text model · DeepSeek-V4 · Linux
Can I run DeepSeek-V4-Flash on Nvidia DGX Spark (128GB)?
No. DeepSeek-V4-Flash needs ~179.7 GB even at Q4_K_M, but Nvidia DGX Spark (128GB) only has ~112 GB usable.
Needs ~179.7 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 67.7 GB: DeepSeek-V4-Flash needs roughly 179.7 GB at Q4_K_M and Nvidia DGX Spark (128GB) leaves only about 112 GB usable for a model. The lightest tracked hardware that runs DeepSeek-V4-Flash is the Apple M3 Ultra (256GB) at 256 GB. See DeepSeek-V4-Flash on Apple M3 Ultra (256GB).
- Q4_K_M needed
- ~179.7 GB
- Usable on device
- ~112 GB
- Device memory
- 128 GB
Which quant fits
How to run it
On Linux use Ollama (One-line install, simple single-user UX.).
- Parameters
- 284B (MoE, 13B active)
- Q4_K_M size
- 174.87 GB
- Q8_0 size
- 302.27 GB
- Context
- 1000k
- 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
Run DeepSeek-V4-Flash on other hardware
FAQ
Can Nvidia DGX Spark (128GB) run DeepSeek-V4-Flash?
No. DeepSeek-V4-Flash needs ~179.7 GB even at Q4_K_M, but Nvidia DGX Spark (128GB) only has ~112 GB usable.
How much memory does DeepSeek-V4-Flash need?
Nvidia DGX Spark (128GB) does not have enough memory. At Q4_K_M the weights are ~174.87 GB; with KV cache and runtime overhead, budget ~179.7 GB at a 4k context. It is a Mixture-of-Experts model (284B total / 13B active), so all experts must stay in memory; memory tracks total params, not active params.
What is the best tool to run DeepSeek-V4-Flash 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-v4-flash/nvidia-dgx-spark-128gb) Sources
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docs.nvidia.com · 2 sources
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github.com · 1 source
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huggingface.co · 3 sources
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nvidia.com · 1 source
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nvidianews.nvidia.com · 1 source
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ollama.com · 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.