Model library / IFM
K2-Horizon-MoVA-36B-A4B is a sparse Mixture-of-Experts language model with MoVA attention, released by IFM.
Mixture of experts, count not published
Context length in tokens, as published
Weights at the released precision
Licence declared on the repository
Overview
The model is a Mixture-of-Experts architecture employing Mixture-of-Values attention, with 36 billion parameters and an active 4 billion-parameter compute budget per token. IFM describes it as targeting agentic and reasoning benchmarks, offering frontier-class performance relative to dense and other MoE models.
In practice the model weighs 37.4 billion parameters, stored in bfloat16 format, and supports a native context window of 524,288 tokens. It is released under the Apache-2.0 licence. The repository notes the presence of experts, though the exact count is not disclosed.
Specification
| Repository | IFM/K2-Horizon-MoVA-36B-A4B |
| Publisher | IFM |
| Published | 2 September 2026 |
| Architecture | K2HorizonForCausalLM |
| Model type | k2_horizon |
| Parameters | 37,444,792,020 |
| Layers | 48 |
| Hidden size | 2,560 |
| Attention heads | 32, 8 key/value heads |
| Context length | 524,288 tokens |
| Vocabulary | 250,624 tokens |
| Weight format | bfloat16 |
| Licence | apache-2.0 |
Memory
Parameter count multiplied by the bytes each format uses. The key-value cache comes on top.
| Precision | Weights | Notes |
|---|---|---|
| bf16 | 69.7 GiB | Full precision as released |
| fp8 | 34.9 GiB | 8-bit, near-lossless on most models |
| int4 | 17.4 GiB | 4-bit, smallest footprint |
The cache costs 192 KiB per token at 16-bit, so the full 524,288-token context of one request needs about 96.0 GiB. Concurrency multiplies that number, not the weights.
Hardware
At the released precision, with 90% of the card given to the server and 2.5 GiB kept for the runtime. The context column is what is left for the cache on that setup.
| GPU | Cards needed | Free for cache | Context it holds |
|---|---|---|---|
| RTX 4090 24GB | 4 | 6.7 GiB | about 36,337 tokens |
| RTX 5090 32GB | 4 | 35.5 GiB | about 193,624 tokens |
| L40S 48GB | 2 | 11.7 GiB | about 63,644 tokens |
| A100 80GB | 2 | 69.3 GiB | about 378,217 tokens |
| H100 80GB | 2 | 69.3 GiB | about 378,217 tokens |
| RTX PRO 6000 96GB | 1 | 14.2 GiB | about 77,297 tokens |
| H200 141GB | 1 | 54.7 GiB | about 298,481 tokens |
Serving
A vLLM launch line for the shape above. Check the model card for a runtime the publisher recommends.
vllm serve IFM/K2-Horizon-MoVA-36B-A4B \ --max-model-len 32768 \ --gpu-memory-utilization 0.90
Questions
The weights take about 70 GiB at the released precision. That fits on one RTX PRO 6000 96GB, which leaves roughly 14 GiB for the key-value cache.
The repository declares apache-2.0. Read the licence text before commercial use: the name alone does not tell you what is allowed.
The configuration allows 524,288 tokens. Whether the whole window is usable depends on the memory left for the cache, which the hardware table works out per card.
This page is a reference, and Aiqre does not serve this model today. We deploy open-weight models on dedicated EU hardware on request, so if you want an OpenAI-compatible endpoint for it, email [email protected] with the model name and roughly what volume you expect.
Also in the library
K2-Horizon-375B-A23B is an open-weight sparse MiE language model from IFM with 379 B parameters, 23 B active per token and a 512 …
K2-Horizon-7B is a 9-billion-parameter decoder-only language model with a 524k token context window, released under Apache-2.0.
K2-Horizon-3.7B is a 3.7 billion-parameter dense decoder-only model with a 512 K token context, released openly by IFM.
K2-Horizon-32B-Stage1 is a 32-billion-parameter dense decoder-only model released by IFM for benchmarking agentic, coding and rea…
Hosting
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