text model · DeepSeek-R1 · Linux
Can I run DeepSeek-R1-0528 on Nvidia DGX Spark (128GB)?
No. DeepSeek-R1-0528 needs ~384.1 GB even at Q4_K_M, but Nvidia DGX Spark (128GB) only has ~112 GB usable.
Needs ~384.1 GB even at Q4_K_M, but only ~112 GB is usable.
That figure is at a 4k context and moves about ±15% as context length changes.
The gap is about 272.1 GB: DeepSeek-R1-0528 needs roughly 384.1 GB at Q4_K_M and Nvidia DGX Spark (128GB) leaves only about 112 GB usable for a model. No single tracked device has enough memory; DeepSeek-R1-0528 needs a multi-GPU or high-memory rig.
- Q4_K_M needed
- ~384.1 GB
- Usable on device
- ~112 GB
- Device memory
- 128 GB
Which quant fits
- Parameters
- 671B (MoE, 37B active)
- Q4_K_M size
- 377.13 GB
- Q8_0 size
- 664.3 GB
- Context
- 160k
- Ollama tag
- deepseek-r1:671b
- Memory
- 128 GB unified
- Usable for weights
- ~112 GB
- Power draw
- ~140 W
- Best runtime
- llama.cpp (CUDA) / Ollama (CUDA)
What you can run instead
FAQ
Can Nvidia DGX Spark (128GB) run DeepSeek-R1-0528?
No. DeepSeek-R1-0528 needs ~384.1 GB even at Q4_K_M, but Nvidia DGX Spark (128GB) only has ~112 GB usable.
How much memory does DeepSeek-R1-0528 need?
Nvidia DGX Spark (128GB) does not have enough memory. At Q4_K_M the weights are ~377.13 GB; with KV cache and runtime overhead, budget ~384.1 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-R1-0528 on Linux?
Ollama for a simple setup; vLLM for the most speed. vLLM shines for multi-user serving/throughput. For a single local chat, Ollama or llama.cpp is simpler and lighter.
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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-r1-0528/nvidia-dgx-spark-128gb) Sources
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aider.chat · 1 source
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docs.nvidia.com · 2 sources
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github.com · 1 source
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huggingface.co · 5 sources
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nvidia.com · 1 source
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nvidianews.nvidia.com · 1 source
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ollama.com · 2 sources
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openrouter.ai · 1 source
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tomshardware.com · 1 source
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.