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 64k context is the NVIDIA GeForce RTX 3060 12GB, $497 at Amazon, at about 167 tok/s (estimated). The best value is the NVIDIA GeForce RTX 3080 10GB (about 353 tok/s for $769). The fastest we price is the NVIDIA RTX PRO 6000 Blackwell, at about 831 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 64k context. On the NVIDIA GeForce RTX 5080 it runs at about 445 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 64k context. On the NVIDIA GeForce RTX 3090 Ti it runs at about 468 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

chat

In short

The cheapest hardware in Canada that runs Qwen2.5 0.5B fully in memory at 64k context is the NVIDIA GeForce RTX 3060 12GB, $497 at Amazon, at about 167 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 64k context, priced at retailers today.

Cheapest
NVIDIA GeForce RTX 3060 12GB
167tok/sest.Q8_0
$497
Best value
NVIDIA GeForce RTX 3080 10GB
353tok/sest.Q8_0
$769
Fastest
NVIDIA RTX PRO 6000 Blackwell
831tok/sest.Q8_0
$3,133
Mac
Mac mini M4 16GB
58tok/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	167 tok/s	$497	336.0
Intel Arc B580	Q8_0	fits	171 tok/s	$560	304.6
Intel Arc A770 16GB	Q8_0	fits	210 tok/s	$655	319.6
NVIDIA GeForce RTX 3080 10GB	Q8_0	fits	353 tok/s	$769	458.5
AMD Radeon RX 9060 XT 16GB	Q8_0	fits	132 tok/s	$800	164.6
AMD Radeon RX 9070 XT	Q8_0	fits	263 tok/s	$1,050	250.9
Intel Arc Pro B60	Q8_0	fits	171 tok/s	$1,060	160.9
NVIDIA GeForce RTX 5060 Ti 16GB	Q8_0	fits	208 tok/s	$1,120	185.5
NVIDIA GeForce RTX 5070	Q8_0	fits	312 tok/s	$1,181	264.0
AMD Radeon RX 7800 XT	Q8_0	fits	257 tok/s	$1,397	183.8
Mac mini M4 16GB	Q8_0	fits	58 tok/s	$1,399	41.7
NVIDIA GeForce RTX 4070 Super	Q8_0	fits	234 tok/s	$1,400	167.0
AMD Radeon RX 7600 XT	Q8_0	fits	119 tok/s	$1,440	82.3
NVIDIA GeForce RTX 5070 Ti	Q8_0	fits	416 tok/s	$1,690	246.0
NVIDIA GeForce RTX 4070 Ti Super	Q8_0	fits	312 tok/s	$1,790	174.2
NVIDIA GeForce RTX 4080 Super	Q8_0	fits	342 tok/s	$2,000	170.8
AMD Radeon RX 7900 XT	Q8_0	fits	329 tok/s	$2,083	158.0
NVIDIA GeForce RTX 5080	Q8_0	fits	445 tok/s	$2,370	187.9
NVIDIA GeForce RTX 3090	Q8_0	fits	434 tok/s	$2,800	155.1
AMD Radeon AI PRO R9700	Q8_0	fits	263 tok/s	$2,800	94.1
NVIDIA RTX PRO 6000 Blackwell	Q8_0	fits	831 tok/s	$3,133	265.4
NVIDIA GeForce RTX 3090 Ti	Q8_0	fits	468 tok/s	$3,999	117.0
GMKtec EVO-X2 Ryzen AI Max+ 395 128GB	Q8_0	fits	105 tok/s	$5,099	20.7
NVIDIA GeForce RTX 4090	Q8_0	fits	468 tok/s	$7,107	65.8
ASUS Ascent GX10 128GB	Q8_0	fits	127 tok/s	$8,299	15.3
NVIDIA GeForce RTX 5090	Q8_0	fits	831 tok/s	$10,300	80.7
Mac Studio M4 Max 128GB	Q8_0	fits	266 tok/s	–	–
Mac mini M4 Pro 64GB	Q8_0	fits	133 tok/s	–	–
AMD Radeon RX 7900 XTX	Q8_0	fits	395 tok/s	–	–
Framework Desktop Ryzen AI Max+ 395 128GB	Q8_0	fits	105 tok/s	–	–
Mac Studio M3 Ultra 512GB	Q8_0	fits	398 tok/s	–	–
NVIDIA DGX Spark 128GB	Q8_0	fits	127 tok/s	–	–
Mac Studio M3 Ultra 256GB	Q8_0	fits	398 tok/s	–	–
Mac mini M4 Pro 48GB	Q8_0	fits	133 tok/s	–	–
NVIDIA GeForce RTX 4060 Ti 16GB	Q8_0	fits	134 tok/s	–	–
Mac Studio M4 Max 64GB	Q8_0	fits	266 tok/s	–	–
Mac Studio M3 Ultra 96GB	Q8_0	fits	398 tok/s	–	–
Framework Desktop Ryzen AI Max+ 395 64GB	Q8_0	fits	105 tok/s	–	–
Mac mini M4 32GB	Q8_0	fits	58 tok/s	–	–
NVIDIA Tesla P40	Q8_0	fits	161 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 64k context is the NVIDIA GeForce RTX 3060 12GB, $497 at Amazon, at about 167 tok/s (estimated). The best value is the NVIDIA GeForce RTX 3080 10GB (about 353 tok/s for $769). The fastest we price is the NVIDIA RTX PRO 6000 Blackwell, at about 831 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 64k context. On the NVIDIA GeForce RTX 5080 it runs at about 445 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 64k context. On the NVIDIA GeForce RTX 3090 Ti it runs at about 468 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.