text model · Qwen3 · Windows
Can I run Qwen3 0.6B on Nvidia GeForce RTX 3090 (24GB)?
Yes. Qwen3 0.6B runs on Nvidia GeForce RTX 3090 (24GB) at Q4_K_M (~1.5 GB of ~23 GB usable).
Runs at Q4_K_M using ~1.5 GB of ~23 GB usable. You have room for FP16 for higher quality.
- Q4_K_M needed
- ~1.5 GB
- Usable on device
- ~23 GB
- Device memory
- 24 GB
- Best quant
- Q4_K_M
Run it
Pick your tool. All three load the same Q4_K_M weights.
ollama run qwen3:0.6b llama-cli -hf bartowski/Qwen_Qwen3-0.6B-GGUF:Q4_K_M lms get bartowski/Qwen_Qwen3-0.6B-GGUF AMD GPUs run via Vulkan/ROCm at roughly half CUDA throughput. NVIDIA is the smooth path on Windows.
- Parameters
- 0.6B
- Q4_K_M size
- 0.48 GB
- Q8_0 size
- 0.8 GB
- Context
- 32k
- Ollama tag
- qwen3:0.6b
- Memory
- 24 GB vram
- Usable for weights
- ~23 GB
- Best runtime
- vLLM (Linux) / Ollama (CUDA)
You could also run
Run Qwen3 0.6B on other hardware
FAQ
Can Nvidia GeForce RTX 3090 (24GB) run Qwen3 0.6B?
Yes. Qwen3 0.6B runs on Nvidia GeForce RTX 3090 (24GB) at Q4_K_M (~1.5 GB of ~23 GB usable).
How much memory does Qwen3 0.6B need?
Nvidia GeForce RTX 3090 (24GB) has room to spare. At Q4_K_M the weights are ~0.48 GB; with KV cache and runtime overhead, budget ~1.5 GB at a 4k context.
What is the best tool to run Qwen3 0.6B on Windows?
LM Studio for a simple setup; Ollama (CUDA) for the most speed. AMD GPUs run via Vulkan/ROCm at roughly half CUDA throughput. NVIDIA is the smooth path on Windows.
Sources
Memory figures are estimates. See methodology.