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Can the NVIDIA GeForce RTX 4090 run Qwen3.8 Max (2.4T-A95B)?

9 ptsWon't run (F) · 9 of 100 points: Model quality 9.3/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15

No, it doesn’t fit

No. Qwen3.8 Max (2.4T-A95B) needs about 981 GB even at Q2_K; the NVIDIA GeForce RTX 4090 has 24.0 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)

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

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

Links checked 7 days ago against Hugging Face, Ollama and GitHub.

How the F grade is scored

Every result gets up to 100 points from four parts. The points decide the letter grade.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.9.3 / 45
Memory fitFull marks when everything sits in GPU or unified memory; fewer when part spills to system RAM.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
Total9.3 / 1009 ptsWon't run (F) · 9 of 100 points: Model quality 9.3/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15

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

Grade scale: S 85+ · A 72+ · B 58+ · C 42+ · D 25+ · F under 25 points.

Every quantization on the NVIDIA GeForce RTX 4090

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 981 GB 24.0 GB — Won't run 9 ptsWon't run (F) · 9 of 100 points: Model quality 9.3/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 GGUF (IQ2_XXS) · folder (opens in a new tab)
Q3_K_M 1,201 GB 24.0 GB — Won't run 11 ptsWon't run (F) · 11 of 100 points: Model quality 11.1/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 GGUF (IQ3_XXS) · folder (opens in a new tab)
Q4_K_M 1,495 GB 24.0 GB — Won't run 12 ptsWon't run (F) · 12 of 100 points: Model quality 12.6/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 GGUF files (opens in a new tab)
Q5_K_M 1,739 GB 24.0 GB — Won't run 12 ptsWon't run (F) · 12 of 100 points: Model quality 12.9/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 GGUF files (opens in a new tab)
Q6_K 2,008 GB 24.0 GB — Won't run 13 ptsWon't run (F) · 13 of 100 points: Model quality 13.1/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 GGUF files (opens in a new tab)
Q8_0 2,596 GB 24.0 GB — Won't run 13 ptsWon't run (F) · 13 of 100 points: Model quality 13.3/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 GGUF · folder (opens in a new tab)
F16 4,895 GB 24.0 GB — Won't run 13 ptsWon't run (F) · 13 of 100 points: Model quality 13.3/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 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

No consumer GPU or Mac in our catalog runs this model on its own.

Models that do run on the NVIDIA GeForce RTX 4090

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.