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text model · Ornith · Linux

Can I run Ornith 1.0 35B on Nvidia DGX Spark (128GB)?

Compatibility verdict VRAM threshold engine
Yes, it runs GPU accelerated

Yes. Ornith 1.0 35B runs on Nvidia DGX Spark (128GB) at Q4_K_M (~23.2 GB of ~112 GB usable).

Needs ~23.2 GB Device usable ~112 GB

Runs at Q4_K_M using ~23.2 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 ~88.8 GB of headroom, room to step up to FP16 for higher quality.

Q4_K_M needed
~23.2 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
~16.9 GB
Q3_K_M
~19.3 GB
Q4_K_M
~23.2 GB
Q5_K_M
~27.1 GB
Q6_K
~30.9 GB
Q8_0
~39.2 GB
FP16
~71.2 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 3 load the same Q4_K_M weights.

Ollama
$ ollama run ornith:35b
llama.cpp
$ llama-cli -hf deepreinforce-ai/Ornith-1.0-35B-GGUF:Q4_K_M
LM Studio
$ lms get deepreinforce-ai/Ornith-1.0-35B-GGUF
Model Ornith
Parameters
35B (MoE, 3B active)
Q4_K_M size
21 GB
Q8_0 size
37 GB
Context
256k
Ollama tag
ornith:35b

Set up this model at 4K →

Full Ornith 1.0 35B 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 Ornith 1.0 35B on other hardware

Nvidia GeForce RTX 5090 (32GB)32GB RAM Laptop (CPU/iGPU only)Apple M4 Pro (48GB)Apple M5 Pro (48GB)Apple M4 Max (64GB)Apple M4 Max (128GB)

FAQ

Can Nvidia DGX Spark (128GB) run Ornith 1.0 35B?

Yes. Ornith 1.0 35B runs on Nvidia DGX Spark (128GB) at Q4_K_M (~23.2 GB of ~112 GB usable).

How much memory does Ornith 1.0 35B need?

Nvidia DGX Spark (128GB) has room to spare. At Q4_K_M the weights are ~21 GB; with KV cache and runtime overhead, budget ~23.2 GB at a 4k context. It is a Mixture-of-Experts model (35B total / 3B active), so all experts must stay in memory; memory tracks total params, not active params.

What is the best tool to run Ornith 1.0 35B 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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Ornith 1.0 35B 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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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.