Qwen2.5 0.5B
494M parameters · 32k context · Apache License 2.0 · released 2024-09-16
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In short
Qwen2.5 0.5B is an open model from Alibaba Qwen with 494M parameters. At Q8_0, Qwen2.5 0.5B needs about 1 GB of memory at 8k context and 1 GB at 8k, counting the weights, the KV cache and runtime buffers. The cheapest hardware in Germany that runs Qwen2.5 0.5B fully in memory at 8k context is the NVIDIA GeForce RTX 3060 12GB, 309 € at Amazon, at about 353 tok/s (estimated).
Updated . Speeds are estimates; how they are worked out.
What to run it on in Germany
Fully in memory at 8k context, priced at retailers today.
Cheapest
NVIDIA GeForce RTX 3060 12GB353tok/sest.Q8_0
309 €
Best value
NVIDIA GeForce RTX 3080 10GB746tok/sest.Q8_0
500 €
Fastest
NVIDIA GeForce RTX 50901759tok/sest.Q8_0
6.028 €
Mac
Mac mini M4 16GB124tok/sest.Q8_0
800 €
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 | 353 tok/s | 309 € | 1143.4 |
| Intel Arc B580 | Q8_0 | fits | 361 tok/s | 360 € | 1003.5 |
| NVIDIA Tesla P40 | Q8_0 | fits | 340 tok/s | 369 € | 920.3 |
| NVIDIA GeForce RTX 3080 10GB | Q8_0 | fits | 746 tok/s | 500 € | 1491.8 |
| AMD Radeon RX 9060 XT 16GB | Q8_0 | fits | 279 tok/s | 550 € | 506.6 |
| Intel Arc A770 16GB | Q8_0 | fits | 443 tok/s | 646 € | 686.4 |
| NVIDIA GeForce RTX 4060 Ti 16GB | Q8_0 | fits | 283 tok/s | 650 € | 435.0 |
| NVIDIA GeForce RTX 4070 Super | Q8_0 | fits | 495 tok/s | 707 € | 699.4 |
| NVIDIA GeForce RTX 5060 Ti 16GB | Q8_0 | fits | 440 tok/s | 739 € | 595.0 |
| AMD Radeon RX 9070 XT | Q8_0 | fits | 557 tok/s | 778 € | 716.2 |
| NVIDIA GeForce RTX 5070 | Q8_0 | fits | 660 tok/s | 798 € | 826.4 |
| Mac mini M4 16GB | Q8_0 | fits | 124 tok/s | 800 € | 154.4 |
| AMD Radeon RX 7600 XT | Q8_0 | fits | 251 tok/s | 813 € | 308.2 |
| AMD Radeon RX 7900 XT | Q8_0 | fits | 697 tok/s | 855 € | 814.9 |
| AMD Radeon RX 7800 XT | Q8_0 | fits | 543 tok/s | 1.002 € | 542.4 |
| Intel Arc Pro B60 | Q8_0 | fits | 361 tok/s | 1.074 € | 335.9 |
| NVIDIA GeForce RTX 4080 Super | Q8_0 | fits | 722 tok/s | 1.100 € | 656.6 |
| NVIDIA GeForce RTX 5070 Ti | Q8_0 | fits | 879 tok/s | 1.149 € | 765.4 |
| NVIDIA GeForce RTX 4070 Ti Super | Q8_0 | fits | 660 tok/s | 1.247 € | 528.9 |
| Mac mini M4 32GB | Q8_0 | fits | 124 tok/s | 1.400 € | 88.2 |
| NVIDIA GeForce RTX 5080 | Q8_0 | fits | 942 tok/s | 1.500 € | 628.1 |
| AMD Radeon RX 7900 XTX | Q8_0 | fits | 836 tok/s | 1.555 € | 537.5 |
| NVIDIA GeForce RTX 3090 | Q8_0 | fits | 919 tok/s | 1.850 € | 496.5 |
| AMD Radeon AI PRO R9700 | Q8_0 | fits | 557 tok/s | 1.867 € | 298.5 |
| NVIDIA GeForce RTX 3090 Ti | Q8_0 | fits | 989 tok/s | 2.500 € | 395.7 |
| Mac mini M4 Pro 48GB | Q8_0 | fits | 281 tok/s | 2.699 € | 104.1 |
| Mac mini M4 Pro 64GB | Q8_0 | fits | 281 tok/s | 2.799 € | 100.4 |
| GMKtec EVO-X2 Ryzen AI Max+ 395 128GB | Q8_0 | fits | 223 tok/s | 3.700 € | 60.2 |
| NVIDIA GeForce RTX 4090 | Q8_0 | fits | 989 tok/s | 3.955 € | 250.1 |
| Mac Studio M4 Max 64GB | Q8_0 | fits | 562 tok/s | 4.183 € | 134.3 |
| Mac Studio M4 Max 128GB | Q8_0 | fits | 562 tok/s | 5.800 € | 96.9 |
| Mac Studio M3 Ultra 96GB | Q8_0 | fits | 843 tok/s | 5.999 € | 140.5 |
| NVIDIA GeForce RTX 5090 | Q8_0 | fits | 1759 tok/s | 6.028 € | 291.7 |
| NVIDIA DGX Spark 128GB | Q8_0 | fits | 268 tok/s | 8.313 € | 32.2 |
| ASUS Ascent GX10 128GB | Q8_0 | fits | 268 tok/s | 8.623 € | 31.1 |
| Mac Studio M3 Ultra 256GB | Q8_0 | fits | 843 tok/s | 15.555 € | 54.2 |
| Mac Studio M3 Ultra 512GB | Q8_0 | fits | 843 tok/s | 18.995 € | 44.4 |
| NVIDIA RTX PRO 6000 Blackwell | Q8_0 | fits | 1759 tok/s | 19.076 € | 92.2 |
| Framework Desktop Ryzen AI Max+ 395 128GB | Q8_0 | fits | 223 tok/s | – | – |
| Framework Desktop Ryzen AI Max+ 395 64GB | Q8_0 | fits | 223 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
- Ollama
ollama run hf.co/bartowski/Qwen2.5-0.5B-Instruct-GGUF:Q8_0 - LM Studio
lms get bartowski/Qwen2.5-0.5B-Instruct-GGUF@q8_0 - llama.cpp
llama-server -hf bartowski/Qwen2.5-0.5B-Instruct-GGUF:Q8_0
- Ollama
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
- How much VRAM does Qwen2.5 0.5B need?
- At Q8_0, Qwen2.5 0.5B needs about 1 GB of memory at 8k context and 1 GB at 8k, counting the weights, the KV cache and runtime buffers.
- What is the best GPU for running Qwen2.5 0.5B locally?
- The cheapest hardware in Germany that runs Qwen2.5 0.5B fully in memory at 8k context is the NVIDIA GeForce RTX 3060 12GB, 309 € at Amazon, at about 353 tok/s (estimated). The best value is the NVIDIA GeForce RTX 3080 10GB (about 746 tok/s for 500 €). The fastest we price is the NVIDIA GeForce RTX 5090, at about 1759 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 8k context. On the NVIDIA GeForce RTX 5080 it runs at about 942 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 8k context. On the NVIDIA GeForce RTX 3090 Ti it runs at about 989 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.