About this model
Qwen3 8B combines the practical 8B memory class with the newer generation's reasoning capabilities. On an 8 GB card at Q4 it is one of the strongest all-round local assistants available today.
What it is good for
- Everyday chat and research assistance
- Reasoning tasks with thinking mode enabled
- Local tool-use and automation backends
Limitations
- Thinking mode adds latency to simple questions
- Still behind 14B+ models on complex analysis
Memory by quantization (estimates)
Weights at a given bit-width plus estimated runtime and context overhead. These are estimates — see the methodology.
| Quantization | Approx. memory | Fits a 6 GB card | Recommended class |
|---|---|---|---|
| Q4 (GGUF) | ~4.7 GB | Good | 6 GB+ |
| Q8 (GGUF) | ~8.5 GB | Tight | 9 GB+ |
| FP16 | ~16 GB | Tight | 16 GB+ |
GPUs that can run it
NVIDIA GeForce RTX 4050 Laptop GPU6 GB VRAMNVIDIA GeForce RTX 40608 GB VRAMNVIDIA GeForce RTX 4060 Ti8 GB VRAMNVIDIA GeForce RTX 407012 GB VRAMNVIDIA GeForce RTX 408016 GB VRAMNVIDIA GeForce RTX 409024 GB VRAMNVIDIA GeForce RTX 3060 12 GB12 GB VRAMAMD Radeon RX 7900 XTX24 GB VRAMAMD Radeon RX 7800 XT16 GB VRAMIntel Arc A77016 GB VRAMApple M4 Pro27 GB unifiedApple M3 Pro (18 GB unified memory)18 GB unifiedApple M4 Max (36 GB+ unified memory)36 GB unified
FAQ
How much VRAM does Qwen3 8B need?
About 4.5–5 GB at Q4 — an 8 GB card is comfortable; 6 GB works with a moderate context.
Is it better than Qwen2.5 7B?
Generally yes on reasoning and instruction following; both fit the same hardware, so for most users Qwen3 8B is the default pick of the two.
Can I turn off thinking mode?
Yes — Qwen3 supports disabling the reasoning phase for faster answers when you do not need it.
Does it run on Apple Silicon?
Yes, via Ollama/llama.cpp with Metal or MLX builds, comfortably within 16 GB of unified memory.