text model · DeepSeek-R1-Distill · Windows
Can I run DeepSeek-R1-Distill-Llama 8B on Nvidia GeForce RTX 3090 (24GB)?
Yes. DeepSeek-R1-Distill-Llama 8B runs on Nvidia GeForce RTX 3090 (24GB) at Q4_K_M (~6.4 GB of ~23 GB usable).
Runs at Q4_K_M using ~6.4 GB of ~23 GB usable. You have room for FP16 for higher quality.
- Q4_K_M needed
- ~6.4 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 deepseek-r1:8b llama-cli -hf bartowski/DeepSeek-R1-Distill-Llama-8B-GGUF:Q4_K_M lms get bartowski/DeepSeek-R1-Distill-Llama-8B-GGUF AMD GPUs run via Vulkan/ROCm at roughly half CUDA throughput. NVIDIA is the smooth path on Windows.
- Parameters
- 8B
- Q4_K_M size
- 4.92 GB
- Q8_0 size
- 8.54 GB
- Context
- 128k
- Ollama tag
- deepseek-r1:8b
- Memory
- 24 GB vram
- Usable for weights
- ~23 GB
- Best runtime
- vLLM (Linux) / Ollama (CUDA)
You could also run
Run DeepSeek-R1-Distill-Llama 8B on other hardware
FAQ
Can Nvidia GeForce RTX 3090 (24GB) run DeepSeek-R1-Distill-Llama 8B?
Yes. DeepSeek-R1-Distill-Llama 8B runs on Nvidia GeForce RTX 3090 (24GB) at Q4_K_M (~6.4 GB of ~23 GB usable).
How much memory does DeepSeek-R1-Distill-Llama 8B need?
Nvidia GeForce RTX 3090 (24GB) has room to spare. At Q4_K_M the weights are ~4.92 GB; with KV cache and runtime overhead, budget ~6.4 GB at a 4k context.
What is the best tool to run DeepSeek-R1-Distill-Llama 8B 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.