# Ornith-1.5-35B-A3B-NVFP4 by Ornith: Open-Weight Model Source: https://savrn.com/models/ornith-1-5-35b-a3b-nvfp4 Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## 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. | Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits | | --- | --- | --- | --- | --- | --- | | 16-bit | 39.1 GB | 46.9 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 8-bit | 19.5 GB | 23.4 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 4-bit | 9.8 GB | 11.7 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/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](https://savrn.com/ai-index/pricing/gpus), read Oct 23, 2026. [Ornith-1.5-35B-A3B-NVFP4 on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/ornith-1-5-35b-a3b-nvfp4/gpus) ## SAVRN's Notes on Ornith-1.5-35B-A3B-NVFP4 The name says 35B-A3B, the parameter field says 19.5B, and the published weights are 23.4 GB. All three can be true of a 256-expert mixture with 8 experts active per token, but it means confirming the precision you actually load before sizing hardware. Our table reads 46.9 GB needed at 16-bit, 23.4 GB at 8-bit and 11.7 GB at 4-bit — all fit one MI300X at $1.85 an hour, with the 262,144-token window the real consumer. Ornith's pitch is process, not benchmarks: a self-improvement loop that generates its own tasks, builds scaffolds and rolls out solutions, refined with reinforcement learning, extending Ornith-1.0 — which the card says was built on Qwen3.5 and Gemma4 with continued pre-training and post-training. MIT makes commercial use open. At 1M downloads a month and with no base-model terms on the page, the homework is confirming upstream obligations yourself. SAVRN Research ## 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](https://ornith.ai/ornith_1_5.html). ### Ornith 1.5 35B-A3B [Read the full model card (1,476 words)](https://savrn.com/models/ornith-1-5-35b-a3b-nvfp4/card) ## 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 | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | model-00001-of-00003.safetensors | Weights | 10.0 GB | 648fe8317148 | | model-00002-of-00003.safetensors | Weights | 10.0 GB | cb5c5978721a | | model-00003-of-00003.safetensors | Weights | 3.4 GB | 8303cef7d375 | | config.json | Configuration | 58.2 KB | — | | generation_config.json | Configuration | 202 B | — | | hf_quant_config.json | Configuration | 42.2 KB | — | | model.safetensors.index.json | Configuration | 10.4 MB | — | | preprocessor_config.json | Configuration | 390 B | — | | processor_config.json | Configuration | 1.2 KB | — | | video_preprocessor_config.json | Configuration | 385 B | — | | README.md | Documentation | 29.9 KB | — | | assets/ornith_35b_eval.png | Other | 709.4 KB | 758a8f72b41a | | assets/ornith_logo.png | Other | 962.4 KB | 458ee0d85bae | | chat_template.jinja | Other | 7.5 KB | — | | .gitattributes | Repository | 1.8 KB | — | | .model_only_pruned | Repository | | — | | .quant_summary.txt | Repository | 4.7 MB | — | | .sync_complete | Repository | 295 B | — | | merges.txt | Tokenizer | 3.4 MB | — | | tokenizer.json | Tokenizer | 12.8 MB | 5f9e4d4901a9 | | tokenizer_config.json | Tokenizer | 16.7 KB | — | | vocab.json | Tokenizer | 6.7 MB | — | ## License and Download License mit Access Open weights, no gate Download size 23.4 GB [Download from Ornith](https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-NVFP4) Released by Ornith through its official repository on Hugging Face. [Read the license](https://opensource.org/license/mit). ## Memory Requirements | Precision | Weights in memory | | --- | --- | | As published | 23.4 GB | | 16-bit | 39.1 GB | | 8-bit | 19.5 GB | | 4-bit | 9.8 GB | Weights only, from the published parameter count; the key-value cache and runtime add to this. ## Compare Ornith-1.5-35B-A3B-NVFP4 - [Ornith-1.5-35B-A3B-NVFP4 vs Qwen3.6-35B-A3B-NVFP4](https://savrn.com/models/compare/ornith-1-5-35b-a3b-nvfp4-vs-qwen3-6-35b-a3b-nvfp4) - [gpt-oss-20b vs Ornith-1.5-35B-A3B-NVFP4](https://savrn.com/models/compare/gpt-oss-20b-vs-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. ## Similar Models Model · Text generation ### [K2-Horizon-MoVA-36B-A4B-NSLM-p4mx-q4v](https://savrn.com/models/k2-horizon-mova-36b-a4b-nslm-p4mx-q4v) [Matthew P Barnson](https://savrn.com/model-publishers/txgsync) An experimental NanoSeedLM (SeedLM) compression of IFM/K2-Horizon-MoVA-36B-A4B for Apple Silicon Macs with 32 GB of unified memory. SeedLM keeps each block of 8 weights as a 16-bit seed of a linear feedback shift register (LFSR), 4 coefficients and an exponent. The GPU makes the weights again from the seed at run time. The paper used an FPGA for this. This model uses Metal kernels on the Mac GPU. This is a research proof of concept. It is not a replacement for a calibrated quantization. - Six safetensors shards (22.76 GB in total, at most 4.5 GB each) and model.safetensors.index.json. The other files are config.json, the tokenizer files of the base model and nanoseedlmk2.py, the MLX loader.