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Model Library
Arcee AI
K2 Horizon 375B A23BK2 Horizon 7BK2 Horizon 3.7BK2 Horizon MoVA 36B A4BK2 Horizon 32BK2 Horizon 0.9B
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  5. K2 Horizon MoVA 36B A4B

K2 Horizon MoVA 36B A4B

K2 Horizon MoVA 36B A4B is a sparse Mixture-of-Experts model designed for agentic workflows, reasoning, coding, and efficient long-context inference. It features 36B total parameters with 4B activated per token, using a 48-layer architecture with 2,560 hidden size, 32 attention heads, and 8 KV heads. The architecture combines Mixture-of-Values Attention with 100 routed experts, 8 activated per token, 1 shared expert, and 64 MoVA experts with 4 selected per token. Supporting a native 512K-token context, it uses SiLU-activated 6,144-dimensional feed-forward layers for efficient high-capability inference.

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About this model

BlogHF Model Card

K2 Horizon MoVA 36B A4B is a sparse Mixture-of-Experts model designed for agentic workflows, reasoning, coding, and efficient long-context inference. It features 36B total parameters with 4B activated per token, using a 48-layer architecture with 2,560 hidden size, 32 attention heads, and 8 KV heads. The architecture combines Mixture-of-Values Attention with 100 routed experts, 8 activated per token, 1 shared expert, and 64 MoVA experts with 4 selected per token. Supporting a native 512K-token context, it uses SiLU-activated 6,144-dimensional feed-forward layers for efficient high-capability inference.

Model Type

MoE LLM

Released

September 3, 2026

License: Apache 2.0

Parameters

36B

Model ID
IFM/K2-Horizon-MoVA-36B-A4B
Capabilities
Text GenerationInstruction FollowingReasoningMathematical ReasoningCode Generation+4

Model specifications and capabilities are published by the model author and reproduced here from HF Model Card (IFM/K2-Horizon-MoVA-36B-A4B). Released under Apache 2.0. Further reading: Blog

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Hardware

B200

1,440 GB

Engine

vLLM
SGLang

Deployment code

INFERENCE COMMAND
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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

115k92k69k46k23k011664128256512Total Tokens / secConcurrent requests
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.

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Shivam Sharma

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