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text model · Command · Linux

Can I run Command A on Nvidia DGX Spark (128GB)?

Compatibility verdict VRAM threshold engine
Yes, it runs usable speed ~3 tok/s est.

Yes. Command A runs on Nvidia DGX Spark (128GB) at Q4_K_M (~70.4 GB of ~112 GB usable).

Needs ~70.4 GB Device usable ~112 GB

Runs at Q4_K_M using ~70.4 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 ~41.6 GB of headroom.

Q4_K_M needed
~70.4 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
~49.8 GB
Q3_K_M
~57.6 GB
Q4_K_M
~70.4 GB
Q5_K_M
~82.4 GB
Q6_K
~94.3 GB
Q8_0
~121.3 GB
FP16
~225.3 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 command-a:111b
llama.cpp
$ llama-cli -hf lmstudio-community/c4ai-command-a-03-2025-GGUF:Q4_K_M
LM Studio
$ lms get lmstudio-community/c4ai-command-a-03-2025-GGUF
Model Command
Parameters
111B
Q4_K_M size
67.1 GB
Q8_0 size
118 GB
Context
256k
Ollama tag
command-a:111b

Set up this model at 4K →

Full Command A 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 Command A 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 Command A?

Yes. Command A runs on Nvidia DGX Spark (128GB) at Q4_K_M (~70.4 GB of ~112 GB usable).

How much memory does Command A need?

Nvidia DGX Spark (128GB) has room to spare. At Q4_K_M the weights are ~67.1 GB; with KV cache and runtime overhead, budget ~70.4 GB at a 4k context.

What is the best tool to run Command A 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.