Skip to content
localmodel.run

text model · EXAONE · Linux

Can I run EXAONE 4.0 32B on Nvidia DGX Spark (128GB)?

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

Yes. EXAONE 4.0 32B runs on Nvidia DGX Spark (128GB) at Q4_K_M (~20.2 GB of ~112 GB usable).

Needs ~20.2 GB Device usable ~112 GB

Runs at Q4_K_M using ~20.2 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 ~91.8 GB of headroom, room to step up to FP16 for higher quality.

Q4_K_M needed
~20.2 GB
Usable on device
~112 GB
Device memory
128 GB
Best quant
Q4_K_M
Share on X Share on Reddit

Which quant fits

Quant ladder vs ~112 GB usable
Q2_K
~15.6 GB
Q3_K_M
~17.8 GB
Q4_K_M
~20.2 GB
Q5_K_M
~25 GB
Q6_K
~28.4 GB
Q8_0
~33.9 GB
FP16
~66.2 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

Pick your tool. All 2 load the same Q4_K_M weights.

llama.cpp
$ llama-cli -hf LGAI-EXAONE/EXAONE-4.0-32B-GGUF:Q4_K_M
LM Studio
$ lms get LGAI-EXAONE/EXAONE-4.0-32B-GGUF

How to run it

On Linux use Ollama (One-line install, simple single-user UX.).

Model EXAONE
Parameters
32B
Q4_K_M size
18.02 GB
Q8_0 size
31.67 GB
Context
131k

Set up this model at 4K →

Full EXAONE 4.0 32B 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 EXAONE 4.0 32B on other hardware

Nvidia GeForce RTX 4090 (24GB)Nvidia GeForce RTX 3090 (24GB)AMD Radeon RX 7900 XTX (24GB)Nvidia GeForce RTX 5090 (32GB)32GB RAM Laptop (CPU/iGPU only)Apple M5 (32GB)

FAQ

Can Nvidia DGX Spark (128GB) run EXAONE 4.0 32B?

Yes. EXAONE 4.0 32B runs on Nvidia DGX Spark (128GB) at Q4_K_M (~20.2 GB of ~112 GB usable).

How much memory does EXAONE 4.0 32B need?

Nvidia DGX Spark (128GB) has room to spare. At Q4_K_M the weights are ~18.02 GB; with KV cache and runtime overhead, budget ~20.2 GB at a 4k context.

What is the best tool to run EXAONE 4.0 32B 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

EXAONE 4.0 32B on Nvidia DGX Spark (128GB) compatibility badge A live badge for your model card or README, updated as the data is.
Markdown
$ [![EXAONE 4.0 32B on Nvidia DGX Spark (128GB)](https://localmodel.run/badge/exaone-4-32b/nvidia-dgx-spark-128gb.svg)](https://localmodel.run/can-i-run/exaone-4-32b/nvidia-dgx-spark-128gb)

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.