… Open weights apache-2.0 19.2B parameters 524,288 tokens mlx [View model](https://savrn.com/models/k2-horizon-mova-36b-a4b-nslm-p4mx-q4v) Model · Text generation ### [Qwen3-32B-NVFP4-W4A4](https://savrn.com/models/qwen3-32b-nvfp4-w4a4) [Cezar](https://savrn.com/model-publishers/ggamecrazy) NVFP4 W4A4 quantization of (revision 9216db5781bf21249d130ec9da846c4624c16137, BF16). It was made for a controlled NVFP4-vs-MXFP4 decode benchmark on NVIDIA B200 (fp4bench), not as a general-purpose release: the NVFP4 and MXFP4 checkpoints share the model, the tool, the recipe, the calibration data and the layer coverage, and differ only in the format. Recipe: QuantizationModifier(targets="Linear", scheme="NVFP4", ignore=["lmhead"]). The plain preset recipe, with no weight-rounding optimization (GPTQ, AutoRound and the like). anon8231489123/ShareGPTVicunaunfiltered at revision 192ab2185289094fc556ec8ce5ce1e8e587154ca, at most 1024 tokens each (33208 tokens in all, seed 3). The data sets… Open weights apache-2.0 19.1B parameters 40,960 tokens [View model](https://savrn.com/models/qwen3-32b-nvfp4-w4a4) Model · Text generation ### [Qwen3.6-35B-A3B-NVFP4](https://savrn.com/models/qwen3-6-35b-a3b-nvfp4) [NVIDIA](https://savrn.com/model-publishers/nvidia) The NVIDIA Qwen3.6-35B-A3B-NVFP4 model is the quantized version of Alibaba's Qwen3.6-35B-A3B model, which is an auto-regressive language model that uses an optimized transformer architecture. For more information, please check here. The NVIDIA Qwen3.6-35B-A3B-NVFP4 model is quantized with Model Optimizer. This model is ready for commercial/non-commercial use. This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case; see link to Non-NVIDIA (Qwen3.6-35B-A3B) Model Card from Alibaba. Global Developers looking to take off-the-shelf, pre-quantized models for deployment in AI Agent systems, chatbots… Open weights apache-2.0 18.7B parameters 262,144 tokens Model Optimizer [View model](https://savrn.com/models/qwen3-6-35b-a3b-nvfp4) Model · Text generation ### [veriloop-coder-e2-nvfp4](https://savrn.com/models/veriloop-coder-e2-nvfp4) [Rodrigo Ramos Da Silveira](https://savrn.com/model-publishers/rodrigoramosrs) Apache-2.0 This repository contains an NVFP4 quantization of VeriLoop E2, an open 27B post-trained model built on Qwen3.8-27B for code, mathematics, and physics. Its core reasoning discipline is VeriLoop-Governed Recurrence (VGR): candidate states are recursively proposed, externally checked, and retained only when the protected evidence state improves without regression. Quantized by Rodrigo Ramos. Produced with NVIDIA Model Optimizer using the canonical NVFP4W4A4WEIGHTLOCALHESSIANCFG recipe: static per-block 4-bit weights (group size 16) + dynamic 4-bit activations, FP8 attention, local-Hessian calibration with MSE and fp8 scale sweep. The linearattn (Gated Delta Net) blocks and the… Open weights apache-2.0 18.3B parameters 262,144 tokens transformers [View model](https://savrn.com/models/veriloop-coder-e2-nvfp4) Model · Text generation ### [gpt-neox-20b](https://savrn.com/models/gpt-neox-20b) [EleutherAI](https://savrn.com/model-publishers/eleutherai) GPT-NeoX-20B is a 20 billion parameter autoregressive language model trained on the Pile using the GPT-NeoX library. Its architecture intentionally resembles that of GPT-3, and is almost identical to that of GPT-J- 6B. Its training dataset contains a multitude of English-language texts, reflecting the general-purpose nature of this model. See the accompanying paper for details about model architecture (including how it differs from GPT-3), training procedure, and additional evaluations. Model](https://arxiv.org/abs/2204.06745). For details about the training dataset, see the Pile paper, and its data sheet. Discord](https://discord.gg/zBGx3azzUn), and post them in #release-discussion. Please… Open weights apache-2.0 20.7B parameters 2,048 tokens transformers [View model](https://savrn.com/models/gpt-neox-20b) Model · Text generation ### [NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4](https://savrn.com/models/nvidia-nemotron-3-nano-30b-a3b-nvfp4) [NVIDIA](https://savrn.com/model-publishers/nvidia) September 2025 \- December 2025 The post-training data has a cutoff date of November 28, 2025\. The pre-training data has a cutoff date of June 25, 2025\. Nemotron-Nano-3-30B-A3B-NVFP4 is a quantized version of Nemotron-Nano-3-30B-A3B and is a large language model (LLM) trained from scratch by NVIDIA, and designed as a unified model for both reasoning and non-reasoning tasks. It responds to user queries and tasks by first generating a reasoning trace and then concluding with a final response. The model's reasoning capabilities can be configured through a flag in the chat template. If the user prefers the model to provide its final answer without intermediate reasoning traces, it can be… Open weights other 18.2B parameters 262,144 tokens transformers [View model](https://savrn.com/models/nvidia-nemotron-3-nano-30b-a3b-nvfp4) ## Ornith [All models and datasets](https://savrn.com/model-publishers/ornith-ai) ## Versions - [94e431d9cc47](https://savrn.com/models/ornith-1-5-35b-a3b-nvfp4/versions/94e431d9cc47) · current 2026-10-23 ## Explore More - [All text generation models](https://savrn.com/models/tasks/text-generation) - [All models under mit](https://savrn.com/models/licenses/mit) - [Model comparisons](https://savrn.com/models/comparisons) - [The model directory](https://savrn.com/models) - [Open model prices by host](https://savrn.com/ai-index/pricing/open-models) ## Source - Repository metadata, read 2026-10-19. - [Hugging Face record](https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-NVFP4) - [How the hub is built](https://savrn.com/model-hub/methodology)