Model ID
ornith-ai/Ornith-1.0-9B
Capabilities
Ornith 1.0 9B
Ornith 1.0 9B is a 9B parameter dense multimodal model designed for agentic coding and software-engineering workflows. Built on Qwen3.5, it uses a 32-layer hybrid architecture combining linear and full attention, with a 4,096 hidden size, 16 attention heads, and 4 KV heads. Supporting a 262K-token context window, the model is optimized for coding agents, repository-level tasks, and tool-driven workflows. Its self-improving RL framework jointly optimizes solution rollouts and the scaffolds that guide them, enabling stronger search trajectories and higher-quality solutions.
About this model
Ornith 1.0 9B is a 9B parameter dense multimodal model designed for agentic coding and software-engineering workflows. Built on Qwen3.5, it uses a 32-layer hybrid architecture combining linear and full attention, with a 4,096 hidden size, 16 attention heads, and 4 KV heads. Supporting a 262K-token context window, the model is optimized for coding agents, repository-level tasks, and tool-driven workflows. Its self-improving RL framework jointly optimizes solution rollouts and the scaffolds that guide them, enabling stronger search trajectories and higher-quality solutions.
Model specifications and capabilities are published by the model author and reproduced here from HF Model Card (ornith-ai/Ornith-1.0-9B). 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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