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Best LLM on Mac Studio Ultra in 2026

Q: Mac Studio M2 Ultra 192 GB: Llama 70B running smoothly?

Yes — Llama 3.3 70B runs at around 11 tok/s in Q5_K_M (~48 GB) and 14 tok/s in Q4 (estimates: around 70% of the ceiling set by its 800 GB/s). It was the first Apple hardware capable of running a 70B comfortably locally. See the Mac Studio guide.

Q: Mac Studio Ultra 512 GB: DeepSeek 671B?

Yes — DeepSeek R1 671B (37B active, MoE) fits in Q4_K_M (~400 GB) on an M3 Ultra or an M5 Ultra with 512 GB, at a usable speed because only 37B parameters are read per token. No other desktop machine can do this. See DeepSeek R1 671B.

◆ 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

The Mac Studio (M2 / M3 / M5 Ultra, up to 512 GB of unified memory, 800 GB/s to 1.2 TB/s) is the most capable consumer workstation for local AI. 70B in Q5, 200B in Q4, frontier 670B in Q3.

Offers and alternatives for local AI

Mac Studio : purchasing alternative available for local AI — Mac Studio M5 Max (36 GB / 512 GB) (Other memory tiers: the Apple Store BTO configurator.):

A mini PC is a complete machine: check the required memory and software compatibility. It does not replace macOS/MLX or CUDA.

AmazonPC alternative: GMKtec EVO-X2 64 GB / 1 TB (Ryzen AI Max+ 395) →

Why this choice? Our complete guide to Mac Studio M5 Max (36 GB / 512 GB) →

Which PC should you choose for your budget? Our picks from €800 to €3,500 →

Affiliate links — QuelLLM may earn a commission on purchases at no extra cost to you, which does not influence the ranking (established independently). As an Amazon Associate, BestLLMfor earns from qualifying purchases.

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 M2 Ultra (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 M2 Ultra (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 M2 Ultra (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 M2 Ultra (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 M2 Ultra (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 M2 Ultra (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 M2 Ultra (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 M2 Ultra (128 GB)

FP16

62 GB · 100 tok/s

Comparison table

Rank	Model	Params	Q4 VRAM	Context	License	On Apple M2 Ultra (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: 7–700B (frontier MoE models allowed). Big bonus for 30–200B (peak Studio Ultra) and MoE in general: 800+ GB/s bandwidth really leverages these models.

Criteria considered:

64–512 GB unified memory
800 GB/s to 1.2 TB/s bandwidth
Compatible MoE and frontier models
Powerful stable LLM server

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

Frequently asked questions

Mac Studio M2 Ultra 192 GB: Llama 70B running smoothly?

Yes—Llama 3.3 70B runs at approximately 11 tok/s in Q5_K_M (~48 GB) and 14 tok/s in Q4 (estimates: approximately 70% of the ceiling set by its 800 GB/s). It was the first Apple hardware capable of running a 70B comfortably locally. See the Mac Studio guide.

Mac Studio Ultra 512 GB: DeepSeek 671B?

Yes — DeepSeek R1 671B (37B active parameters, MoE) fits in Q4_K_M (~400 GB) on an M3 Ultra or an M5 Ultra with 512 GB, at a usable speed because only 37B parameters are read per token. No other desktop machine can do this. See DeepSeek R1 671B.

Mac Studio vs. 4× H100 server?

4× H100 (320 GB HBM3) costs ~120 000 € + 2 kW power. A 512 GB Mac Studio costs more than 10 000 €, at approximately 200 W. The H100 is ~5-10× faster in throughput, but the Studio wins on €/GB of memory and silence.

Is MLX mandatory on Studio Ultra?

Recommended. MLX makes better use of unified memory and is often faster than llama.cpp on large models. Ollama (llama.cpp Metal) works, but slightly underutilizes the machine.

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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