Model ID
ornith-ai/Ornith-1.0-35B
Capabilities
Ornith 1.0 35B
Ornith 1.0 35B is a 35B parameter sparse Mixture-of-Experts model designed for agentic coding and software-engineering workflows. Built on Qwen3.5, it uses a 40-layer hybrid architecture combining linear and full attention, with a 2,048 hidden size, 16 attention heads, 2 KV heads, and 256 experts activating 8 per token. Supporting a 262K-token context window and multimodal inputs, it targets coding agents, repository-level tasks, and tool-driven workflows. Its self-improving RL framework jointly optimizes solution rollouts and their scaffolds for stronger search trajectories.
About this model
Ornith 1.0 35B is a 35B parameter sparse Mixture-of-Experts model designed for agentic coding and software-engineering workflows. Built on Qwen3.5, it uses a 40-layer hybrid architecture combining linear and full attention, with a 2,048 hidden size, 16 attention heads, 2 KV heads, and 256 experts activating 8 per token. Supporting a 262K-token context window and multimodal inputs, it targets coding agents, repository-level tasks, and tool-driven workflows. Its self-improving RL framework jointly optimizes solution rollouts and their scaffolds for stronger search trajectories.
Model specifications and capabilities are published by the model author and reproduced here from HF Model Card (ornith-ai/Ornith-1.0-35B). Released under MIT. Further reading: Blog
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Hardware
B200
1,440 GB
Engine
vLLM
SGLang
Deployment code
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Performance under load
Tokens/s (total)
Benchmarks measured by Vultr on B200 · vLLM. Throughput and latency vary with concurrency, input length and engine configuration, so treat these as a comparison baseline, not a service guarantee.
NVIDIA HGX B200
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