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Open-weight model · Text generation

Ornith-1.5-35B-A3B-NVFP4

by Ornith ornith-ai/Ornith-1.5-35B-A3B-NVFP4

Chirp Chirp! We are introducing Ornith-1.5, a major step toward building foundation models through end-to-end self-improvement.

Parameters19.5B
Context262,144
Weights23.4 GB
Licensemit
AccessOpen weights
Monthly Downloads1.1M

Runs On

What it takes to serve Ornith-1.5-35B-A3B-NVFP4 (19.5B parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.

PrecisionWeightsMemory neededCheapest setupPer hourAlso fits
16-bit 39.1 GB 46.9 GB 1x MI300X (192 GB)
Vultr		 $1.85 1x H100 $1.99 · 1x MI325X $2.00
8-bit 19.5 GB 23.4 GB 1x MI300X (192 GB)
Vultr		 $1.85 1x H100 $1.99 · 1x MI325X $2.00
4-bit 9.8 GB 11.7 GB 1x MI300X (192 GB)
Vultr		 $1.85 1x H100 $1.99 · 1x MI325X $2.00

Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Sep 18, 2026.

SAVRN's Notes on Ornith-1.5-35B-A3B-NVFP4

Eight of 256 experts fire on each token. With 19.5B parameters listed, memory comes to 46.9 GB at 16-bit, 23.4 GB at 8-bit, 11.7 GB at 4-bit. All three fit the cheapest setup on our board, one 192 GB MI300X at $1.85 per hour on-demand, leaving the rest of the card to budget for the 262,144 token context. Ornith built it for text generation and trained it through a self-improvement loop that generates its own tasks.

MIT is as light as a license gets: commercial use, modification and redistribution, keeping only the copyright and permission notices. Two things to check. The name says NVFP4 and 35B, yet the 22 files total about 23.5 GB and the 4-bit row reads 9.8 GB of weights, so confirm the precision you are loading. The lineage runs through Ornith-1.0 to Qwen3.5 and Gemma4 with no base-model relation recorded, so confirm the upstream terms yourself.

SAVRN Research, 2026-09-18

Model Card

By Ornith, published under mit, revision 94e431d9cc47.

Ornith-1.5-35B-A3B

Chirp Chirp! We are introducing Ornith-1.5, a major step toward building foundation models through end-to-end self-improvement.

Ornith-1.5 extends Ornith-1.0 (which was developed on top of Qwen3.5 and Gemma4 with additional continued pretraining, mid-training, and post-training) by expanding the self-improvement loop from scaffold and rollout optimization to jointly optimizing task generation, scaffold construction, and solution rollouts. Rather than relying on a fixed set of human-curated tasks and manually designed harnesses, Ornith-1.5 continuously generates new training tasks, discovers effective strategies for solving them, and improves the policy through reinforcement learning. For more details on the task, harness, and rollout reward design, please refer to our blog.

Ornith 1.5 35B-A3B

Read the full model card (1,476 words)

Configuration

Architecture
Qwen3_5MoeForConditionalGeneration
Context length (tokens)
262,144
Layers
40
Hidden size
2,048
Attention heads
16
Key/value heads
2
Head dimension
256
Vocabulary size
248,320
Experts
256
Experts active per token
8
Model type
qwen3_5_moe
Quantization
modelopt

Identity and Version

Repository
ornith-ai/Ornith-1.5-35B-A3B-NVFP4
Publisher
Ornith
Task
Text generation
Modality
Text
Library
transformers
Parameters
19.5B parameters
Languages
Not stated by the source
Revision
94e431d9cc47fa1986a7a1a4e9a80f7f118b03aa
First published
2026-08-18
Last updated
2026-08-26

Files and Weights

22 files, 23.5 GB in total. The weights are 3 files totalling 23.4 GB in safetensors.

Weights3 files · 23.4 GB
Configuration7 files · 10.5 MB
Tokenizer4 files · 22.9 MB
Documentation1 file · 29.9 KB
Other3 files · 1.7 MB
Repository4 files · 4.7 MB
Every file
FileTypeSizeSHA-256
model-00001-of-00003.safetensorsWeights10.0 GB 648fe8317148
model-00002-of-00003.safetensorsWeights10.0 GB cb5c5978721a
model-00003-of-00003.safetensorsWeights3.4 GB 8303cef7d375
config.jsonConfiguration58.2 KB —
generation_config.jsonConfiguration202 B —
hf_quant_config.jsonConfiguration42.2 KB —
model.safetensors.index.jsonConfiguration10.4 MB —
preprocessor_config.jsonConfiguration390 B —
processor_config.jsonConfiguration1.2 KB —
video_preprocessor_config.jsonConfiguration385 B —
README.mdDocumentation29.9 KB —
assets/ornith_35b_eval.pngOther709.4 KB 758a8f72b41a
assets/ornith_logo.pngOther962.4 KB 458ee0d85bae
chat_template.jinjaOther7.5 KB —
.gitattributesRepository1.8 KB —
.model_only_prunedRepository —
.quant_summary.txtRepository4.7 MB —
.sync_completeRepository295 B —
merges.txtTokenizer3.4 MB —
tokenizer.jsonTokenizer12.8 MB 5f9e4d4901a9
tokenizer_config.jsonTokenizer16.7 KB —
vocab.jsonTokenizer6.7 MB —

License and Download

License
mit
Access
Open weights, no gate
Download size
23.4 GB
Download from Ornith

Released by Ornith through its official repository on Hugging Face. Read the license.

Memory Requirements

PrecisionWeights in memory
As published23.4 GB
16-bit39.1 GB
8-bit19.5 GB
4-bit9.8 GB

Weights only, from the published parameter count; the key-value cache and runtime add to this.

Compare Ornith-1.5-35B-A3B-NVFP4

Questions About Ornith-1.5-35B-A3B-NVFP4

How much GPU memory does Ornith-1.5-35B-A3B-NVFP4 need?

About 46.9 GB at 16-bit and 11.7 GB at 4-bit: the weights (19.5B parameters) plus a working margin. A long context needs more.

What is the cheapest GPU to run Ornith-1.5-35B-A3B-NVFP4 on?

At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.

Can I use Ornith-1.5-35B-A3B-NVFP4 commercially?

Yes. Ornith-1.5-35B-A3B-NVFP4 is released under MIT License. The MIT License is a short permissive license. It permits commercial use, modification and redistribution, provided the copyright notice and permission notice are included.

What is Ornith-1.5-35B-A3B-NVFP4's context length?

262,144 tokens, from the maximum position embeddings in its published configuration.

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