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Best LLM on Mac with 96 GB of unified memory in 2026

Q: MacBook Pro M3 Max 96 GB in 2026: still relevant?

Yes — it’s the portable workstation price/performance sweet spot. ~€3500 used, 400 GB/s bandwidth, Llama 70B runs smoothly. The M4 Max 128 GB is ~25% faster but 2× more expensive. See MBP M3.

Q: 96 GB vs. 128 GB: what is the leap?

128 GB unlocks 130–150B MoE models (Granite 4 Mamba) in Q6 and Llama 70B Q8 (~75 GB). 96 GB remains limited to Q5/Q6 for 70B. For the ultimate workstation, 128 GB or Studio Ultra. See Mac 128 GB.

◆ 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

96 GB of unified memory (maximum M2/M3/M4 Max, entry-level M2 Studio) provides substantial headroom: Llama 70B in Q5/Q6, 100B models in Q4, 100k+ context for long-form RAG.

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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 M3 Max (64 GB)

35 GB · 40 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 M3 Max (64 GB)

35 GB · 40 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 M3 Max (64 GB)

35 GB · 30 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 M3 Max (64 GB)

38 GB · 22 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 M3 Max (64 GB)

35 GB · 40 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 M3 Max (64 GB)

32 GB · 30 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 M3 Max (64 GB)

35 GB · 40 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 M3 Max (64 GB)

35 GB · 40 tok/s

Comparison table

Rank	Model	Params	Q4 VRAM	Context	License	On Apple M3 Max (64 GB)
#1	Laguna XS.2	33B	19 GB	131 072	Apache 2.0	40 tok/s · Q8
#2	GLM 4.7 Flash	31B	19 GB	128 000	MIT	40 tok/s · Q8
#3	Granite 4.0 H-Small 32B-A9B	32B	19 GB	128 000	Apache 2.0	30 tok/s · Q8
#4	Qwen 3.6 35B-A3B	35B	21 GB	262 000	Apache 2.0	22 tok/s · Q8
#5	Qwen 3 30B-A3B	30B	19 GB	131 072	Apache 2.0	40 tok/s · Q8
#6	Nemotron Cascade 2 30B-A3B	30B	17 GB	128 000	NVIDIA Open Model License	30 tok/s · Q8
#7	Qwen3-Coder 30B-A3B	30B	19 GB	262 144	Apache 2.0	40 tok/s · Q8
#8	Qwen 3 VL 30B-A3B	30B	19 GB	262 144	Apache 2.0	40 tok/s · Q8

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.

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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: 13–150B models whose Q4_K_M fits under 72 GB (leaves 24 GB for macOS + long context). Bonus: 30–100B (96 GB peak) and MoE (up to 150B accessible).

Criteria considered:

Q4_K_M ≤ 72 GB
Comfortable 70B Q5/Q6
100B Q4 models
Stable 100k+ context

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

Frequently asked questions

96 GB Mac: Llama 70B Q5 or Mistral Large 123B Q4?

Llama 70B Q5_K_M (~48 GB) + 32k context = ~58 GB used, 38 GB free. Mistral Large 123B Q4_K_M (~68 GB) just fits, with 28 GB free. Llama 70B Q5 is more comfortable, Mistral Large is more capable. Depending on your use case.

MacBook Pro M3 Max 96 GB in 2026: still relevant?

Yes — this is the portable workstation price/performance sweet spot. ~€3500 used, 400 GB/s bandwidth, Llama 70B runs smoothly. The M4 Max 128 GB is ~25% faster but 2× more expensive. See MBP M3.

100k+ context on a 96 GB Mac?

Yes: Qwen 3 32B Q5 (~22 GB) + 100k KV cache (~25 GB) = ~47 GB, with plenty of headroom. For Llama 70B + 100k ctx (~16 GB KV), Q4_K_M (~40 GB) fits by itself—for a total of ~56 GB, still comfortable.

96 GB vs. 128 GB: what is the leap?

128 GB unlocks 130–150B MoE (Granite 4 Mamba) in Q6 and Llama 70B Q8 (~75 GB). 96 GB remains limited to Q5/Q6 on 70B. For the ultimate workstation, 128 GB or Studio Ultra. See Mac 128 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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