text model · DeepSeek-R1-Distill · Windows
Can I run DeepSeek-R1-Distill-Llama 8B on Nvidia GeForce RTX 5090 (32GB)?
Yes. DeepSeek-R1-Distill-Llama 8B runs on Nvidia GeForce RTX 5090 (32GB) at Q4_K_M (~6.4 GB of ~31 GB usable).
Runs at Q4_K_M using ~6.4 GB of ~31 GB usable. You have room for FP16 for higher quality.
That figure is at a 4k context and moves about ±15% as context length changes. Nvidia GeForce RTX 5090 (32GB) leaves ~24.6 GB of headroom, room to step up to FP16 for higher quality.
- Q4_K_M needed
- ~6.4 GB
- Usable on device
- ~31 GB
- Device memory
- 32 GB
- Best quant
- Q4_K_M
Which quant fits
Running cost · estimate
- Power draw
- ~575 W
- Electricity / 1M tokens
- ~$0.1
- Pays for itself after
- ~4,998M tok
At ~$0.15/kWh and the estimated ~237 tok/s, a million generated tokens costs about $0.1 in electricity. Versus a hosted API at ~$0.5 per million tokens, the ~$1,999 Nvidia GeForce RTX 5090 (32GB) pays for itself after roughly 4,998 million tokens, so local hardware is mostly a fixed cost, not a per-token one. TDP is the peak draw, so this is an upper bound. Assumptions.
Run it
Pick your tool. All 3 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 - Parameters
- 8B
- Q4_K_M size
- 4.92 GB
- Q8_0 size
- 8.54 GB
- Context
- 128k
- Ollama tag
- deepseek-r1:8b
- Memory
- 32 GB vram
- Usable for weights
- ~31 GB
- Power draw
- ~575 W
- Best runtime
- vLLM (Linux) / Ollama (CUDA)
You could also run
Run DeepSeek-R1-Distill-Llama 8B on other hardware
FAQ
Can Nvidia GeForce RTX 5090 (32GB) run DeepSeek-R1-Distill-Llama 8B?
Yes. DeepSeek-R1-Distill-Llama 8B runs on Nvidia GeForce RTX 5090 (32GB) at Q4_K_M (~6.4 GB of ~31 GB usable).
How much memory does DeepSeek-R1-Distill-Llama 8B need?
Nvidia GeForce RTX 5090 (32GB) 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.
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A live badge for your model card or README, updated as the data is. [](https://localmodel.run/can-i-run/deepseek-r1-distill-llama-8b/nvidia-rtx-5090-32gb) Sources
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.