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Can the Apple M3 Ultra (256 GB) run Qwen3.8 Max (2.4T-A95B)?

68Good quality (B) · 68 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 9/100 (quality, fit, speed and memory use) sets the ranking.

No, it doesn’t fit

No. Qwen3.8 Max (2.4T-A95B) needs about 995 GB even at Q2_K; the Apple M3 Ultra (256 GB) has 192 GB usable.

Download Qwen3.8 Max (2.4T-A95B)

Recommended: Q2_K. Every other quantization has its own download link in the table below.

Quantized files · GGUF

Works in LM Studio, llama.cpp, Jan and Ollama. No sign-in needed.

Download the Q2_K (IQ2_XXS) files (opens in a new tab)

huggingface.co/unsloth/Qwen3.8-2.4T-A95B-GGUF (opens in a new tab)

ollama run hf.co/unsloth/Qwen3.8-2.4T-A95B-GGUF

Original weights · Hugging Face

Full-precision files from Alibaba, for fine-tuning, vLLM, Transformers.

huggingface.co/Qwen/Qwen3.8-2.4T-A95B (opens in a new tab)

No sign-in needed.

Official repo · GitHub

Reference code, inference scripts and docs from Alibaba. The weights themselves download from Hugging Face.

github.com/QwenLM/Qwen3.8 (opens in a new tab)

Qwen’s current repo: Qwen3.5 and Qwen3.6 redirect here.

git clone https://github.com/QwenLM/Qwen3.8

Links checked 3 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 68Good quality (B) · 68 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 9/100 (quality, fit, speed and memory use) sets the ranking.
How capable the model itself is at Q2_K, from its editorial quality tier scaled by the quantization or precision. It’s the same on any hardware that runs this variant. Scale: A+ 85+ · A 70+ · B 55+ · C 40+ · D under 40.
Runs Won't run
How well it runs on the Apple M3 Ultra (256 GB): speed, capped by how well it fits in memory. Runs great: 30+ tokens/s (or near the image, video or decision speed target) · Runs well: 10+ tokens/s · Slow: 3+ tokens/s · Very slow: under 3 · Won’t run: doesn’t fit even with offload. A tight fit or RAM offload caps it at Runs well; heavy offload caps it at Slow.

Match score

To order lists, each result also gets a match score out of 100 from four parts. It favors the most capable model that still runs well.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.9.3 / 45
Memory fitFull marks when the model fits in the share of unified memory the GPU can use; fewer when it goes past that limit.0.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.0.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.0.0 / 15
Match score9.3 / 100

The match score orders results; it is not shown as a grade. Quality and Runs are the two ratings in the tables.

It doesn’t fit, so only a reduced share of the quality points counts and fit, speed and memory use score zero.

Every quantization on the Apple M3 Ultra (256 GB)

Quant Memory needed Available Speed Runs Quality Download
Q2_K 995 GB 192 GB — Won't run 68Good quality (B) · 68 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 9/100 (quality, fit, speed and memory use) sets the ranking. GGUF (IQ2_XXS) · folder (opens in a new tab)
Q3_K_M 1,215 GB 192 GB — Won't run 82Great quality (A) · 82 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 11/100 (quality, fit, speed and memory use) sets the ranking. GGUF (IQ3_XXS) · folder (opens in a new tab)
Q4_K_M 1,509 GB 192 GB — Won't run 93Excellent quality (A+) · 93 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 12/100 (quality, fit, speed and memory use) sets the ranking. GGUF files (opens in a new tab)
Q5_K_M 1,754 GB 192 GB — Won't run 95Excellent quality (A+) · 95 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 12/100 (quality, fit, speed and memory use) sets the ranking. GGUF files (opens in a new tab)
Q6_K 2,023 GB 192 GB — Won't run 97Excellent quality (A+) · 97 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 13/100 (quality, fit, speed and memory use) sets the ranking. GGUF files (opens in a new tab)
Q8_0 2,610 GB 192 GB — Won't run 98Excellent quality (A+) · 98 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 13/100 (quality, fit, speed and memory use) sets the ranking. GGUF · folder (opens in a new tab)
F16 4,909 GB 192 GB — Won't run 98Excellent quality (A+) · 98 of 100: how capable the model is at F16, whatever it runs on. Match score 13/100 (quality, fit, speed and memory use) sets the ranking. GGUF · folder (opens in a new tab) Original weights (opens in a new tab)

Memory figures assume 8K of context. Longer conversations or documents grow the KV cache.

Hardware that can run it

None. No single GPU or device in our catalog runs this model on its own; it needs several GPUs working together.

Models that do run on the Apple M3 Ultra (256 GB)

Quick answers

How much memory does Qwen3.8 Max (2.4T-A95B) need on the M3 Ultra 256GB?

Even at Q2_K it needs about 995 GB; the M3 Ultra 256GB has 192 GB usable.

What can the M3 Ultra 256GB run instead?

Qwen3.8 Flash Next (125B-A6B), Nemotron 3.5 Super VL 120B-A12B and Step 3.7 Flash (198B-A11B) run best on it; the page lists more, each with its speed.

What hardware can run Qwen3.8 Max (2.4T-A95B)?

No single GPU or device in our catalog runs it on its own; it needs several GPUs working together.

Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.

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