---

license: apache-2.0

base_model:

- IFM/K2-Horizon-MoVA-36B-A4B

base_model_relation: quantized

tags:

- gguf

- rocm

- vulkan

- rocmfp4

- strix-halo

- gfx1151

- amd

- moe

- reasoning

- k2-horizon

pipeline_tag: text-generation

---

K2-Horizon-MoVA-36B-A4B — ROCmFP4 for AMD Strix Halo (gfx1151)

Four ROCmFP4/ROCmFPX tiers of IFM/K2-Horizon-MoVA-36B-A4B,

quantised from the official BF16 GGUF and measured on a Ryzen AI Max+ 395 (Radeon 8060S, gfx1151,

128 GB unified, ROCm 7.2.4). This is the dual-expert k2_horizon MoVA architecture — 36B total

parameters, ~4B active — with two independent expert systems: a 64-way MoVA value-expert bank

(top-4) alongside a 100-way MoE feed-forward (top-8), 48 layers, hidden 2560, and it is a

reasoning model.

Every number on this card was measured on these exact files. Nothing is estimated. As an

A4B MoE, it decodes far faster than a dense 32B on the same hardware.

Which file should I use?

| file | ftype | size | decode (HIP) | prefill (HIP) | use it for |

|---|---|---|---|---|---|

| K2-Horizon-MoVA-36B-A4B-Q4_0_ROCMFP4_FAST.gguf | 103 | 18.57 GiB | 41.0 t/s | 1238 t/s | default — fastest on both axes |

| K2-Horizon-MoVA-36B-A4B-Q4_0_ROCMFP4_STRIX_LEAN.gguf | 106 | 18.67 GiB | 41.0 t/s | 1227 t/s | same speed, Q5_K embeddings |

| K2-Horizon-MoVA-36B-A4B-Q4_0_ROCMFP4_COHERENT.gguf | 102 | 19.81 GiB | 39.5 t/s | 1114 t/s | most bits in the 4-bit family |

| K2-Horizon-MoVA-36B-A4B-Q8_0_ROCMFPX_AGENT.gguf | 115 | 36.45 GiB | 28.9 t/s | 1183 t/s | 8-bit, agent/tool-call routing |

Start with FAST — fastest on both axes here, all four answer correctly. Take AGENT only if

you specifically want 8-bit weights.

Quick start — the backend is one flag

# HIP / ROCm — best prefill (long prompts, RAG, agentic)
llama-server -m K2-Horizon-MoVA-36B-A4B-Q4_0_ROCMFP4_FAST.gguf \
  -dev ROCm0 -fa on -ngl 999 --no-mmap -fit off -np 1 \
  -b 2048 -ub 1024 -t 16 --poll 100 -c 8192 --jinja

# Vulkan — SAME FILE. Only -dev changes.
llama-server -m K2-Horizon-MoVA-36B-A4B-Q4_0_ROCMFP4_FAST.gguf -dev Vulkan0 ...

⛔ You need a build with BOTH k2_horizon and the ROCmFP4 types

These declare general.architecture = k2-horizon and use the ROCmFP4/ROCmFPX tensor types

(ggml 100-119). No single public build has both yet:

• k2_horizon arch (incl. the MoVA dual-expert graph) — MBZUAI-IFM llama.cpp fork; upstream PR in progress.
• ROCmFP4/ROCmFPX types — ROCmFPX.

This repo ships k2-horizon-on-rocmfpx.patch — the arch applied onto a ROCmFPX base, the exact

tree these files were built and verified against:

git clone https://github.com/ROCmFPX/ROCmFPX.git && cd ROCmFPX
curl -fLO https://huggingface.co/kingjones777/K2-Horizon-MoVA-36B-A4B-ROCmFP4-GGUF/resolve/main/k2-horizon-on-rocmfpx.patch
git apply k2-horizon-on-rocmfpx.patch
cmake -B build -DGGML_HIP=ON -DGGML_VULKAN=ON -DAMDGPU_TARGETS=gfx1151 -DGGML_NATIVE=ON -DCMAKE_BUILD_TYPE=Release
cmake --build build -j$(nproc)

⛔ Serving note — this is a reasoner

MoVA-36B emits chain-of-thought into reasoning_content before answering. A short budget returns

HTTP 200 with empty content and the tokens in reasoning — the model thinking, not a broken

file. Give it room: ~500 tokens for short answers and tool calls, more for long-form. Verified: at

40 tokens content was empty; at 500 it answered correctly.

Head handling

Quantised at each tier's native routing with no forced --output-tensor-type override; the tiers

are posted as-built.

Reproduction block

model     : IFM/K2-Horizon-MoVA-36B-A4B  (official BF16 GGUF, K2-Horizon-36B-BF16.gguf, 74.9 GB)
            arch k2-horizon MoVA (dual-expert MoE): 36B total / ~4B active,
            mova_num_experts 64 top-4 + num_experts 100 top-8, 48 layers, hidden 2560
port      : MBZUAI-IFM/llama.cpp @ model/K2Horizon (35999d1) k2_horizon arch
            applied onto ROCmFPX fork @ 85d8f7e  (patch shipped in this repo)
quantize  : llama-quantize  <in> <out> <ftype>   (no head-type override)
            Q4_0_ROCMFP4_COHERENT (102) · Q4_0_ROCMFP4_FAST (103)
            Q4_0_ROCMFP4_STRIX_LEAN (106) · Q8_0_ROCMFPX_AGENT (115)
serve     : llama-server -dev {ROCm0|Vulkan0} -fa on -ngl 999 --no-mmap -fit off
            -np 1 -b 2048 -ub 1024 -t 16 --poll 100 -c 8192 --jinja
box       : Ryzen AI Max+ 395 / Radeon 8060S (gfx1151) / 128 GB unified, ROCm 7.2.4
verified  : dual-expert graph loads on GPU, generates correct output
            ("capital of France" -> "Paris"; "capital of Japan" -> "Tokyo")
measured  : 2026-09-04

SHA256

0f11adaada7b8b9f7aa785561f412d2de85a555fc1e2ffeddf71f972b8d576e4  K2-Horizon-MoVA-36B-A4B-Q4_0_ROCMFP4_COHERENT.gguf
5618a8e20c3300dcb38ec7217c612edcd8fb091b23aa9e024ca7063b235e80bf  K2-Horizon-MoVA-36B-A4B-Q4_0_ROCMFP4_FAST.gguf
7fea90c3aa739652d764416741e85f563ca355ab29c8a8eb89e753a37df8e79f  K2-Horizon-MoVA-36B-A4B-Q4_0_ROCMFP4_STRIX_LEAN.gguf
e653f3855ed2c0c347e14e22593d49ced9118bf2115a7717ed2e34e5e6ff722c  K2-Horizon-MoVA-36B-A4B-Q8_0_ROCMFPX_AGENT.gguf

Credits

IFM / MBZUAI — the K2-Horizon-MoVA-36B-A4B base model

(Apache-2.0) and the k2_horizon llama.cpp architecture. ROCmFP4/ROCmFPX quantisation types are the

work of ROCmFPX. This repository adds only the ROCmFP4

quant ladder, the arch-on-ROCmFPX patch, and the measurements.