Qwen3-VL-32B H3-Compatible INT8 TensorWise Text Encoder

The target hardware assumed to be Ryzen-AI workstations (entry level starting at ~64GB vram like AMD Strix-halo or similar).

Enable Triton

For the intended accelerated INT8 TensorWise path, start ComfyUI with:

python main.py --enable-triton-backend

Container example:

python /root/ComfyUI/main.py --listen 0.0.0.0 --enable-triton-backend

On startup, confirm that ComfyUI reports that Triton was found and that the comfy-kitchen Triton backend is enabled.

Do not assume that simply having a Python package called triton installed means that ComfyUI is actually using the accelerated INT8 path.

For ROCm INT8 use, Triton 3.7 or newer is recommended.

If your ComfyUI launch command includes --disable-triton-backend, remove it.

Canonical Hugging Face release, full provenance, hashes, and supporting files:
https://huggingface.co/PuppetVision/qwen3vl-32b-minimax-h3-amd-rocm-optimized-comfy-triton

What this is

This release contains a Qwen3-VL-32B-derived INT8 TensorWise text/vision conditioning encoder for MiniMax H3 workflows in stock ComfyUI.

It is designed to substantially reduce memory use while retaining the quality and behavior of the validated H3-compatible encoder, with AMD ROCm 7-capable GPUs using Triton as the primary target audience.

This file is not a standalone Qwen chat model and does not include the MiniMax H3 diffusion model, VAE, LoRA, or sampler. It is a text-encoder file intended for MiniMax H3 ComfyUI workflows using CLIPLoader with type minimax.

H3-compatible refers to the retained Qwen topology, key layout, and conditioning contract expected by ComfyUI MiniMax H3. The released encoder weights were derived from the official Qwen/Qwen3-VL-32B-Instruct checkpoint. No MiniMax-trained tensor values were introduced into these encoder weights.

Included model

qwen3vl_32b_h3_compatible_int8_tensorwise.safetensors

• Size: 24.726 GiB

• SHA256: 438036c50abff3292ebc9d891ad8454fb5e7ff53fe6e7622643358ea9d2f2e55

• 467 linear weights quantized to INT8

• FP32 per-output-channel weight scales

• Dynamic rowwise INT8 activation quantization at runtime

• ConvRot disabled

• Requires a working comfy-kitchen Triton backend for the intended accelerated path

This is the performance-oriented H3-compatible encoder and is the only model file included in this Civitai release.

How it was built

Official source:

Qwen/Qwen3-VL-32B-Instruct

Pinned revision:

0cfaf48183f594c314753d30a4c4974bc75f3ccb

The quantization source was first reconstructed as a clean H3-compatible BF16 checkpoint directly from the official Qwen weights using a lossless structural extraction/repack:

Qwen3-VL token embedding          retained
full Qwen3-VL vision tower        retained
language layers 0..49             retained
language layers 50..63            omitted
final language-model RMSNorm      omitted
lm_head                            omitted

ComfyUI MiniMax H3 consumes the unnormalized hidden state after language layer 50, so the H3-compatible encoder intentionally ends at layer index 49 and does not contain the final language-model norm or LM head.

All 902/902 retained BF16 source tensors were verified as exact dtype, shape, and value matches to the pinned official Qwen checkpoint.

The INT8 TensorWise model in this Civitai release was then quantized from that independently reconstructed clean BF16 source.

The BF16 checkpoint itself is not included in this Civitai release. It is documented and available through the canonical Hugging Face repository linked above.

INT8 TensorWise details

format                         int8_tensorwise
ConvRot                        false
quantized linear weights       467
language-model matrices        350
vision matrices                117
stored weight scale            FP32 [out_features, 1]
activation quantization        dynamic rowwise INT8
reproducible quantization      source-dtype division on CPU

The 350 language-model matrices correspond to 50 layers × 7 projections:

q_proj
k_proj
v_proj
o_proj
gate_proj
up_proj
down_proj

The release tensor data was validated against the known-good high-quality/high-performance INT8 TensorWise baseline.

ComfyUI installation

Place the file in:

ComfyUI/models/text_encoders/

Example:

cp qwen3vl_32b_h3_compatible_int8_tensorwise.safetensors /path/to/ComfyUI/models/text_encoders/

Restart ComfyUI.

In your MiniMax H3 workflow, use CLIPLoader with:

clip_name: qwen3vl_32b_h3_compatible_int8_tensorwise.safetensors
type:      minimax
device:    default

AMD ROCm users: check your own GPU environment

Do not blindly copy GPU-specific environment variables from another AMD system.

