What is the best GPU for running Qwen2.5 0.5B locally?

The cheapest hardware in Canada that runs Qwen2.5 0.5B fully in memory at 128k context is the NVIDIA GeForce RTX 3060 12GB, $497 at Amazon, at about 104 tok/s (estimated). The best value is the NVIDIA GeForce RTX 3080 10GB (about 220 tok/s for $769). The fastest we price is the NVIDIA RTX PRO 6000 Blackwell, at about 519 tok/s.

Can I run Qwen2.5 0.5B on a 16 GB GPU?

Yes: at Q8_0 it fits fully in 16 GB at 128k context. On the NVIDIA GeForce RTX 5080 it runs at about 278 tok/s (estimated).

Can I run Qwen2.5 0.5B on a 24 GB GPU?

Yes: at Q8_0 it fits fully in 24 GB at 128k context. On the NVIDIA GeForce RTX 3090 Ti it runs at about 292 tok/s (estimated).

How do I run Qwen2.5 0.5B locally?

With Ollama: ollama run hf.co/bartowski/Qwen2.5-0.5B-Instruct-GGUF:Q8_0 (the Q8_0 build). LM Studio and llama.cpp commands are on this page.

What licence is Qwen2.5 0.5B released under?

Apache License 2.0, which allows commercial use.

Qwen2.5 0.5B

494M parameters · 32k context · Apache License 2.0 · released 2024-09-16

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In short

The cheapest hardware in Canada that runs Qwen2.5 0.5B fully in memory at 128k context is the NVIDIA GeForce RTX 3060 12GB, $497 at Amazon, at about 104 tok/s (estimated).

Updated October 15, 2026. Speeds are estimates; how they are worked out.

What to run it on in Canada

Fully in memory at 128k context, priced at retailers today.

Cheapest
NVIDIA GeForce RTX 3060 12GB
104tok/sest.Q8_0
$497
Best value
NVIDIA GeForce RTX 3080 10GB
220tok/sest.Q8_0
$769
Fastest
NVIDIA RTX PRO 6000 Blackwell
519tok/sest.Q8_0
$3,133
Mac
Mac mini M4 16GB
36tok/sest.Q8_0
$1,399

All hardware that runs it

Everything we price, best value first. Two cards split the model by layers.

