AI Knowledge HubQwen 3.5: the low-spec local king โ the 4B rebellion
Qwen 3.5: the low-spec local king โ the 4B rebellion
Conclusion
Among local AI models you can run in an 8GB VRAM environment, Qwen 3.5 4B is by far the most sensible choice right now. It is not an official distilled model, but community labs such as Empero AI have distilled Qwen 3.8 down to 2B/4B/9B, and those have passed one million HuggingFace downloads. On an RTX 8GB card, the 4B holds a stable 50-60 tok/s and the 9B 20-30 tok/s.
Why Qwen โ how it differs from Gemma 4
Gemma 4 12B has natural Korean. But there is a problem. Its interjections are set in stone. Expressions like "Wow, that's really amazing!" keep coming out, and over a long conversation they grate. No interjections at all would be better.
Qwen 3.5 is different. Its Korean responses are clean and it uses little unnecessary decoration. In particular, it follows system instructions precisely, so when used for an agent it follows skills and rules almost 90% of the time or better. Gemma 4 is smoother in Korean but strays from the rules more often.
The Qwen 3.5 lineup โ low-spec model comparison
| Model | Parameters | Thinking by default | VRAM (Q4) | Context | Best fit |
|---|---|---|---|---|---|
| Qwen3.5 0.8B | 800M | OFF | Runs on CPU alone | 262K-1M | Edge devices, offline |
| Qwen3.5 2B | 2B | OFF | ~2GB | 262K-1M | Laptops, GTX 1060-class |
| Qwen3.5 4B | 4B | ON | ~3-5GB | 262K-1M | RTX 8GB (recommended) |
| Qwen3.5 9B | 9B | ON | ~5-6GB (Q4) / 18GB (full) | 262K-1M | RTX 16GB or more |
4B vs 9B โ a 5% performance gap
On benchmarks, the overall performance gap between the 4B and 9B is only about 5%. That is a huge number.
- 4B: ~85% of the 397B flagship's performance
- 9B: ~90% of the 397B flagship's performance
- Difference: 5 percentage points (for more than 3x the VRAM)
On agent tasks, multi-step reasoning, and tool calls, the 4B is on par with the 9B. Only in visual/video understanding does the 9B pull ahead, so for text-centric agent work the 4B is enough.
Key numbers (4B vs 9B):
| Item | 4B | 9B | Note |
|---|---|---|---|
| Agent tasks | mid-90s | 100-class | 4B is ~95% of the 9B |
| Reasoning | Strong | Slightly stronger | Thinking mode ON |
| VRAM (Q4) | 3-5GB | 5-6GB / 18GB full | 4B fits an 8GB card |
| Token speed (RTX 8GB) | 50-60 tok/s | 20-30 tok/s | 4B is 2x faster |
| Korean quality | Clean | Clean | Both good |
The 4B that beat GPT-4o
In an independent benchmark, Claude Opus compared Qwen 3.5 4B and GPT-4o across 1,000 prompts. The result:
- 4B wins: 50 wins (STEM, roleplay, conversation, general knowledge)
- GPT-4o wins: 43 wins (creative writing)
- Ties: 7
A 4B model that runs on an 8GB GPU beat GPT-4o overall. This is an exceptional result in the history of small models.
Empero AI distilled models โ shrinking Qwen 3.8
Models created by Empero AI, an independent German AI lab, by distilling Qwen 3.8 (27B):
| Model | Source | Parameters | License | Downloads |
|---|---|---|---|---|
| Qwen3.8-Distilled-2B | Qwen3.8 27B | 2B | Apache 2.0 | HuggingFace |
| Qwen3.8-Distilled-4B | Qwen3.8 27B | 4B | Apache 2.0 | HuggingFace |
| Qwen3.8-Distilled-9B | Qwen3.8 27B | 9B | Apache 2.0 | 1M+ |
Distilled models shrink the knowledge of the original 27B, and their chain-of-thought (Thinking) reasoning can be stronger than official Qwen3.5. They install directly from Ollama.
Hands-on comparison โ RTX 8GB environment
Measured on the operator's environment (RTX 8GB, Ollama, Q4_K_M):
| Model | VRAM used | Tokens/sec | Response quality | Korean |
|---|---|---|---|---|
| Qwen3.5 4B | ~4.5GB | 55 tok/s | Excellent | Clean, no decoration |
| Qwen3.5 9B | ~6.2GB | 25 tok/s | Best | Clean |
| Gemma 4 12B | ~8.5GB | 15 tok/s | Excellent | Smooth but repetitive interjections |
Conclusion โ at low spec, 4B is the answer
If you have an 8GB VRAM environment, Qwen 3.5 4B is the best choice. It is only 5% behind the 9B, uses less than half the VRAM, and is twice as fast. Its Korean quality is cleaner than Gemma 4's, and its agent skill-injection accuracy is above 90%.
The Qwen series is Apache 2.0 licensed, so free commercial use is allowed, and it installs directly with ollama pull qwen3.5:4b. The fundamentals of the 2026 local AI agent start with this model.
Sources:
- Qwen3.5 official repo (QwenLM/Qwen3)
- Empero AI โ empero.org (Qwen3.8 Distilled)
- Sonusahani.com โ Qwen3.5 0.8B/2B/4B/9B comparison benchmark
- Claude Opus 1,000-prompt comparison evaluation (2026)
- Ollama official library