Best local LLM for mac 24gb
Last updated 2026-05-26 · Page updated 2026-07-13
Top 8 open-source picks for mac 24gb, ranked by benchmark performance and real-world fit. Updated monthly.
Granite 4.0 H-Tiny 7B-A1B
IBM's edge-class hybrid MoE with 7B total and only 1B active parameters — Apache 2.0 licensed and built for embedded and low-cost serving.
Qwen 3 14B
A 14B dense model from Alibaba that matches Qwen 2.5 32B Base on STEM and code, with the same hybrid thinking system as the rest of the Qwen 3 family. The pragmatic sweet spot for a single 24GB GPU.
Phi-4 Reasoning 14B
Microsoft's 14B reasoner that beats R1-Distill-Llama-70B on AIME and GPQA with 50x fewer parameters. MIT-licensed, English-first, with a 32K context.
DeepSeek R1 Distill Qwen 14B
DeepSeek's R1 reasoning distilled into Qwen 14B under MIT. AIME24 69.7 and MATH-500 93.9 — beats o1-mini on most reasoning benchmarks.
gpt-oss 20B
OpenAI's compact open-weight MoE with 3.6B active out of 21B total parameters. Matches o3-mini on a laptop-class GPU under Apache 2.0.
ERNIE 4.5 21B-A3B Thinking
Baidu's compact reasoning MoE with 3B active parameters out of 21B total. Fast inference thanks to the small active set, with Chinese-language strength.
Trinity Mini 26B-A3B
Arcee AI's US-built MoE with 3B active parameters out of 26B total. Apache-licensed, fast in practice, and tuned for agent-style workloads.
OLMoE 1B-7B Instruct
Allen AI's OLMoE is the only MoE released with weights, training data, and code fully open — 7B total with 1.3B active, matching Llama2-13B-Chat quality.
Which GPU should you buy to run Granite 4.0 H-Tiny 7B-A1B?
To run Granite 4.0 H-Tiny 7B-A1B locally at Q4, you need ~4 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 24gb?
Granite 4.0 H-Tiny 7B-A1B tops this ranking — a 7B model, licensed under Apache 2.0, needing about 4 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 Granite 4.0 H-Tiny 7B-A1B?
At Q4 quantization, Granite 4.0 H-Tiny 7B-A1B needs about 4 GB of VRAM and fits comfortably on a single 24 GB GPU.
Which of these models fit an 8 GB GPU?
At Q4 quantization, Granite 4.0 H-Tiny 7B-A1B, OLMoE 1B-7B Instruct fit within 8 GB of VRAM.
Are the models on this mac 24gb 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 4k to 128k tokens, depending on the model.