Apple Silicon hardware guide

Best local LLMs for MacBook Air M2 8GB

MacBook Air M2 8GB with 8GB unified memory is a portable entry-level local AI machine. This page lists local AI models that fit its memory budget, with realistic performance expectations for LM Studio and similar runtimes.

View at Apple See 8GB RAM guide

Chip

Unified memory

8GB

Compatible models

Best pick

Qwen 3 (8B)

Quick answer

For MacBook Air M2 8GB, start with Qwen 3 (8B). Models marked “Comfortable” leave useful memory headroom; “Tight but possible” can work, but you should close other apps and prefer lower quantization.

MacBook Air · M2 · 8GB RAM · 256GB SSD · Entry Mac

Top compatible local LLMs

#1 · Tight but possible

Qwen 3 (8B)

8B · 8GB min · Q5_K_M · 5.5GB

One of the best 8B models ever made. Thinking mode + lightning fast. The new king of 8B.

chatcodestandardgeneralreasoning

#2 · Tight but possible

Gemma 4 E4B

E4B · 8GB min · Q4_K_M · 4.6GB

Gemma 4 balanced edge model with strong multimodal quality and 256K context. Great for laptops and high-end mobile devices. Apache 2.0.

chatvisionstandardmultimodalreasoning

#3 · Good

Qwen 3.5 (4B)

4B · 6GB min · Q4_K_M · 3GB

Sweet-spot small model. Surprisingly capable for its size with hybrid thinking, 256K context and strong multilingual support. Runs on 8 GB RAM. The go-to for MacBook Air M4 16 GB. Apache 2.0.

chatcodereasoningspeedgeneral

#4 · Comfortable

Phi-4 Mini (3.8B)

3.8B · 4GB min · Q5_K_M · 2.5GB

Microsoft's latest small miracle. Punches way above its weight in reasoning & code.

chatcodelightspeed

#5 · Tight but possible

Gemma 3 (4B)

4B · 8GB min · Q5_K_M · 3GB

Google's multimodal gem. Understands text AND images natively. Great quality-to-size ratio.

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#6 · Tight but possible

DeepSeek R1 Distill (8B)

8B · 8GB min · Q5_K_M · 5.5GB

DeepSeek's reasoning model distilled to 8B. Shows its thought process step-by-step. Mind-blowing for logic.

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#7 · Tight but possible

DeepSeek R1 0528 Distill (8B)

8B · 8GB min · Q4_K_M · 5GB

Updated R1 reasoning distilled to Qwen3-8B. Improved chain-of-thought with fewer hallucinations vs original R1 distills. MIT licensed.

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#8 · Tight but possible

Qwen 3.6 (6.7B)

6.7B · 8GB min · Q4_K_M · 4.5GB

Alibaba's hybrid-thinking micro-flagship. Toggles between instant answers and deep chain-of-thought reasoning on demand. 128K context, 29 languages, outperforms Qwen3-8B on reasoning benchmarks. Apache 2.0.

chatcodereasoningspeedgeneral

#9 · Good

Llama-3.1-Nemotron-Nano (4B)

4B · 6GB min · Q5_K_M · 2.8GB

⭐ Mac Mini M4 16GB top pick! NVIDIA fine-tune of Llama 3.1. Hybrid /think • /no_think mode — deep reasoning on demand, instant chat otherwise. ~80–120 tok/s on Apple Silicon Metal. 128K context. Apache 2.0.

chatlightspeedreasoning

#10 · Good

Nemotron 3 Nano (4B)

4B · 6GB min · Q5_K_M · 2.8GB

⭐ Mac Mini M4 16GB top pick! NVIDIA's hybrid model — distilled from 9B, keeps 95% of its quality. Hybrid attention + SSM layers = ~80–120 tok/s on Apple Silicon. Blazing fast, minimal RAM. NVIDIA Open Model License.

chatlightspeedreasoning

#11 · Tight but possible

Qwen 3.5 (9B)

9B · 8GB min · Q4_K_M · 6GB

The best small Qwen 3.5 for everyday use. Strong reasoning, coding and chat at 9B scale with hybrid thinking mode and 256K context. Runs on 8-16 GB RAM. Great for Mac Mini M4 Pro. Apache 2.0.

chatcodereasoninggeneral

#12 · Tight but possible

Granite 3.3 (8B Instruct)

8B · 8GB min · Q5_K_M · 4.9GB

IBM enterprise-grade 8B. Trained for RAG, tool-use and structured output. Strong function calling and long-context performance (128K). Apache 2.0 with full data provenance.

chatcodestandardgeneralreasoning

Buying note

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