Model ID
ornith-ai/Ornith-1.5-9B
Capabilities
Ornith 1.5 9B
Ornith 1.5 9B is the lightweight member of the Ornith-1.5 family, designed for efficient single-GPU deployment and edge use through its quantized mobile variant. It is a 9B dense model built around an end-to-end self-improvement approach that jointly optimizes task generation, scaffold construction, and solution rollouts through reinforcement learning. Its Qwen3.5-based architecture uses 32 layers with hybrid linear and full attention, 16 attention heads, and a 4-head KV configuration. The model supports a 262,144 token context window, making it suitable for efficient deployment across demanding long-context workloads.
About this model
Ornith 1.5 9B is the lightweight member of the Ornith-1.5 family, designed for efficient single-GPU deployment and edge use through its quantized mobile variant. It is a 9B dense model built around an end-to-end self-improvement approach that jointly optimizes task generation, scaffold construction, and solution rollouts through reinforcement learning. Its Qwen3.5-based architecture uses 32 layers with hybrid linear and full attention, 16 attention heads, and a 4-head KV configuration. The model supports a 262,144 token context window, making it suitable for efficient deployment across demanding long-context workloads.
Model specifications and capabilities are published by the model author and reproduced here from HF Model Card (ornith-ai/Ornith-1.5-9B). 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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