LLMs · Alibaba

Qwen2.5 0.5B Instruct

Name: Qwen2.5 0.5B Instruct
Author: Alibaba

Parameters: 0.5B
Architecture: Dense
Max context: 32K tokens
License: apache-2.0
Released: 2024-09

How to run it

Quickest · Ollama

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

ollama run qwen2.5:0.5b

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.

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

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 5 days ago against Hugging Face, Ollama and GitHub.

Memory needed

Weights plus KV cache and runtime overhead. Leave a little extra free for the operating system and other apps.

Quantization	Quality	Weights	Total at 8K context	Total at 32K context	Download
`Q2_K`	Low	0.2 GB	0.9 GB	1.2 GB	GGUF (opens in a new tab)
`Q3_K_M`	Low	0.2 GB	0.9 GB	1.2 GB	GGUF (opens in a new tab)
`Q4_K_M`	Good	0.3 GB	1.0 GB	1.3 GB	GGUF (opens in a new tab)
`Q5_K_M`	High	0.3 GB	1.0 GB	1.4 GB	GGUF (opens in a new tab)
`Q6_K`	High	0.4 GB	1.1 GB	1.4 GB	GGUF (opens in a new tab)
`Q8_0`	Very high	0.5 GB	1.2 GB	1.5 GB	GGUF (opens in a new tab)
`F16`	Full	1.0 GB	1.7 GB	2.0 GB	GGUF (opens in a new tab) Original weights (opens in a new tab)

Best hardware for Qwen2.5 0.5B Instruct

The least expensive GPU in our catalog that runs it without offloading is the Intel Arc A380 (launch price $139) at F16.

Grade · points	Hardware	Quant	Memory	Speed	Fit
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	NVIDIA GeForce RTX 4060	`F16`	2.1 GB / 8.0 GB	160 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 5.5/15	Apple M4 (16 GB)	`F16`	2.5 GB / 10.7 GB	60.7 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 4.5/15	Apple M1 (16 GB)	`Q8_0`	2.1 GB / 10.7 GB	75.0 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 4.4/15	Intel Arc B580	`F16`	2.3 GB / 12.0 GB	205 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 4.4/15	NVIDIA GeForce RTX 3060 12GB	`F16`	2.3 GB / 12.0 GB	212 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 4.4/15	NVIDIA GeForce RTX 4070 SUPER	`F16`	2.3 GB / 12.0 GB	296 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	Apple M4 (24 GB)	`F16`	3.0 GB / 16.1 GB	60.7 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	Apple M4 Pro (24 GB)	`F16`	3.0 GB / 16.1 GB	138 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 3.6/15	AMD Radeon RX 9070 XT	`F16`	2.5 GB / 16.0 GB	324 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 3.6/15	NVIDIA GeForce RTX 4060 Ti 16GB	`F16`	2.5 GB / 16.0 GB	169 tok/s	Runs well

Check it on your own hardware

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.