text model · Mistral · Linux

Can I run Devstral 2 123B on Nvidia DGX Spark (128GB)?

Compatibility verdict VRAM threshold engine

Yes, it runs usable speed ~3 tok/s est.

Yes. Devstral 2 123B runs on Nvidia DGX Spark (128GB) at Q4_K_M (~73.3 GB of ~112 GB usable).

Needs ~73.3 GB Device usable ~112 GB

Runs at Q4_K_M using ~73.3 GB of ~112 GB usable.

That figure is at a 4k context and moves about ±15% as context length changes. Nvidia DGX Spark (128GB) leaves ~38.7 GB of headroom.

Q4_K_M needed: ~73.3 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

~55 GB

Q3_K_M

~63.6 GB

Q4_K_M

~73.3 GB

Q5_K_M

~91.1 GB

Q6_K

~104.4 GB

Q8_0

~127.2 GB

FP16

~253.6 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: ~$1.9

At ~$0.15/kWh and the estimated ~3 tok/s, a million generated tokens costs about $1.9 in electricity. 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 devstral-2:123b

llama.cpp

$ llama-cli -hf unsloth/Devstral-2-123B-Instruct-2512-GGUF:Q4_K_M

LM Studio

$ lms get unsloth/Devstral-2-123B-Instruct-2512-GGUF

Model Mistral

Parameters: 123B
Q4_K_M size: 69.75 GB
Q8_0 size: 123.73 GB
Context: 256k
Ollama tag: devstral-2:123b

Set up this model at 4K →

Full Devstral 2 123B 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 Devstral 2 123B on other hardware

Apple M4 Max (128GB)AMD Ryzen AI Halo (128GB)Apple M5 Max (128GB)Apple M3 Ultra (256GB)

FAQ

Can Nvidia DGX Spark (128GB) run Devstral 2 123B?

Yes. Devstral 2 123B runs on Nvidia DGX Spark (128GB) at Q4_K_M (~73.3 GB of ~112 GB usable).

How much memory does Devstral 2 123B need?

Nvidia DGX Spark (128GB) has room to spare. At Q4_K_M the weights are ~69.75 GB; with KV cache and runtime overhead, budget ~73.3 GB at a 4k context.

What is the best tool to run Devstral 2 123B 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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Markdown

[![Devstral 2 123B on Nvidia DGX Spark (128GB)](https://localmodel.run/badge/devstral-2-123b/nvidia-dgx-spark-128gb.svg)](https://localmodel.run/can-i-run/devstral-2-123b/nvidia-dgx-spark-128gb)

Sources

docs.nvidia.com · 2 sources
- docs.nvidia.com/dgx/dgx-spark
- docs.nvidia.com/dgx/dgx-spark-porting-guide
forums.developer.nvidia.com · 2 sources
- forums.developer.nvidia.com/t/difference-in-total-vram-available-for-different-sparks
- forums.developer.nvidia.com/t/memory-creep-on-dgx-spark-where-your-128-gb-actually-goes-and-how-to-stop-it
github.com · 1 source
- github.com
huggingface.co · 2 sources
- huggingface.co/mistralai
- huggingface.co/unsloth
nvidia.com · 1 source
- nvidia.com
nvidianews.nvidia.com · 1 source
- nvidianews.nvidia.com
ollama.com · 2 sources
- ollama.com
- ollama.com/library
tomshardware.com · 1 source
- tomshardware.com

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.