text model · Qwen3 · Windows
Can I run Qwen3 235B A22B on Nvidia GeForce RTX 4060 Ti (16GB)?
No. Qwen3 235B A22B needs ~136.9 GB even at Q4_K_M, but Nvidia GeForce RTX 4060 Ti (16GB) only has ~15 GB usable.
Needs ~136.9 GB even at Q4_K_M, but only ~15 GB is usable.
That figure is at a 4k context and moves about ±15% as context length changes.
The gap is about 121.9 GB: Qwen3 235B A22B needs roughly 136.9 GB at Q4_K_M and Nvidia GeForce RTX 4060 Ti (16GB) leaves only about 15 GB usable for a model. The lightest tracked hardware that runs Qwen3 235B A22B is the Apple M3 Ultra (256GB) at 256 GB. See Qwen3 235B A22B on Apple M3 Ultra (256GB).
- Q4_K_M needed
- ~136.9 GB
- Usable on device
- ~15 GB
- Device memory
- 16 GB
Which quant fits
- Parameters
- 235B (MoE, 22B active)
- Q4_K_M size
- 132.39 GB
- Q8_0 size
- 232.77 GB
- Context
- 128k
- Ollama tag
- qwen3:235b
- Memory
- 16 GB vram
- Usable for weights
- ~15 GB
- Power draw
- ~165 W
- Best runtime
- Ollama (CUDA) / llama.cpp CUDA
What you can run instead
Run Qwen3 235B A22B on other hardware
FAQ
Can Nvidia GeForce RTX 4060 Ti (16GB) run Qwen3 235B A22B?
No. Qwen3 235B A22B needs ~136.9 GB even at Q4_K_M, but Nvidia GeForce RTX 4060 Ti (16GB) only has ~15 GB usable.
How much memory does Qwen3 235B A22B need?
Nvidia GeForce RTX 4060 Ti (16GB) does not have enough memory. At Q4_K_M the weights are ~132.39 GB; with KV cache and runtime overhead, budget ~136.9 GB at a 4k context. It is a Mixture-of-Experts model (235B total / 22B active), so all experts must stay in memory; memory tracks total params, not active params.
What is the best tool to run Qwen3 235B A22B 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.
Embed this
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Weights are measured from GGUF files; KV cache and overhead are computed, so totals can vary ~15% with context and runtime. Any tok/s is a bandwidth estimate. See methodology.