Model ID
IFM/K2-Horizon-3.7B
Capabilities
K2 Horizon 3.7B
K2 Horizon 3.7B is a compact dense model designed for agentic workflows, coding, reasoning, and long-context language tasks. It features 3.7B parameters with a 36-layer decoder-only architecture using 2,560 hidden size, 32 attention heads, and 8 KV heads. The architecture employs Grouped Query Attention and SiLU-activated 10,240-dimensional feed-forward layers, with two layer-normalization groups and a 10M RoPE theta. Supporting a native 512K-token context from midtraining onward, it provides an efficient small-model baseline for long-context workloads while enabling capability analysis through released intermediate training checkpoints.
About this model
K2 Horizon 3.7B is a compact dense model designed for agentic workflows, coding, reasoning, and long-context language tasks. It features 3.7B parameters with a 36-layer decoder-only architecture using 2,560 hidden size, 32 attention heads, and 8 KV heads. The architecture employs Grouped Query Attention and SiLU-activated 10,240-dimensional feed-forward layers, with two layer-normalization groups and a 10M RoPE theta. Supporting a native 512K-token context from midtraining onward, it provides an efficient small-model baseline for long-context workloads while enabling capability analysis through released intermediate training checkpoints.
Model specifications and capabilities are published by the model author and reproduced here from HF Model Card (IFM/K2-Horizon-3.7B). 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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