent specialist	`ollama run devstral:24b`
24 GB	RTX 3090 / 4090 · RX 7900 XTX · M4 Pro 48 GB	Qwen 3.8 27B (Q4, 18 GB) — the “close to Copilot” option	`ollama run qwen3.8:27b`
32 GB	RTX 5090	Qwen 3.6 35B-A3B (Q4, 23 GB) — fast MoE	`ollama run qwen3.6:35b`
48 GB+	Mac M4 Max 64 GB · M2 Ultra 128 GB	Qwen3-Coder 30B-A3B (Q8, 32 GB — 256k context)	`ollama run qwen3-coder:30b-a3b-q8_0`

💡 Chat/agent ≠ autocomplete. The table above is your model’s chat and agent. For inline autocomplete (FIM), keep a small dedicated model as a variant -base : ollama run qwen2.5-coder:7b-base — it’s still the reference for this specific use case. ⚠️ Qwen 3.8: its reasoning is set very high by default and it “overthinks” simple requests — lower it to low (or turn it off) on first launch. ⚠️ License trap: Codestral 22B = Mistral Non-Production License → prohibited for coding at work. Qwen 3.5/3.8, Gemma 4, and Devstral are Apache 2.0. 💡 Running out of memory? Keep ~1.5 GB of VRAM free for context, or drop down one quantization level.

🔌 To connect it to VS Code: Cline (multi-file agent), Aider (CLI) or Tabby/Twinny (FIM autocomplete) — they all connect to Ollama locally. The kit Local Copilot — ready-to-paste configs + tested setup — is available: /copilote-local.

Ranking methodology

Filter: 30–250B models whose Q4_K_M fits under 96 GB (leaving 32 GB for macOS + massive context). Bonus: 70–150B (128 GB peak) and MoE up to 250B.

Criteria considered:

Q4_K_M ≤ 96 GB
70B Q8, comfortable
Accessible 150B+ MoE
Stable 200k context

The scoring is fully transparent: see our methodology for details on VRAM/tokens/sec calculations.

Frequently asked questions

Mac 128 GB: Llama 70B Q8 or 123B Q5?

Llama 70B Q8 (~75 GB) at 10–14 tokens/sec on M4 Max. Mistral Large 123B Q5 (~85 GB) at 8–12 tokens/sec. Q8 on 70B is generally more useful (near-FP16, marginally better than Q6 elsewhere). 123B remains more capable overall.

Frontier MoE on 128 GB: feasible?

DeepSeek V4 Flash 284B (13B active MoE) Q3_K_M (~140 GB) doesn't fit — you need a Mac Studio with 192+ GB. Granite 4 Mamba 150B Q4 (~80 GB) fits. For frontier 200B+, move to Mac Studio Ultra.

MacBook Pro M5 Max 128 GB: really 128 GB in a laptop?

Yes, and it is the only laptop on the market at this tier. The 14- and 16-inch MacBook Pro models support up to 128 GB of unified memory with both M5 Max and M4 Max, but only in the 40-core GPU variant—with the M5 Max, it is also the only one with 614 GB/s (460 GB/s on the 32-core variant). Llama 70B Q8 plus 128k context on the go, without the cloud. See the MacBook Pro M5 Max specs.

128 GB Mac vs. 2× H100 80 GB server?

2× H100 = ~10× faster on 70B (1700 GB/s per card vs. 546 GB/s unified). But ~€80,000 + 1 kW vs. Mac 128 GB ~€5,000 + 100 W. For personal use or a small team, the Mac wins by a mile on €/GB.

Go further

QuelLLM Kits The reference guide by use case

Mac Local Copilot Local AI Local Agents Local RAG AI for Business Unfiltered AI AI Images

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