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Can I run Mistral Nemo 12B on Nvidia DGX Spark (128GB)?

Compatibility verdict VRAM threshold engine
Yes, it runs GPU accelerated ~25 tok/s est.

Yes. Mistral Nemo 12B runs on Nvidia DGX Spark (128GB) at Q4_K_M (~8.6 GB of ~112 GB usable).

Needs ~8.6 GB Device usable ~112 GB

Runs at Q4_K_M using ~8.6 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 ~103.4 GB of headroom, room to step up to FP16 for higher quality.

Q4_K_M needed
~8.6 GB
Usable on device
~112 GB
Device memory
128 GB
Best quant
Q4_K_M
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Which quant fits

Quant ladder vs ~112 GB usable
Q2_K
~6.7 GB
Q3_K_M
~7.6 GB
Q4_K_M
~8.6 GB
Q5_K_M
~10.3 GB
Q6_K
~11.6 GB
Q8_0
~13.7 GB
FP16
~26 GB
The line marks Nvidia DGX Spark (128GB)'s ~112 GB budget; rungs past it are too large.

Running cost · estimate

Power & economics est.
Power draw
~140 W
Electricity / 1M tokens
~$0.23
Pays for itself after
~14,811M tok

At ~$0.15/kWh and the estimated ~25 tok/s, a million generated tokens costs about $0.23 in electricity. Versus a hosted API at ~$0.5 per million tokens, the ~$3,999 Nvidia DGX Spark (128GB) pays for itself after roughly 14,811 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

Install commands Linux

Pick your tool. All 3 load the same Q4_K_M weights.

Ollama
$ ollama run mistral-nemo:12b
llama.cpp
$ llama-cli -hf bartowski/Mistral-Nemo-Instruct-2407-GGUF:Q4_K_M
LM Studio
$ lms get bartowski/Mistral-Nemo-Instruct-2407-GGUF
Model Mistral
Parameters
12.2B
Q4_K_M size
6.96 GB
Q8_0 size
12.13 GB
Context
128k
Ollama tag
mistral-nemo:12b

Set up this model at 4K →

Full Mistral Nemo 12B requirements →
Device Linux
Memory
128 GB unified
Usable for weights
~112 GB
Power draw
~140 W
Best runtime
llama.cpp (CUDA) / Ollama (CUDA)
Best models for Nvidia DGX Spark (128GB) →

You could also run

Run Mistral Nemo 12B on other hardware

Nvidia GeForce RTX 3060 (12GB)Nvidia GeForce RTX 4070 (12GB)Nvidia GeForce RTX 5070 (12GB)Apple M2 (16GB)Apple M4 (16GB)Nvidia GeForce RTX 4060 Ti (16GB)

FAQ

Can Nvidia DGX Spark (128GB) run Mistral Nemo 12B?

Yes. Mistral Nemo 12B runs on Nvidia DGX Spark (128GB) at Q4_K_M (~8.6 GB of ~112 GB usable).

How much memory does Mistral Nemo 12B need?

Nvidia DGX Spark (128GB) has room to spare. At Q4_K_M the weights are ~6.96 GB; with KV cache and runtime overhead, budget ~8.6 GB at a 4k context.

What is the best tool to run Mistral Nemo 12B 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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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.