Ranking updated on 09/10/2026
The MacBook Air M4 (16 / 24 / 32 GB of unified memory, 120 GB/s) runs 3-9B LLMs very well in Q4_K_M via Ollama or MLX. The lack of a fan limits long sessions, so efficient models (low active MoE) and tighter quantization are preferred.
MacBook Air M4 : purchasing alternative available for local AI — MacBook Pro M5 Pro — 24 GB / 1 TB :
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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 M4 (24 GB) |
|---|---|---|---|---|---|---|
| #1 | Gemma 4 E4B | 4B | 10 GB | 128 000 | Apache 2.0 | 14 tok/s · FP16 |
| #2 | Granite 4.1 8B Instruct | 8B | 5 GB | 131 072 | Apache 2.0 | 12 tok/s · FP16 |
| #3 | Granite 4.2 8B | 8B | 4.6 GB | 128 000 | Apache 2.0 | 32 tok/s · FP16 |
| #4 | OLMo 3 7B Think (SFT) | 7B | 4.2 GB | 16 000 | Apache 2.0 | 32 tok/s · FP16 |
| #5 | GLM 5.3 7B | 7B | 4.1 GB | 128 000 | MIT | 32 tok/s · FP16 |
| #6 | OLMo 3 7B | 7B | 5 GB | 8 192 | Apache 2.0 | 12 tok/s · FP16 |
| #7 | Qwen 3 8B | 8B | 5 GB | 131 072 | Apache 2.0 | 12 tok/s · FP16 |
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: 1–15B models whose Q4_K_M version fits under 14 GB (leaving 10+ GB for macOS). Bonus: 3–9B (ideal without thermal throttling) and small MoE models with active parameters (Qwen 3 30B-A3B runs on a 32 GB Air).
Criteria considered:
The scoring is fully transparent: see our methodology for details on VRAM/tokens/sec calculations.
MacBook Air M4 16 GB: which LLM should you choose?
Mistral 7B Q4 (~4.5 GB) or Qwen 3 8B Q4 (~5 GB) are the sweet spot—25–35 tokens/sec, smooth for chat. Avoid Gemma 4 27B even in Q3: too marginal without a fan. See the MacBook Air M4 guide.
Air M4 24 / 32 GB: can you run 13B?
Yes: Mistral Nemo 12B Q4 (~7 GB) or Qwen 3 14B Q4 (~8 GB) run at 15–22 tokens/sec. With 32 GB, you can test Qwen 3 30B A3B (MoE, 3 GB active in VRAM)—surprisingly good on Air.
MLX or Ollama on a MacBook Air M4?
Ollama starts in 30 seconds. MLX delivers 15-25% more tok/s but requires converting the weights. For an Air (battery life + simplicity), Ollama remains the default choice. LM Studio combines both in a UI.
Does the Air M4 heat up when running a 7B LLM?
Yes, after 5–10 minutes of continuous generation: it throttles by ~10–15%. For occasional chat, this is unnoticeable. For batch RAG, switch to a MacBook Pro (with a fan) or Mac mini. Also see M4 MacBook Pro.
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