---

pipeline_tag: text-generation

base_model:

• IFM/K2-Horizon-MoVA-36B-A4B

library_name: llama-cpp

tags:

• k2-horizon
• 36b
• moe
• mova
• open-weights
• ifm
• gguf
• nvfp4

language:

• en

license: apache-2.0

license_name: apache-license-2.0

license_link: https://apache.org/licenses/LICENSE-2.0

---

K2-Horizon-MoVA-36B-A4B NVFP4 — GGUF

NVFP4 GGUF conversions derived from the K2-Horizon-MoVA-36B-A4B model family. This repository provides NVFP4 (NVIDIA 4-bit floating-point) GGUF files, making the model usable with llama.cpp and other GGUF-compatible inference engines.

Model Overview

K2-Horizon-MoVA-36B-A4B is the sparse member of the K2-Horizon family: a Mixture-of-Experts model with Mixture-of-Values attention (MoVA) that stores 36B parameters and runs 4B per token. It delivers frontier-class results with high efficiency in agentic and reasoning tasks.

Model Architecture

| Property | Value |

| :----------------------- | :------------------------------------ |

| Architecture | MoE with Mixture-of-Values (MoVA) |

| Total Parameters | 36B |

| Activated Parameters | 4B |

| Context Length | 512K tokens (524,288) |

| Supported Modalities | Text |

| Type | Sparse (MoE) |

Benchmark Results

| Benchmark | K2-Horizon-MoVA-36B-A4B | Nemotron 3 Ultra | Nemotron 3 Super | G9v3-39A5B | Qwen3.6-35B-A3B | Muse Glimmer-30B | Gemma 4 31B-it |

| :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: |

| # Params | 36B | 550B | 120B | 39B | 35B | 30B | 31B |

| # Active Params | 4B | 55B | 12B | 5B | 3B | 30B | 31B |

| Agents | | | | | | | |

| tau3-Banking | 26.8 | 14.2 | 10.3 | 22.1 | 9.3 | 23.5 | 14.8 |

| Coding | | | | | | | |

| Terminal-Bench 2.1 | 58.6 | 53.9 | 38.6 | 32.6 | 44.9 | 51.7 | 43.4 |

| SciCode | 38.9 | 39.9 | 36.0 | 34.0 | 35.8 | 43.6 | 43.4 |

| Scientific Reasoning | | | | | | | |

| Humanity's Last Exam | 25.2 | 28.4 | 20.8 | 17.5 | 22.2 | 22.0 | 23.6 |

| GPQA Diamond | 80.8 | 86.7 | 80.0 | 80.5 | 84.1 | 83.5 | 85.7 |

| CritPt | 2.1 | 3.1 | 3.1 | 0.3 | 0.3 | 2.6 | 1.4 |

| General | | | | | | | |

| AA-LCR | 66.3 | 71.0 | 60.3 | 62.0 | 66.7 | 80.0 | 68.3 |

| AA-Omniscience Acc. | 18.8 | 22.6 | 24.3 | 14.9 | 18.8 | 27.0 | 20.0 |

| AA-Omniscience Non-Hall. | 69.2 | 70.3 | 13.0 | 87.0 | 49.5 | 18.1 | 15.0 |

Scores in %. Bold marks the best score in each row. Results are sourced from the original K2-Horizon-MoVA-36B-A4B model description.*

GGUF Files

| File | Format | Description |

| :--- | :--- | :--- |

| k2-horizon-mova-36b-a4b-nvfp4.gguf | NVFP4 | NVFP4 quantized model |

Usage

llama.cpp (CLI)

# Run inference
./llama-cli \
  -m k2-horizon-mova-36b-a4b-nvfp4.gguf \
  --temp 1.0 --top-p 0.95

llama-server (OpenAI-compatible API)

# Start the server
./llama-server \
  -m k2-horizon-mova-36b-a4b-nvfp4.gguf \
  --port 8080

Key Features

• High Efficiency MoE. Achieves frontier-class results with only 4B active parameters, outscoring many dense models significantly larger.
• 512K Context. Native 524,288-token context window supported from midtraining onward.
• NVFP4 Quantization. Optimized for NVIDIA Blackwell and compatible hardware for high-performance inference.
• Reasoning Capabilities. Strong performance in scientific reasoning, coding, and agentic tool use.

Citation

@misc{k2horizon2026,
  title  = {Introducing K2 Horizon: Frontier Performance, Radically Open},
  author = {{IFM Team}},
  year   = {2026},
  url    = {https://ifm.ai/blog/k2/},
}