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Can the NVIDIA GeForce RTX 5080 run Qwen3 4B Instruct 2507?

79 ptsGreat (A) · 79 of 100 points: Model quality 24.3/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15

Yes, it runs well

Runs well. At F16 it needs about 10.6 GB of 16.0 GB usable, with an estimated speed of 68.8 tok/s.

Best quant
F16
Generation speed
68.8 tok/s
Prompt processing
10,084 tok/s
First token (1K prompt)
0.3 s
Largest safe context
43K

Download Qwen3 4B Instruct 2507

Recommended for the NVIDIA GeForce RTX 5080: F16. 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 qwen3:4b-instruct-2507-fp16

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 F16 file (opens in a new tab)

huggingface.co/unsloth/Qwen3-4B-Instruct-2507-GGUF (opens in a new tab)

ollama run hf.co/unsloth/Qwen3-4B-Instruct-2507-GGUF:F16

Original weights · Hugging Face

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

huggingface.co/Qwen/Qwen3-4B-Instruct-2507 (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

Qwen’s main repo, renamed from Qwen2.5 to Qwen3; it still documents Qwen2.5.

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

How the A 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.24.3 / 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
Total79.3 / 10079 ptsGreat (A) · 79 of 100 points: Model quality 24.3/45, 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 5080

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 4.2 GB 16.0 GB 481 tok/s Runs well 63 ptsGood (B) · 63 of 100 points: Model quality 17.0/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 6.1/15 GGUF (opens in a new tab)
Q3_K_M 4.6 GB 16.0 GB 379 tok/s Runs well 67 ptsGood (B) · 67 of 100 points: Model quality 20.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 6.6/15 GGUF (opens in a new tab)
Q4_K_M 5.1 GB 16.0 GB 293 tok/s Runs well 70 ptsGood (B) · 70 of 100 points: Model quality 23.1/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 7.3/15 GGUF (opens in a new tab)
Q5_K_M 5.5 GB 16.0 GB 242 tok/s Runs well 71 ptsGood (B) · 71 of 100 points: Model quality 23.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 7.9/15 GGUF (opens in a new tab)
Q6_K 5.9 GB 16.0 GB 203 tok/s Runs well 72 ptsGreat (A) · 72 of 100 points: Model quality 24.1/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 8.5/15 GGUF (opens in a new tab)
Q8_0 6.9 GB 16.0 GB 149 tok/s Runs well 74 ptsGreat (A) · 74 of 100 points: Model quality 24.3/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 9.9/15 GGUF (opens in a new tab)
F16 10.6 GB 16.0 GB 68.8 tok/s Runs well 79 ptsGreat (A) · 79 of 100 points: Model quality 24.3/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (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.