Model ID
ornith-ai/Ornith-1.5-397B
Capabilities
Ornith 1.5 397B
Ornith 1.5 397B is the flagship 397B parameter Mixture-of-Experts model, designed for advanced coding and agentic workloads. Its end-to-end self-improvement approach jointly optimizes task generation, scaffold construction, and solution rollouts through reinforcement learning. The Qwen3.5-based architecture features 60 layers with hybrid linear and full attention, 32 attention heads, 2 KV heads, and 512 experts with 10 activated per token. The model supports a 262,144 token context window.
About this model
Ornith 1.5 397B is the flagship 397B parameter Mixture-of-Experts model, designed for advanced coding and agentic workloads. Its end-to-end self-improvement approach jointly optimizes task generation, scaffold construction, and solution rollouts through reinforcement learning. The Qwen3.5-based architecture features 60 layers with hybrid linear and full attention, 32 attention heads, 2 KV heads, and 512 experts with 10 activated per token. The model supports a 262,144 token context window.
Model specifications and capabilities are published by the model author and reproduced here from HF Model Card (ornith-ai/Ornith-1.5-397B). 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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