Qwen3.8 27B on NVIDIA GeForce RTX 4090
Can the NVIDIA GeForce RTX 4090 run Qwen3.8 27B?
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_MQuantized 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_MOriginal weights · Hugging Face
Full-precision files from Alibaba, for fine-tuning, vLLM, Transformers.
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/Qwen3.8How the S grade is scored
Every result gets up to 100 points from four parts. The points decide the letter grade.
| Model quality | 40.9 / 45 | |
|---|---|---|
| Memory fit | 25.0 / 25 | |
| Speed | 15.0 / 15 | |
| Memory use | 15.0 / 15 | |
| Total | 95.9 / 100 | 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 |
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
- 97 ptsExcellent (S) · 97 of 100 points: Model quality 42.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 5090
- 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 NVIDIA GeForce RTX 3090
- 95 ptsExcellent (S) · 95 of 100 points: Model quality 40.9/45, Memory fit 25.0/25, Speed 14.3/15, Memory use 15.0/15 AMD Radeon RX 7900 XTX
- 91 ptsExcellent (S) · 91 of 100 points: Model quality 43.0/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 8.5/15 NVIDIA RTX PRO 6000 Blackwell
- 90 ptsExcellent (S) · 90 of 100 points: Model quality 36.1/45, Memory fit 24.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 4080 SUPER
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.