ROCm behavior can vary based on:

• GPU architecture

• ROCm version

• PyTorch build

• kernel

• native vs containerized runtime

• whether your PyTorch build contains native support for your architecture

Identify your actual hardware and software stack first:

rocminfo | grep -m1 -E 'gfx[0-9]+'

python - <<'PY'
import torch
print('torch:', torch.__version__)
print('HIP:', torch.version.hip)
print('GPU:', torch.cuda.get_device_name(0) if torch.cuda.is_available() else 'not visible')
if torch.cuda.is_available():
    print('arch:', torch.cuda.get_device_properties(0).gcnArchName)
PY

Then research the recommended environment variables for your specific GPU architecture + ROCm/PyTorch combination.

In particular, do not blindly set HSA_OVERRIDE_GFX_VERSION. Some compatibility stacks may require an override, while architecture-aware ROCm/PyTorch builds may not.

Strix Halo example from this project

The Strix Halo build/validation environment used a dedicated ROCm 7.2.4 userspace under $HOME with:

ROCM_ROOT="$HOME/amd-rocm/rocm-7.2.4"

export ROCM_PATH="$ROCM_ROOT"
export HIP_PATH="$ROCM_ROOT"
export PATH="$ROCM_ROOT/bin:$PATH"
export LD_LIBRARY_PATH="$ROCM_ROOT/lib:$ROCM_ROOT/lib64:$ROCM_ROOT/llvm/lib${LD_LIBRARY_PATH:+:$LD_LIBRARY_PATH}"

This is an example from the validation system, not a universal prescription for every Strix Halo or AMD GPU.

Use the paths, architecture settings, and overrides appropriate for your own ROCm installation.

Provenance and exact hash

Official upstream:

Repository: Qwen/Qwen3-VL-32B-Instruct
Revision:   0cfaf48183f594c314753d30a4c4974bc75f3ccb
License:    Apache-2.0
Source shards verified: 14/14 by SHA256
Official source tensors: 1058

Clean H3-compatible BF16 quantization source:

Retained tensors: 902
Retained:         embedding + full vision tower + language layers 0..49
Omitted:          language layers 50..63 + final LM norm + lm_head
Repack numerical loss: 0.0

Civitai release artifact:

File:
qwen3vl_32b_h3_compatible_int8_tensorwise.safetensors

SHA256:
438036c50abff3292ebc9d891ad8454fb5e7ff53fe6e7622643358ea9d2f2e55

Size:
24.726 GiB

Tensors:
1836

INT8 weights:
467

FP32 scales:
467

Quantization markers:
467

For the full provenance record, validation history, BF16 reference build, and canonical release metadata:

https://huggingface.co/PuppetVision/qwen3vl-32b-minimax-h3-amd-rocm-optimized-comfy-triton

Scope and limitations

• This release contains one INT8 H3 conditioning encoder.

• It is not a full MiniMax H3 checkpoint.

• It does not contain the H3 diffusion model, VAE, LoRA, or sampler.

• The BF16 reference checkpoint is not included in this Civitai release.

• H3-specific prompt/tokenizer behavior is implemented by ComfyUI and is not contained solely in this safetensors file.

• Hardware/runtime support depends on ComfyUI, comfy-kitchen, Triton, PyTorch, ROCm, and your GPU architecture.

• Performance and behavior claims apply to the validated TensorWise recipe represented by this artifact.

License and attribution

The upstream Qwen/Qwen3-VL-32B-Instruct repository is released under the Apache License 2.0.

This artifact is a modified/repacked derivative:

• the clean BF16 source changes topology/key layout for H3-compatible conditioning;

• this release additionally applies the documented TensorWise INT8 quantization.

Qwen, MiniMax, ComfyUI, AMD, and ROCm names are used only to describe source, compatibility, and target runtime. No endorsement by those projects or companies is implied.

The canonical Hugging Face release contains the complete license, notice, provenance, BF16 reference, and checksum information:

https://huggingface.co/PuppetVision/qwen3vl-32b-minimax-h3-amd-rocm-optimized-comfy-triton

Work with me / More projects

If this model saved you VRAM, debugging time, or helped make a demanding AI workflow more practical, I build a lot more systems like this.

I’m currently looking for full-time opportunities as an AI Systems Engineer, particularly roles involving model optimization, inference systems, GPU acceleration, quantization, generative AI infrastructure, and the engineering required to make large models run reliably in real-world environments.

LinkedIn — full-time employment opportunities:
https://www.linkedin.com/in/allen-b-3a35505a/

PuppetVisionAI on YouTube — more projects and experiments:
https://www.youtube.com/@PuppetVisionAI

Website:
https://puppetvision.nl