Ranking updated on 09/10/2026
The MacBook Air M3 (8 / 16 / 24 GB, 100 GB/s) remains an excellent laptop for local AI. Without a fan, prioritize 3–9B models in Q4_K_M and small MoEs with few active parameters.
MacBook Air M3 : purchasing alternative available for local AI — MacBook Pro M5 Pro — 24 GB / 1 TB :
Which PC should you choose for your budget? Our picks from €800 to €3,500 →
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4B effective multimodal (text+image+audio). 140 languages. For laptops and edge devices.
ollama run gemma4:e4b
Dense 8B Apache 2.0, 12 languages including FR, 131k context, GQA 32Q/8KV. MMLU 73.84, HumanEval 85.37. Released April 29, 2026.
# HuggingFace : ibm-granite/granite-4.1-8b
Granite 4.2 8B (IBM): dense Apache 2.0, 128k context, ~4.6 GB Q4 VRAM. Multilingual chat, coding, and reasoning for the enterprise.
ollama pull granite4.2
SFT “thinking” fine-tune of OLMo 3 7B: step-by-step reasoning, 16k context, ~4.2 GB VRAM in Q4. 100% open, Apache 2.0 license.
# HuggingFace : zimplex/olmo3-7b-think-sft-eosfix-16k-3ep-euc
GLM 5.3 (Zhipu): dense 7B specialized in code and reasoning, 128k context, ~4.1 GB VRAM in Q4. Lightweight, runs on a 6–8 GB GPU, MIT license.
ollama pull glm-5.3
Dense 7B 100% open (weights + data + code). Complete transparency for research.
ollama run olmo-3:7b
Hybrid thinking/fast mode. 119 languages, 32k native (131k via YaRN).
ollama run qwen3:8b
| Rank | Model | Params | Q4 VRAM | Context | License | On Apple M2 (16 GB) |
|---|---|---|---|---|---|---|
| #1 | Gemma 4 E4B | 4B | 10 GB | 128 000 | Apache 2.0 | 14 tok/s · Q4_K_M |
| #2 | Granite 4.1 8B Instruct | 8B | 5 GB | 131 072 | Apache 2.0 | 12 tok/s · Q8 |
| #3 | Granite 4.2 8B | 8B | 4.6 GB | 128 000 | Apache 2.0 | 32 tok/s · Q8 |
| #4 | OLMo 3 7B Think (SFT) | 7B | 4.2 GB | 16 000 | Apache 2.0 | 32 tok/s · Q8 |
| #5 | GLM 5.3 7B | 7B | 4.1 GB | 128 000 | MIT | 32 tok/s · Q8 |
| #6 | OLMo 3 7B | 7B | 5 GB | 8 192 | Apache 2.0 | 12 tok/s · Q8 |
| #7 | Qwen 3 8B | 8B | 5 GB | 131 072 | Apache 2.0 | 12 tok/s · Q8 |
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.
Free memo
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 →No spam. Unsubscribe in 1 click. Your data stays with us (never resold).
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 |
-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.
Filter: 1–13B models whose Q4_K_M fits under 12 GB. Bonus: 3–8B (peak Air M3) and small active MoE models. Bonus for permissive licenses for MLX (simpler conversion).
Criteria considered:
The scoring is fully transparent: see our methodology for details on VRAM/tokens/sec calculations.
MacBook Air M3 8 GB: is it workable?
Barely: Llama 3.2 3B Q4 (~2 GB) or Phi-4 Mini 3.8B Q4 (~2.3 GB)—30–40 tokens/sec. macOS already uses 4 GB, leaving little room for context. For a truly minimal setup, prefer 16 GB.
MacBook Air M3 16 GB: which models?
Mistral 7B Q4 (~4.5 GB), Qwen 3 8B Q4 (~5 GB), Gemma 4 9B Q4 (~5.5 GB) — 20-28 tokens/sec. Ideal for chat, light coding, and personal RAG. See the MacBook Air M3 guide.
Air M3 vs Air M4: difference for LLMs?
M4 is ~15-20% faster at equivalent memory bandwidth (with an enhanced Neural Engine). For LLMs, the difference remains minor: Mistral 7B Q4 = 25 tok/s on M3 vs 30 tok/s on M4. Not a necessary upgrade.
Which French model on a MacBook Air M3?
Mistral 7B Instruct or Mistral Nemo 12B Q4 (on 24 GB) are the best in French. Lucie 7B (CNRS) is the 100% sovereign option but limited to a 4k context. See FR ranking.
Prices in euros (€) are French market prices including VAT, as checked by BestLLMfor. US prices differ: the Amazon buttons show the current US price.