text model · DeepSeek-R1 · Windows
Can I run DeepSeek R1 on Nvidia GeForce RTX 4060 Ti (16GB)?
No. DeepSeek R1 needs ~383.7 GB even at Q4_K_M, but Nvidia GeForce RTX 4060 Ti (16GB) only has ~15 GB usable.
Needs ~383.7 GB even at Q4_K_M, but only ~15 GB is usable.
- Q4_K_M needed
- ~383.7 GB
- Usable on device
- ~15 GB
- Device memory
- 16 GB
- Parameters
- 671B (MoE, 37B active)
- Q4_K_M size
- 376.66 GB
- Context
- 128k
- Ollama tag
- deepseek-r1:671b
- Memory
- 16 GB vram
- Usable for weights
- ~15 GB
- Best runtime
- Ollama (CUDA) / llama.cpp CUDA
What you can run instead
FAQ
Can Nvidia GeForce RTX 4060 Ti (16GB) run DeepSeek R1?
No. DeepSeek R1 needs ~383.7 GB even at Q4_K_M, but Nvidia GeForce RTX 4060 Ti (16GB) only has ~15 GB usable.
How much memory does DeepSeek R1 need?
Nvidia GeForce RTX 4060 Ti (16GB) does not have enough memory. At Q4_K_M the weights are ~376.66 GB; with KV cache and runtime overhead, budget ~383.7 GB at a 4k context. It is a Mixture-of-Experts model (671B total / 37B active), so all experts must stay in memory; memory tracks total params, not active params.
What is the best tool to run DeepSeek R1 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.