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Can the NVIDIA GeForce RTX 3090 run Qwen3 32B?

85 ptsExcellent (S) · 85 of 100 points: Model quality 32.5/45, Memory fit 24.0/25, Speed 14.0/15, Memory use 15.0/15

Yes, with little memory to spare

Tight fit. At Q4_K_M it needs about 24.0 GB of 24.0 GB usable, with an estimated speed of 35.0 tok/s.

Best quant
Q4_K_M
Generation speed
35.0 tok/s
Prompt processing
781 tok/s
First token (1K prompt)
1.3 s
Largest safe context
8K

Download Qwen3 32B

Recommended for the NVIDIA GeForce RTX 3090: 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 qwen3:32b-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/Qwen/Qwen3-32B-GGUF (opens in a new tab)

ollama run hf.co/Qwen/Qwen3-32B-GGUF:Q4_K_M

Original weights · Hugging Face

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

huggingface.co/Qwen/Qwen3-32B (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 (opens in a new tab)

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

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.32.5 / 45
Memory fitFull marks when everything sits in GPU or unified memory; fewer when part spills to system RAM.24.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.14.0 / 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
Total85.5 / 10085 ptsExcellent (S) · 85 of 100 points: Model quality 32.5/45, Memory fit 24.0/25, Speed 14.0/15, 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 3090

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 17.1 GB 24.0 GB 57.5 tok/s Runs well 78 ptsGreat (A) · 78 of 100 points: Model quality 23.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF files (opens in a new tab)
Q3_K_M 20.0 GB 24.0 GB 45.3 tok/s Runs well 83 ptsGreat (A) · 83 of 100 points: Model quality 28.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF files (opens in a new tab)
Q4_K_M 24.0 GB 24.0 GB 35.0 tok/s Tight fit 85 ptsExcellent (S) · 85 of 100 points: Model quality 32.5/45, Memory fit 24.0/25, Speed 14.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q5_K_M 27.2 GB 24.0 GB 10.3 tok/s Runs with offload 65 ptsGood (B) · 65 of 100 points: Model quality 33.2/45, Memory fit 10.0/25, Speed 7.6/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q6_K 30.8 GB 24.0 GB 5.6 tok/s Runs with offload 64 ptsGood (B) · 64 of 100 points: Model quality 33.9/45, Memory fit 10.0/25, Speed 5.6/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q8_0 38.7 GB 24.0 GB 2.7 tok/s Heavy offload 57 ptsUsable (C) · 57 of 100 points: Model quality 34.2/45, Memory fit 4.0/25, Speed 3.9/15, Memory use 15.0/15 GGUF (opens in a new tab)
F16 69.5 GB 24.0 GB — Won't run 10 ptsWon't run (F) · 10 of 100 points: Model quality 10.3/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 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.