What INT8 ConvRot Actually Proves in Local Generation

TL;DR

This is a review of public evidence on INT8 ConvRot, Q8, FP8, MXFP8, and row-wise INT8.
It matters because quantization can affect quality, speed, VRAM use, and whether a workload runs.
You should run same-seed A/B tests on your own model, prompts, and resolution before choosing.

Example: A small team tries several quantization formats on one local workflow. The team keeps prompts and settings fixed. It then compares image quality, runtime behavior, and memory stability before picking a format.

Current status

By contrast, some community rankings circulate without official reproducible support. One example is the claim that Q8 leads and INT8 ConvRot follows. On Hugging Face, INT8 W8A8 ConvRot models such as Flux2-Dev-INT8-W8A8-Convrot-Model are available. There is also information that they are packaged for ComfyUI-INT8-Fast. That suggests a practical deployment path. It does not show validated superiority.

Analysis

If the goal is simply to run on older GPUs or tight memory budgets, the criteria change. Execution stability, VRAM headroom, and pipeline compatibility can matter more. In that context, practical packages such as INT8 ConvRot may become more useful. Even so, caution still helps. Reported speed gains in community posts were not directly verified here. In video workloads, attention, VAE, scheduler, and I/O can all bottleneck performance. Because of that, gains from changing only quantization can vary by model and pipeline.

Another risk is treating all 8-bit formats as the same class. FP8, MXFP8, INT8, and Q8 have similar labels. Their error distributions and runtime behavior can still differ. So conclusions may not transfer cleanly across models. The same limit applies to the RTX 3090 comparison. Those results fit that setup. They may not extend cleanly to different pipelines or kernel optimizations.

Practical application

A realistic selection rule is fairly simple. Separate the quality-first group from the completion-rate-first group. The first group should compare Q8-type, FP8-type, and INT8-type formats side by side. It should use the same prompt set. It should use the same seed. The second group should first check model loading, time to first sample, and memory stability during continuous generation. In production, crash-free batch execution can matter more than a small quality gain.

For a local image workflow, a team can fix 20 representative prompts. It can also fix the seed. Then it can generate samples at the same resolution for each format. After that, it should check more than average processing time. Worst-case stalls also matter. For video generation, one scene is not enough. Teams should record frame consistency, cumulative error across long sequences, and conflicts with attention optimization.

Checklist for Today:

Generate Q8-, FP8-, and INT8-family samples with the same model, prompts, seed, and resolution, then record quality.
Log load time, peak VRAM, and failure status during continuous runs, not only generation time.
Promote formats only after they pass your own workload checks for quality, stability, and compatibility.

FAQ

Q. Is INT8 ConvRot higher quality than FP8?

Based on current public evidence, that seems hard to state clearly. In the confirmed paper, the setup used an RTX 3090 and 200 prompts. It also used same-seed bootstrap CI conditions. Under that setup, INT8 W8A8 and FP8 were not clearly separated on quality.

Q. Then is Q8 the safest choice?

That view does circulate in the community. Within this review, no official validation under identical conditions was confirmed. So direct comparison on your own model and hardware seems safer than adopting that ranking.

Q. What is the most important criterion for users of older GPUs?

First, whether the model loads. Second, whether memory remains stable during generation. Third, whether the resulting quality is acceptable. In older GPU environments, those three checks can matter more than a score table.

Conclusion

INT8 ConvRot looks like a practical candidate. At this stage, it appears to be one option among several. Official reproducible comparisons are still partial. Community quality and speed rankings may not be strong decision rules yet. A careful approach is to measure under the same conditions yourself. Then choose using quality, speed, and VRAM together.

Aionda