Best local LLM for mac 8gb
Last updated 2026-05-26 · Page updated 2026-07-13
Top 7 open-source picks for mac 8gb, ranked by benchmark performance and real-world fit. Updated monthly.
Pleias-RAG 1B
A 1.2B RAG-specialized model from PleIAs with built-in citation and grounding behavior. Beats most sub-4B small language models on HotPotQA.
DeepSeek R1 Distill Qwen 1.5B
DeepSeek's R1 reasoning distilled into a 1.5B MIT-licensed model with visible chain-of-thought. Hits MATH-500 83.9 and runs on any laptop.
CroissantLLM 1.3B
A 1.3B bilingual French/English model from Sorbonne's MLIA lab, light enough to run on a CPU and shipped with a fully auditable training corpus.
SmolLM2 1.7B Instruct
HuggingFace's 1.7B Apache 2.0 instruct model trained on 11T tokens. Beats Qwen2.5-1.5B by roughly 6 points on MMLU-Pro, making it a top pick at the sub-2B tier.
Qwen 2.5 Coder 1.5B Instruct
Alibaba's smallest Qwen 2.5 Coder at 1.5B parameters under Apache 2.0, covering 92 programming languages. HumanEval 70.7 makes it a serious on-device completion model.
SmolVLM2 2.2B Instruct
HuggingFace's 2.2B vision-language model built on SmolLM2-1.7B, handling image, video, and text in roughly 5.2GB of VRAM. The smallest serious VLM with video understanding.
Granite 4.0 3B Vision
IBM's 3B vision-language model purpose-built for enterprise document extraction, including OCR, table parsing, and form understanding. Apache 2.0 and laptop-deployable.
Which GPU should you buy to run Pleias-RAG 1B?
To run Pleias-RAG 1B locally at Q4, you need ~0.8 GB of VRAM. The best value for this is a RTX 5060 (8 GB VRAM).
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Frequently asked questions
What is the best local LLM for mac 8gb?
Pleias-RAG 1B tops this ranking — a 1.2B model, licensed under Apache 2.0, needing about 0.8 GB of VRAM at Q4 quantization. See the full list below for the runner-ups and how they compare.
How much VRAM do I need to run Pleias-RAG 1B?
At Q4 quantization, Pleias-RAG 1B needs about 0.8 GB of VRAM and fits comfortably on a single 24 GB GPU.
Which of these models fit an 8 GB GPU?
At Q4 quantization, Pleias-RAG 1B, DeepSeek R1 Distill Qwen 1.5B, CroissantLLM 1.3B, SmolLM2 1.7B Instruct, Qwen 2.5 Coder 1.5B Instruct and 2 more fit within 8 GB of VRAM.
Are the models on this mac 8gb list free for commercial use?
Licenses across this list include Apache 2.0, MIT. Check the specific license of each model on its catalog page before deploying commercially, as terms vary by author.
What context window do these models support?
Context windows on this list range from 2k to 128k tokens, depending on the model.