Ranking updated on 09/10/2026
The MacBook Air M1 (8 / 16 GB, 68 GB/s) dates back to 2020 but can still run 3–7B LLMs reasonably well. Limited bandwidth → stick with efficient models.
MacBook Air M1: 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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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 3B Apache 2.0, 12 languages including FR, 131k ctx, GQA 40Q/8KV. Tool calling and code FIM. Released April 29, 2026.
ollama run granite4.1:3b
Granite 4.1 (3B Apache 2.0): generic Ollama tag from the IBM Granite 4.1 family, 128k ctx, tool calling, and code. Released May 2026.
ollama run granite4.1
Dense 7B 100% open (weights + data + code). Complete transparency for research.
ollama run olmo-3:7b
3B VLM specialized in enterprise document extraction. OCR, tables, forms.
# HuggingFace : ibm-granite/granite-4.0-3b-vision
3B dual-mode (think/no-think). 6 languages. MMLU 59.7, GSM8K 70.9. Fully open (data + recipe).
# HuggingFace : HuggingFaceTB/SmolLM3-3B
| Rank | Model | Params | Q4 VRAM | Context | License | On Apple M1 (16 GB) |
|---|---|---|---|---|---|---|
| #1 | OLMo 3 7B Think (SFT) | 7B | 4.2 GB | 16 000 | Apache 2.0 | 32 tok/s · Q8 |
| #2 | GLM 5.3 7B | 7B | 4.1 GB | 128 000 | MIT | 32 tok/s · Q8 |
| #3 | Granite 4.1 3B Instruct | 3B | 2 GB | 131 072 | Apache 2.0 | 25 tok/s · FP16 |
| #4 | Granite 4.1 | 3B | 1.7 GB | 128 000 | Apache 2.0 | 50 tok/s · FP16 |
| #5 | OLMo 3 7B | 7B | 5 GB | 8 192 | Apache 2.0 | 12 tok/s · Q8 |
| #6 | Granite 4.0 3B Vision | 3B | 2.2 GB | 16 384 | Apache 2.0 | 25 tok/s · FP16 |
| #7 | SmolLM3 3B | 3B | 2 GB | 128 000 | Apache 2.0 | 25 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: 1–9B, with Q4_K_M fitting under 9 GB. Bonus for 3–7B (peak M1) and strong bonus for ≤ 3B (M1 does not have the M3/M4 Neural Engine).
Criteria considered:
The scoring is fully transparent: see our methodology for details on VRAM/tokens/sec calculations.
MacBook Air M1 in 2026: still usable for LLMs?
Yes, but limited. Mistral 7B Q4 runs at ~14 tok/s, Llama 3.2 3B Q4 at ~25 tok/s. Fine for smooth chat. For sustained coding, expect it to take time. See the MacBook Air M1 guide.
M1 Air 8 GB: does it really work?
Just enough with Phi-4 Mini 3.8B Q4 or Gemma 4 4B Q4 (~2.5 GB). macOS takes 4 GB, leaving 2 GB for the model plus a short context. Prefer 16 GB.
Which quantization on M1?
Q4_K_M remains the sweet spot. Q5_K_M delivers better quality but costs 25% more memory bandwidth → tokens/sec divided by ~1.3. It’s noticeable on M1. Avoid Q3 (noticeable quality degradation).
M1 vs. M2 on Mistral 7B?
M1 ≈ 14 tok/s vs. M2 ≈ 22 tok/s. The difference comes mainly from bandwidth (68 vs. 100 GB/s). No upgrade is needed if the 16 GB M1 is sufficient for you.
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