Model ID
IFM/K2-Horizon-0.9B
Capabilities
K2 Horizon 0.9B
K2 Horizon 0.9B is a compact dense reasoning model designed for mathematics, coding, science, instruction following, and tool-use tasks. It features 0.9B parameters with a 28-layer decoder-only architecture using 1,536 hidden size, 32 attention heads, and 8 KV heads. The architecture employs Grouped Query Attention and SiLU-activated 5,120-dimensional feed-forward layers, with YaRN RoPE scaling extending the original 8K context to 128K tokens. Trained through multi-teacher distillation across mathematical, coding, STEM, and instruction-following domains, it provides efficient long-context reasoning for resource-constrained deployments and general-purpose language tasks.
About this model
K2 Horizon 0.9B is a compact dense reasoning model designed for mathematics, coding, science, instruction following, and tool-use tasks. It features 0.9B parameters with a 28-layer decoder-only architecture using 1,536 hidden size, 32 attention heads, and 8 KV heads. The architecture employs Grouped Query Attention and SiLU-activated 5,120-dimensional feed-forward layers, with YaRN RoPE scaling extending the original 8K context to 128K tokens. Trained through multi-teacher distillation across mathematical, coding, STEM, and instruction-following domains, it provides efficient long-context reasoning for resource-constrained deployments and general-purpose language tasks.
Model specifications and capabilities are published by the model author and reproduced here from HF Model Card (IFM/K2-Horizon-0.9B). Released under Apache 2.0. Further reading: Blog
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Hardware
B200
1,440 GB
Engine
vLLM
SGLang
Deployment code
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Requires vllm/vllm-openai:nightly or any standard vLLM image v0.30.0+ to support this model configuration.
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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