Best LLM for Mac Mini M6 with 24GB RAM (2026)

Apple announced the M6 Mac Mini on August 26, 2026, with pre-orders open the same day and shipping starting September 22. The 24GB config pairs a 12-core CPU and 12-core GPU with 170 GB/s of unified memory, starting at $1,399. This is the value sweet spot of the M6 line: it runs 9B-14B dense models comfortably and unlocks 20B-class MoE models that barely fit on the 16GB tier.

TL;DR: The 24GB M6 Mac Mini is the local AI value king of 2026. LFM2 24B-A2B (~14GB) leads with agentic tool-calling at roughly 66 tok/s est. Gemma 4 26B-A4B (~16GB) fits tightly at roughly 38 tok/s est. GPT-OSS 20B (21B MoE, ~14GB) matches Gemma's speed with more headroom for chat, coding, and reasoning. LFM2.5 8B-A1B (~5.5GB) is the fastest pick at roughly 87 tok/s est. Qwen3.5 9B at Q8 (~11GB, roughly 19 tok/s est.) is the higher-precision small model, and Qwen3 14B (~11GB) fills the deliberate-quality slot. The 24GB tier fits models that feel a generation ahead of what a 16GB machine can do.

Estimated token generation on the Mac Mini M6 24GB. ModelFit engine estimates.

How 24GB Shapes Your Model Choices

The 24GB config steps up to 170 GB/s of memory bandwidth, but capacity is the real story. Usable model budget increases by 50% versus the 16GB config:

The Q4_K_M rule of thumb applies: roughly 0.6 GB per billion parameters. A 14B model needs about 11GB, leaving 6GB for context. A 20B-class MoE loads in ~14GB. A 27B dense model at Q4 needs about 17GB, which fits but is tight.

MoE models change the math. GPT-OSS 20B (21B total, 3.6B active) and LFM2 24B-A2B (24B total, 2B active) load at a fraction of their dense-equivalent memory cost while decoding at small-model speed.

Best Models Ranked

Speeds are ModelFit estimates with a typical error of about 10%.

Model Details

1. LFM2 24B-A2B Instruct: Best for Agents

Liquid AI's LFM2 24B-A2B Instruct has 24B total parameters and roughly 2B active per token. It loads in ~14GB and is tuned for tool calling and on-device agents. At roughly 66 tok/s est., it is the fastest model above 20B on this list.

ollama run lfm2:24b-a2b

2. Gemma 4 26B-A4B: MoE Max Fit

Gemma 4 26B-A4B is a current-generation MoE with 4B active parameters. It loads in ~16GB, right at the edge of the 24GB budget. At roughly 38 tok/s est., it brings next-gen multimodal in a tight fit. Runs best headless or near-idle.

ollama run gemma4:26b

3. GPT-OSS 20B: Best Daily Driver

GPT-OSS 20B is a MoE with 21B total parameters but only 3.6B active per token. At roughly 38 tok/s est. from ~14GB loaded, it delivers near-20B-class knowledge at interactive speed. It handles chat, coding, and reasoning tasks well.

ollama run gpt-oss:20b

4. LFM2.5 8B-A1B: Fastest Pick

Liquid AI's LFM2.5 8B-A1B has 8.3B total parameters and 1.5B active per token with a 128,000-token context, per Liquid AI's model card, which recommends it for agentic workflows, tool use and multilingual assistants rather than heavy programming. It loads in ~5.5GB, leaving most of the ~17GB budget free for context or a second model, and at roughly 87 tok/s est. it is the fastest pick on this tier.

ollama run lfm2.5:8b-a1b-q4_K_M

5. Qwen3.5 9B Instruct (Q8): Precision Small Model

Qwen3.5 9B takes images natively and carries a 262K context window (model card). The Q8_0 build loads ~10.7GB and the engine estimates roughly 19 tok/s, which leaves about 6GB of the ~17GB budget for context. It trades speed for precision: the Q4_K_M build (ollama run qwen3.5:9b, ~7GB) runs at roughly 31 tok/s est.

ollama run qwen3.5:9b-q8_0

6. Ministral 3 14B: Mistral's Multimodal 14B

Ministral 3 14B is the largest of Mistral AI's 2512 edge models, Apache 2.0: a 13.5B language model plus a 0.4B vision encoder with a 256k context window, per Mistral's model card. It loads in ~9.1GB at Q4_K_M and runs at roughly 17 tok/s est. on the M6.

ollama run ministral-3:14b

Mistral's card scores it 68.5 on WildBench against 65.1 for Qwen3 14B in non-thinking mode. It is the pick when you want image input and 14B-class text quality in one download. For translation-heavy work, Mistral Nemo 12B (mistral-nemo:12b, ~9.5GB, roughly 21 tok/s est.) is still the multilingual specialist.

7. Qwen3 14B: Deliberate Quality

Qwen3 14B at Q4_K_M loads in ~11GB and leaves 6GB for context and apps. At roughly 17 tok/s est. it is the deliberate-work option for longer writing and analysis sessions.

ollama run qwen3:14b-q4_K_M

For coding, Mellum2.1 12B-A2.5B Thinking is JetBrains' Apache 2.0 coding model, a 12B mixture of experts with 2.5B parameters active per token (model card). Its Q4_K_M file in JetBrains' official GGUF repo loads in ~8.1GB and runs at roughly 59 tok/s est. on the M6; it ranks outside the seven because this list weighs all-round use, and Ollama has no tag for it yet (llama-server -hf JetBrains/Mellum2.1-12B-A2.5B-Thinking-GGUF:Q4_K_M).

Honorable mention: Gemma 3 12B Instruct (gemma3:12b, ~9.5GB, roughly 20 tok/s est.) dropped out of the ranked list in October 2026; it stays a proven Google DeepMind fallback that fits easily on the 24GB tier.

What 24GB Can't Run

Dense models above 14B and MoE models above 26B push past the budget.

• Gemma 4 26B-A4B (16GB at Q4) - fits but leaves only ~1GB margin.
• Qwen3.8 27B (~17GB at Q4) - tight fit, needs near-headless operation.
• Qwen3.5 27B Instruct (16GB at Q4) - same heavy-fit category.
• Qwen3.6 27B (18GB at Q4) - does not fit comfortably.

If you need 27B dense models, step up to the 32GB M6 Mac Mini. For 35B-class MoE, the M5 Pro Mac Mini at 48GB is the right target.

FAQ

Can the M6 Mac Mini 24GB run a 27B model?

Barely. A 27B model at Q4_K_M needs about 17GB, which leaves minimal headroom after macOS. It loads but you will need to run the Mini nearly headless. The 32GB config is the comfortable home for 27B models.

Is 24GB enough for Ollama?

Yes, and it is the best value M6 config for Ollama in 2026. On 24GB, you can keep GPT-OSS 20B resident at ~14GB with a browser open and room for context.

Should I buy 24GB or 32GB?

If your daily use is 9B-14B dense or 20B-class MoE models, 24GB at $1,399 covers them well. If you want to run 27B models or keep dual models loaded, the 32GB config adds headroom. Bandwidth is identical on both tiers (170 GB/s), so speed does not change.

How does the M6 24GB compare to the M4 24GB?

The M6 delivers 170 GB/s versus the M4's 120 GB/s, roughly 40% more bandwidth. On GPT-OSS 20B, expect roughly 38 tok/s est. on the M6 versus roughly 24 est. on the M4.

Can the 24GB tier run a local AI server?

Yes. With GPT-OSS 20B resident, you have a capable model answering requests from any device on your network via Ollama's API server. The Mini draws about 15-30W under load, and annual power cost at 24/7 operation is roughly $15-20.