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Can the NVIDIA GeForce RTX 4090 run Qwen3.8 27B?

95 ptsExcellent (S) · 95 of 100 points: Model quality 40.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15

Yes, it runs well

Runs well. At Q4_K_M it needs about 19.3 GB of 24.0 GB usable, with an estimated speed of 44.5 tok/s.

Best quant
Q4_K_M
Generation speed
44.5 tok/s
Prompt processing
2,141 tok/s
First token (1K prompt)
0.5 s
Largest safe context
78K

Download Qwen3.8 27B

Recommended for the NVIDIA GeForce RTX 4090: Q4_K_M. Every other quantization has its own download link in the table below.

Quickest · Ollama

One command downloads a ready-to-run version and starts a chat.

ollama run qwen3.8:27b-q4_K_M

Get Ollama at ollama.com/download (opens in a new tab) · model page (opens in a new tab)

Quantized files · GGUF

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

Download the Q4_K_M file (opens in a new tab)

huggingface.co/unsloth/Qwen3.8-27B-GGUF (opens in a new tab)

ollama run hf.co/unsloth/Qwen3.8-27B-GGUF:Q4_K_M

Original weights · Hugging Face

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

huggingface.co/Qwen/Qwen3.8-27B (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.

How the S 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.40.9 / 45
Memory fitFull marks when everything sits in GPU or unified memory; fewer when part spills to system RAM.25.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.15.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.15.0 / 15
Total95.9 / 10095 ptsExcellent (S) · 95 of 100 points: Model quality 40.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15

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 13.4 GB 24.0 GB 73.2 tok/s Runs well 83 ptsGreat (A) · 83 of 100 points: Model quality 30.1/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 12.9/15 GGUF (UD-Q2_K_XL) (opens in a new tab)
Q3_K_M 15.9 GB 24.0 GB 57.6 tok/s Runs well 91 ptsExcellent (S) · 91 of 100 points: Model quality 36.1/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (UD-Q3_K_XL) (opens in a new tab)
Q4_K_M 19.3 GB 24.0 GB 44.5 tok/s Runs well 95 ptsExcellent (S) · 95 of 100 points: Model quality 40.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q5_K_M 22.1 GB 24.0 GB 36.8 tok/s Tight fit 95 ptsExcellent (S) · 95 of 100 points: Model quality 41.7/45, Memory fit 24.0/25, Speed 14.4/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q6_K 25.1 GB 24.0 GB 18.3 tok/s Runs with offload 77 ptsGreat (A) · 77 of 100 points: Model quality 42.6/45, Memory fit 10.0/25, Speed 10.1/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q8_0 31.8 GB 24.0 GB 4.8 tok/s Runs with offload 73 ptsGreat (A) · 73 of 100 points: Model quality 43.0/45, Memory fit 10.0/25, Speed 5.2/15, Memory use 15.0/15 GGUF (opens in a new tab)
F16 57.9 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 · 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.

Other hardware that runs it

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.