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Can the NVIDIA GeForce RTX 5060 Ti 8GB run Qwen2.5 0.5B Instruct?

50 ptsUsable (C) · 50 of 100 points: Model quality 4.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 6.0/15

Yes, it runs well

Runs well. At F16 it needs about 2.1 GB of 8.0 GB usable, with an estimated speed of 263 tok/s.

Best quant
F16
Generation speed
263 tok/s
Prompt processing
34,824 tok/s
First token (1K prompt)
0.3 s
Largest safe context
32K

Download Qwen2.5 0.5B Instruct

Recommended for the NVIDIA GeForce RTX 5060 Ti 8GB: 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 qwen2.5:0.5b-instruct-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/Qwen/Qwen2.5-0.5B-Instruct-GGUF (opens in a new tab)

ollama run hf.co/Qwen/Qwen2.5-0.5B-Instruct-GGUF:FP16

Original weights · Hugging Face

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

huggingface.co/Qwen/Qwen2.5-0.5B-Instruct (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 6 days ago against Hugging Face, Ollama and GitHub.

How the C 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.4.5 / 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.6.0 / 15
Total50.5 / 10050 ptsUsable (C) · 50 of 100 points: Model quality 4.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 6.0/15

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

Every quantization on the NVIDIA GeForce RTX 5060 Ti 8GB

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 1.3 GB 8.0 GB 1,843 tok/s Runs well 46 ptsUsable (C) · 46 of 100 points: Model quality 3.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 3.7/15 GGUF (opens in a new tab)
Q3_K_M 1.3 GB 8.0 GB 1,451 tok/s Runs well 47 ptsUsable (C) · 47 of 100 points: Model quality 3.8/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 3.9/15 GGUF (opens in a new tab)
Q4_K_M 1.4 GB 8.0 GB 1,122 tok/s Runs well 48 ptsUsable (C) · 48 of 100 points: Model quality 4.3/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 4.0/15 GGUF (opens in a new tab)
Q5_K_M 1.4 GB 8.0 GB 927 tok/s Runs well 48 ptsUsable (C) · 48 of 100 points: Model quality 4.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 4.2/15 GGUF (opens in a new tab)
Q6_K 1.5 GB 8.0 GB 779 tok/s Runs well 48 ptsUsable (C) · 48 of 100 points: Model quality 4.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 4.3/15 GGUF (opens in a new tab)
Q8_0 1.6 GB 8.0 GB 571 tok/s Runs well 49 ptsUsable (C) · 49 of 100 points: Model quality 4.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 4.7/15 GGUF (opens in a new tab)
F16 2.1 GB 8.0 GB 263 tok/s Runs well 50 ptsUsable (C) · 50 of 100 points: Model quality 4.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 6.0/15 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.