text model · Mistral · Linux
Can I run Ministral 3 3B on Nvidia DGX Spark (128GB)?
Yes. Ministral 3 3B runs on Nvidia DGX Spark (128GB) at Q4_K_M (~3.2 GB of ~112 GB usable).
Runs at Q4_K_M using ~3.2 GB of ~112 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 DGX Spark (128GB) leaves ~108.8 GB of headroom, room to step up to FP16 for higher quality.
- Q4_K_M needed
- ~3.2 GB
- Usable on device
- ~112 GB
- Device memory
- 128 GB
- Best quant
- Q4_K_M
Which quant fits
Running cost · estimate
- Power draw
- ~140 W
- Electricity / 1M tokens
- ~$0.07
- Pays for itself after
- ~9,300M tok
At ~$0.15/kWh and the estimated ~89 tok/s, a million generated tokens costs about $0.07 in electricity. Versus a hosted API at ~$0.5 per million tokens, the ~$3,999 Nvidia DGX Spark (128GB) pays for itself after roughly 9,300 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 ministral-3:3b llama-cli -hf mistralai/Ministral-3-3B-Instruct-2512-GGUF:Q4_K_M lms get mistralai/Ministral-3-3B-Instruct-2512-GGUF - Parameters
- 3B
- Q4_K_M size
- 2 GB
- Q8_0 size
- 3.4 GB
- Context
- 256k
- Ollama tag
- ministral-3:3b
- Memory
- 128 GB unified
- Usable for weights
- ~112 GB
- Power draw
- ~140 W
- Best runtime
- llama.cpp (CUDA) / Ollama (CUDA)
You could also run
Run Ministral 3 3B on other hardware
FAQ
Can Nvidia DGX Spark (128GB) run Ministral 3 3B?
Yes. Ministral 3 3B runs on Nvidia DGX Spark (128GB) at Q4_K_M (~3.2 GB of ~112 GB usable).
How much memory does Ministral 3 3B need?
Nvidia DGX Spark (128GB) has room to spare. At Q4_K_M the weights are ~2 GB; with KV cache and runtime overhead, budget ~3.2 GB at a 4k context.
What is the best tool to run Ministral 3 3B 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/ministral-3-3b/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 · 2 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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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.