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text model · dots.llm · Linux

Can I run dots.llm1 on Nvidia DGX Spark (128GB)?

Compatibility verdict VRAM threshold engine
Yes, it runs GPU accelerated

Yes. dots.llm1 runs on Nvidia DGX Spark (128GB) at Q4_K_M (~98.1 GB of ~112 GB usable).

Needs ~98.1 GB Device usable ~112 GB

Runs at Q4_K_M using ~98.1 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 ~13.9 GB of headroom.

Q4_K_M needed
~98.1 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
~63.2 GB
Q3_K_M
~73.1 GB
Q4_K_M
~98.1 GB
Q5_K_M
~104.9 GB
Q6_K
~120.1 GB
Q8_0
~155.6 GB
FP16
~289.7 GB
The line marks Nvidia DGX Spark (128GB)'s ~112 GB budget; rungs past it are too large.

Run it

Install commands Linux

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

llama.cpp
$ llama-cli -hf mradermacher/dots.llm1.inst-GGUF:Q4_K_M
LM Studio
$ lms get mradermacher/dots.llm1.inst-GGUF

How to run it

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

Model dots.llm
Parameters
142B (MoE, 14B active)
Q4_K_M size
94.4 GB
Q8_0 size
151.9 GB
Context
32k

Set up this model at 4K →

Full dots.llm1 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 dots.llm1 on other hardware

Apple M3 Ultra (256GB)

FAQ

Can Nvidia DGX Spark (128GB) run dots.llm1?

Yes. dots.llm1 runs on Nvidia DGX Spark (128GB) at Q4_K_M (~98.1 GB of ~112 GB usable).

How much memory does dots.llm1 need?

Nvidia DGX Spark (128GB) has room to spare. At Q4_K_M the weights are ~94.4 GB; with KV cache and runtime overhead, budget ~98.1 GB at a 4k context. It is a Mixture-of-Experts model (142B total / 14B active), so all experts must stay in memory; memory tracks total params, not active params.

What is the best tool to run dots.llm1 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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dots.llm1 on Nvidia DGX Spark (128GB) compatibility badge A live badge for your model card or README, updated as the data is.
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$ [![dots.llm1 on Nvidia DGX Spark (128GB)](https://localmodel.run/badge/dots-llm1/nvidia-dgx-spark-128gb.svg)](https://localmodel.run/can-i-run/dots-llm1/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.