Qwen3-VL-8B-Instruct-abliterated Model Crashes VRAM After ComfyUI Update

Qwen3-VL-8B-Instruct-abliterated Model Crashes VRAM After ComfyUI Update

A growing number of AI image generation practitioners are reporting severe performance degradation with the Qwen3-VL-8B-Instruct-abliterated model, a specialized variant of Alibaba’s multimodal large language model. Users describe the model as completely consuming 32GB of VRAM and freezing during prompt generation tasks — a stark contrast to its previous stable operation and the normal behavior of its standard counterpart, Qwen3-VL-8B-Instruct, which runs efficiently using only 60% of the same hardware.

The issue appears to have emerged following an update to ComfyUI, a popular open-source graphical interface for Stable Diffusion workflows. According to a user report on Reddit’s r/StableDiffusion forum, the individual had previously run the abliterated variant without issue, but after updating ComfyUI to the latest version, the model began exhibiting extreme memory leakage and unresponsiveness. Both models were loaded using the same Qwen VL model loader, eliminating the possibility of a configuration mismatch as the root cause.

The term "abliterated" in the model’s name suggests it may be a community-modified version — potentially fine-tuned or pruned for enhanced prompt generation capabilities — but its current instability raises concerns about the integrity and compatibility of unofficial model variants. While the standard Qwen3-VL-8B-Instruct continues to function as expected, the abliterated variant’s behavior implies a hidden bug, corrupted weight file, or incompatible optimization layer introduced during its modification or deployment process.

Experts in AI model deployment caution that community-driven modifications, while often innovative, can introduce unforeseen memory management issues — especially when paired with evolving software ecosystems like ComfyUI. The ComfyUI update may have altered how model weights are loaded, cached, or decompressed, and the abliterated variant may rely on deprecated or non-standard tensor handling routines that no longer function under the new architecture.

Notably, the user who reported the issue emphasized that no other system changes were made aside from the ComfyUI update. This points to a software-level incompatibility rather than a hardware failure or driver issue. Similar cases have been documented in the past with other modified LLMs, such as LLaVA or Pixtral variants, where minor changes in quantization or attention layer implementation caused catastrophic memory allocation errors in newer inference frameworks.

As of now, there is no official patch or statement from Alibaba or the ComfyUI development team regarding this specific model variant. However, the community is actively investigating whether the abliterated version was built using an outdated checkpoint or contains residual tensors from a training phase that are incompatible with current inference engines. Some users have begun testing the model with different quantization settings (e.g., 4-bit vs. 8-bit) or attempting to reconvert the weights using the latest Hugging Face transformers library, but results remain inconsistent.

For practitioners relying on Qwen3-VL-8B-Instruct-abliterated for high-precision prompt engineering in AI art pipelines, the situation presents a critical bottleneck. While the standard model remains a viable alternative, the abliterated variant may have been preferred for its enhanced contextual understanding or instruction-following fidelity — qualities that could be lost when switching back.

Until a definitive fix is identified, users are advised to revert to ComfyUI’s previous stable version, avoid unofficial model variants in production environments, and monitor GitHub repositories and Reddit threads for updates from the model’s original creators. The incident underscores a broader challenge in the open-source AI community: the tension between rapid innovation and long-term model sustainability.