Model ID
IFM/K2-Horizon-7B
Capabilities
K2 Horizon 7B
K2 Horizon 7B is a medium-sized dense model designed for agentic workflows, coding, reasoning, and long-context language tasks. It features 7B parameters with a 36-layer decoder-only architecture using 4,096 hidden size, 32 attention heads, and 8 KV heads. The architecture employs Grouped Query Attention and SiLU-activated 12,288-dimensional feed-forward layers, with four layer-normalization groups and a 10M RoPE theta. Supporting a native 512K-token context from midtraining onward, it provides a strong dense baseline for long-context workloads and supports faster inference through Diffusion Adapters, with intermediate checkpoints enabling capability analysis across training.
About this model
K2 Horizon 7B is a medium-sized dense model designed for agentic workflows, coding, reasoning, and long-context language tasks. It features 7B parameters with a 36-layer decoder-only architecture using 4,096 hidden size, 32 attention heads, and 8 KV heads. The architecture employs Grouped Query Attention and SiLU-activated 12,288-dimensional feed-forward layers, with four layer-normalization groups and a 10M RoPE theta. Supporting a native 512K-token context from midtraining onward, it provides a strong dense baseline for long-context workloads and supports faster inference through Diffusion Adapters, with intermediate checkpoints enabling capability analysis across training.
Model specifications and capabilities are published by the model author and reproduced here from HF Model Card (IFM/K2-Horizon-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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