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text model · Granite · Linux

Can I run Granite 4.1 30B on Nvidia DGX Spark (128GB)?

Compatibility verdict VRAM threshold engine
Yes, it runs GPU accelerated ~10 tok/s est.

Yes. Granite 4.1 30B runs on Nvidia DGX Spark (128GB) at Q4_K_M (~19.1 GB of ~112 GB usable).

Needs ~19.1 GB Device usable ~112 GB

Runs at Q4_K_M using ~19.1 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 ~92.9 GB of headroom, room to step up to FP16 for higher quality.

Q4_K_M needed
~19.1 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
~14.2 GB
Q3_K_M
~16.2 GB
Q4_K_M
~19.1 GB
Q5_K_M
~22.7 GB
Q6_K
~25.8 GB
Q8_0
~33.1 GB
FP16
~60.1 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
~$0.58

At ~$0.15/kWh and the estimated ~10 tok/s, a million generated tokens costs about $0.58 in electricity. TDP is the peak draw, so this is an upper bound. Assumptions.

Run it

Install commands Linux

Loads the Q4_K_M weights.

Ollama
$ ollama run granite4.1:30b
Model Granite
Parameters
28.9B
Q4_K_M size
17 GB
Q8_0 size
31 GB
Context
128k
Ollama tag
granite4.1:30b
Full Granite 4.1 30B 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 Granite 4.1 30B on other hardware

FAQ

Can Nvidia DGX Spark (128GB) run Granite 4.1 30B?

Yes. Granite 4.1 30B runs on Nvidia DGX Spark (128GB) at Q4_K_M (~19.1 GB of ~112 GB usable).

How much memory does Granite 4.1 30B need?

Nvidia DGX Spark (128GB) has room to spare. At Q4_K_M the weights are ~17 GB; with KV cache and runtime overhead, budget ~19.1 GB at a 4k context.

What is the best tool to run Granite 4.1 30B 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.