MiniMax-H3-Image-PE-NVFP4-GGUF

MiniMax-H3-Image-PE is MiniMax's prompt-expansion model for its MiniMax-H3-Image image generator. It is fine-tuned from Qwen3.8-27B and released in FP4. It turns a short request into a detailed image description and recommends an aspect ratio, returned as JSON with expanded_caption and aspect_ratio, with the output constrained to a schema by XGrammar. It runs with thinking on by default (reasoning_effort xhigh, 4,096 max tokens including reasoning), and the reasoning is returned separately. The weights cover text-to-image expansion. For image editing, the release provides only a task prompt and a message-assembly example for a vision-language model of your choice, and the card says editing support is still under development. In editing mode the model describes what is preserved and changed and returns a target_aspect_ratio. It can be served with SGLang (0.5.23 or later) or run locally through the Modular Diffusers blocks, where the FP4 weights use about 19 GiB of GPU memory. The Diffusers path runs activations in bf16, so its outputs differ slightly from the SGLang deployment. The card gives no benchmarks. The license is the MiniMax-H3-Image Community License Agreement, not Apache 2.0, and some uses need separate written authorization from MiniMax. The Qwen3.8-27B base is Apache 2.0.

Model Files

File Name	Quant Type	File Size	File Link	Description
MiniMax-H3-Image-PE-NVFP4.gguf	NVFP4	17 GB	Link	NVFP4 quantization. Highly optimized for NVIDIA Blackwell hardware accelerators (e.g., RTX Pro 6000, RTX 50-series, B200, B300), offering native hardware-accelerated FP4 Tensor Core speeds and a strong balance of size and performance.
MiniMax-H3-Image-PE.mmproj.gguf	mmproj	931 MB	Link	Multimodal projection file. Used for vision/image capabilities.

NVFP4 Test Run

Tested MiniMax-H3-Image-PE-NVFP4.gguf on an NVIDIA RTX PRO 6000 Blackwell Server Edition GPU with 96 GB of VRAM, using llama-cli with full GPU offloading.

Performance Metrics

Metric	Value
Average Prompt Processing Speed	326.89 tokens/s
Average Generation Speed	74.94 tokens/s
Total Wall Time	77.5 seconds
Context Length	8,192 tokens
KV Cache (K)	q8_0
GPU	NVIDIA RTX PRO 6000 Blackwell Server Edition
GPU VRAM Capacity	96 GB
Peak GPU VRAM Usage	14.94 GiB
Peak GPU Utilization	96%
GPU Layers Offloaded	99 (-ngl 99)
Random Seed	42
Speculative Decoding	No
Test Runs	5

Test Shots

Shot	Test
Shot 1	Edit Prompt: Cyberpunk Portrait Relighting
Shot 2	Edit Prompt: Parisian Cafe Inpainting
Shot 3	Edit Prompt: Winter Forest Transformation
Shot 4	Edit Prompt: Frosted Glass Material Swap
Shot 5	Edit Prompt: Cinematic Medieval Armor Edit

Run Inference

./build/bin/llama-cli \
  -m /path/to/models/MiniMax-H3-Image-PE-NVFP4.gguf \
  -ngl 99 \
  -c 8192 \
  --cache-type-k q8_0 \
  --seed 42 \
  -n 8192 \
  -st \
  -p "Your prompt here"

Performance results reflect this specific hardware and inference configuration. Actual throughput may vary depending on the prompt, model, software build, and runtime settings.

Snapshots (5)

llama.cpp

LLM inference in C/C++ — https://github.com/ggml-org/llama.cpp

Quantized using llama.cpp by prithivMLmods.