Hardware	Quant	Fits	Decode	Price	tok/s per 1,000
NVIDIA GeForce RTX 3060 12GB	Q8_0	fits	104 tok/s	$497	209.7
Intel Arc B580	Q8_0	fits	107 tok/s	$560	190.2
Intel Arc A770 16GB	Q8_0	fits	131 tok/s	$655	199.4
NVIDIA GeForce RTX 3080 10GB	Q8_0	fits	220 tok/s	$769	286.1
AMD Radeon RX 9060 XT 16GB	Q8_0	fits	82 tok/s	$800	102.8
AMD Radeon RX 9070 XT	Q8_0	fits	164 tok/s	$1,050	156.6
Intel Arc Pro B60	Q8_0	fits	107 tok/s	$1,060	100.4
NVIDIA GeForce RTX 5060 Ti 16GB	Q8_0	fits	130 tok/s	$1,120	115.8
NVIDIA GeForce RTX 5070	Q8_0	fits	195 tok/s	$1,181	164.7
AMD Radeon RX 7800 XT	Q8_0	fits	160 tok/s	$1,397	114.7
Mac mini M4 16GB	Q8_0	fits	36 tok/s	$1,399	26.0
NVIDIA GeForce RTX 4070 Super	Q8_0	fits	146 tok/s	$1,400	104.2
AMD Radeon RX 7600 XT	Q8_0	fits	74 tok/s	$1,440	51.4
NVIDIA GeForce RTX 5070 Ti	Q8_0	fits	259 tok/s	$1,690	153.5
NVIDIA GeForce RTX 4070 Ti Super	Q8_0	fits	195 tok/s	$1,790	108.7
NVIDIA GeForce RTX 4080 Super	Q8_0	fits	213 tok/s	$2,000	106.6
AMD Radeon RX 7900 XT	Q8_0	fits	205 tok/s	$2,083	98.6
NVIDIA GeForce RTX 5080	Q8_0	fits	278 tok/s	$2,370	117.3
NVIDIA GeForce RTX 3090	Q8_0	fits	271 tok/s	$2,800	96.8
AMD Radeon AI PRO R9700	Q8_0	fits	164 tok/s	$2,800	58.7
NVIDIA RTX PRO 6000 Blackwell	Q8_0	fits	519 tok/s	$3,133	165.6
NVIDIA GeForce RTX 3090 Ti	Q8_0	fits	292 tok/s	$3,999	73.0
GMKtec EVO-X2 Ryzen AI Max+ 395 128GB	Q8_0	fits	66 tok/s	$5,099	12.9
NVIDIA GeForce RTX 4090	Q8_0	fits	292 tok/s	$7,107	41.1
ASUS Ascent GX10 128GB	Q8_0	fits	79 tok/s	$8,299	9.5
NVIDIA GeForce RTX 5090	Q8_0	fits	519 tok/s	$10,300	50.4
Mac Studio M4 Max 128GB	Q8_0	fits	166 tok/s	–	–
Mac mini M4 Pro 64GB	Q8_0	fits	83 tok/s	–	–
AMD Radeon RX 7900 XTX	Q8_0	fits	247 tok/s	–	–
Framework Desktop Ryzen AI Max+ 395 128GB	Q8_0	fits	66 tok/s	–	–
Mac Studio M3 Ultra 512GB	Q8_0	fits	249 tok/s	–	–
NVIDIA DGX Spark 128GB	Q8_0	fits	79 tok/s	–	–
Mac Studio M3 Ultra 256GB	Q8_0	fits	249 tok/s	–	–
Mac mini M4 Pro 48GB	Q8_0	fits	83 tok/s	–	–
NVIDIA GeForce RTX 4060 Ti 16GB	Q8_0	fits	83 tok/s	–	–
Mac Studio M4 Max 64GB	Q8_0	fits	166 tok/s	–	–
Mac Studio M3 Ultra 96GB	Q8_0	fits	249 tok/s	–	–
Framework Desktop Ryzen AI Max+ 395 64GB	Q8_0	fits	66 tok/s	–	–
Mac mini M4 32GB	Q8_0	fits	36 tok/s	–	–
NVIDIA Tesla P40	Q8_0	fits	100 tok/s	–	–

Memory needed

Weights, KV cache and runtime buffers at each context length (KV cache at fp16).

Quant	File	@8k	@32k
Q8_0gguf	0.5 GB	1.3 GB	1.8 GB

Run it

Copy a command for the runtime you use.

Q8_0
- Ollamaollama run hf.co/bartowski/Qwen2.5-0.5B-Instruct-GGUF:Q8_0
- LM Studiolms get bartowski/Qwen2.5-0.5B-Instruct-GGUF@q8_0
- llama.cppllama-server -hf bartowski/Qwen2.5-0.5B-Instruct-GGUF:Q8_0

Notes

The card says the full context is 32,768 tokens and generation is up to 8192 tokens. Needs transformers 4.37.0 or later, because older versions raise KeyError: 'qwen2'.

Questions

What is the best GPU for running Qwen2.5 0.5B locally?: The cheapest hardware in Canada that runs Qwen2.5 0.5B fully in memory at 128k context is the NVIDIA GeForce RTX 3060 12GB, $497 at Amazon, at about 104 tok/s (estimated). The best value is the NVIDIA GeForce RTX 3080 10GB (about 220 tok/s for $769). The fastest we price is the NVIDIA RTX PRO 6000 Blackwell, at about 519 tok/s.
Can I run Qwen2.5 0.5B on a 16 GB GPU?: Yes: at Q8_0 it fits fully in 16 GB at 128k context. On the NVIDIA GeForce RTX 5080 it runs at about 278 tok/s (estimated).
Can I run Qwen2.5 0.5B on a 24 GB GPU?: Yes: at Q8_0 it fits fully in 24 GB at 128k context. On the NVIDIA GeForce RTX 3090 Ti it runs at about 292 tok/s (estimated).
How do I run Qwen2.5 0.5B locally?: With Ollama: ollama run hf.co/bartowski/Qwen2.5-0.5B-Instruct-GGUF:Q8_0 (the Q8_0 build). LM Studio and llama.cpp commands are on this page.
What licence is Qwen2.5 0.5B released under?: Apache License 2.0, which allows commercial use.