text model · DeepSeek-V3 · Linux
Can I run DeepSeek V3 on Nvidia DGX Spark (128GB)?
No. DeepSeek V3 needs ~383.7 GB even at Q4_K_M, but Nvidia DGX Spark (128GB) only has ~112 GB usable.
Needs ~383.7 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 271.7 GB: DeepSeek V3 needs roughly 383.7 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 V3 needs a multi-GPU or high-memory rig.
- Q4_K_M needed
- ~383.7 GB
- Usable on device
- ~112 GB
- Device memory
- 128 GB
Which quant fits
- Parameters
- 671B (MoE, 37B active)
- Q4_K_M size
- 376.65 GB
- Q8_0 size
- 664.3 GB
- Context
- 128k
- Ollama tag
- deepseek-v3:671b
- 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 V3?
No. DeepSeek V3 needs ~383.7 GB even at Q4_K_M, but Nvidia DGX Spark (128GB) only has ~112 GB usable.
How much memory does DeepSeek V3 need?
Nvidia DGX Spark (128GB) does not have enough memory. At Q4_K_M the weights are ~376.65 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 V3 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.
Embed this
A live badge for your model card or README, updated as the data is. [](https://localmodel.run/can-i-run/deepseek-v3/nvidia-dgx-spark-128gb) Sources
-
aider.chat · 1 source
-
docs.nvidia.com · 2 sources
-
github.com · 1 source
-
huggingface.co · 2 sources
-
nvidia.com · 1 source
-
nvidianews.nvidia.com · 1 source
-
ollama.com · 3 sources
-
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.