dgx-spark-qwen38 / Models / Ornith 1.5 122B
Run Ornith 1.5 122B on a DGX Spark
A 122B mixture of experts with vision, by Ornith AI. NVFP4 with its MTP head, on vLLM.
Ornith AI's 122B mixture of experts with vision and a window of a million tokens, in NVFP4 on vLLM with its MTP head. On an ASUS Ascent GX10 with 128 GB: 42.4 tok/s, greedy median, one request.
Measured
42.4tok/s
greedy median, one request
- 90.0 tok/s in all at 8 streams on five real jobs
- GSM8K 98.8% on 250 problems, on a second GX10
- A window of a million tokens
- Measured on
- an ASUS Ascent GX10 with 128 GB, the project's reference box
- How
- ./bench.sh
- Source
- docs/ornith-lane.md
Load it
From the cockpit: open the Lanes view, pick Ornith 1.5 122B and press Load (5 and a half minutes the first time, 2 to 4 after). From a terminal, in the repo folder on the box:
./prepare-lane.sh ornith-122b # the first time only, builds the lane
./switch-model.sh ornith-122b # switches the boot, then prints the commands to runswitch-model.sh never restarts an engine itself: it prints the exact stop and start commands. The cockpit's Load runs them for you.
On a 64 GB DGX Spark
No: it needs a 128 GB DGX Spark. A 64 GB box serves Nemotron 3.5 Bolt and Qwen3.8-27B: what runs on 64 GB.
Use it from your tools
The box serves OpenAI- and Anthropic-compatible APIs on port 30001, with an API key. Claude Code, opencode, Open WebUI and the OpenAI and Anthropic SDKs point there instead of a cloud: see Claude Code with a local model and opencode with a local model.
Install it
One command, on stock DGX OS. The same command updates it later.
curl -fsSL https://raw.githubusercontent.com/hasso5703/dgx-spark-qwen38/main/get.sh | bashQuestions about Ornith 1.5 122B on a DGX Spark
Can a DGX Spark run Ornith 1.5 122B?
Yes. Ornith AI's 122B mixture of experts with vision and a window of a million tokens, in NVFP4 on vLLM with its MTP head. The installer of dgx-spark-qwen38 sets it up, and the cockpit loads it with one button.
How fast is Ornith 1.5 122B on a DGX Spark?
42.4 tok/s, greedy median, one request, measured on an ASUS Ascent GX10 with 128 GB. 90.0 tok/s in all at 8 streams on five real jobs. GSM8K 98.8% on 250 problems, on a second GX10. A window of a million tokens.
Does Ornith 1.5 122B run on a 64 GB DGX Spark?
No: it needs a 128 GB DGX Spark.
The other lanes
- QwenQwen3.8-27B71.4 tok/s, greedy median, one request
- QwenQwen3.8-Flash-Next 176B47.9 tok/s, on code, with FLASH_TIER=context
- NVIDIAFlash FP4-Local55.4 tok/s, greedy median, at its default effort
- NVIDIANemotron 3.5 Bolt140 tok/s, rewriting code, one request, reasoning off
- NVIDIANemotron 3.5 Super VL69.5 tok/s, rewriting code, one request, reasoning off
- Aleph AlphaKolibri-148.0 tok/s, one request
- QwenQwen-Image 2.17.5 s, with its Turbo, per 1024×1024 image
- MiniMaxMiniMax-H3126.2 to 127.9 s, with the Turbo adapter, for 4 s of 480P video