Optimizing VRAM Usage by Pruning Vision Components

I've done something similar with my language model! Took out image processing parts from my setup to save VRAM and saw about a 12% reduction. Did you notice any issues with the text generation speed? Mine sped up a bit, but I'm not entirely sure if it was due to the pruning.

Interesting approach! Have you considered using mixed precision training to further reduce VRAM usage? I've had some success with it — cut down my usage by another 10% on top of model pruning. It's worth a try if you want to stretch those VRAM savings even further!

That's interesting! I've managed to cut down VRAM usage by about 10% on a similar model by pruning layers selectively, not just the vision components. I'm using TensorFlow, though, so my approach might need some adaptation for PyTorch. It's amazing how much of a difference focused pruning can make on smaller setups.

That's a great strategy! I also removed vision components from a similar model and noted around 12% reduction in VRAM. Haven't seen any downsides in text processing either. Keep an eye on model accuracy though, just in case some quirky edge cases pop up.

Our team explored an alternative by dynamically loading vision components only when needed. This way, models stay adaptable without permanently increasing VRAM usage. It might be worth considering if your workload occasionally shifts between tasks.

I did a similar optimization on a project recently, but instead of just pruning vision components, I used model checkpointing to manage memory usage better. It helped when I was training multiple models simultaneously. Have you considered model checkpointing as an additional optimization strategy?

Interesting approach! Have you tried using model quantization or pruning redundant neurons instead? I found that quantization to lower precision, like INT8, can save substantial memory, though the trade-off can be a slight dip in precision. Curious if you've considered combining these methods?

I agree with your approach, especially when visual processing isn't required. I've done something similar with a ResNet model, focusing solely on text-based tasks. After pruning, there was a 10-12% reduction in VRAM, which allowed me to run additional processes concurrently. Have you tried running any specific benchmarks post-optimization to gauge performance impact more formally?

Great initiative! If you're looking for alternative methods, you might want to check out model distillation techniques. They can also help in having a leaner model by transferring knowledge to a smaller architecture. I've experimented with distillation on BERT-based models, and it helped achieve similar VRAM savings without significant performance drops. Just a thought if you're open to different approaches.

This is super intriguing! Did you notice any changes in the model's ability to handle certain text-heavy tasks, maybe in terms of processing speed or latency? I'm curious whether repurposing VRAM in this way could help manage runtime better, especially under heavy workloads.

Interesting approach! I've been using TensorFlow and usually take a different route by segmenting models based on functionality rather than simply removing parts. Curious if you've tried any comparative benchmarks versus a version with vision components? The numbers could give some quantifiable insights on the trade-offs.

I've done something similar but in a different context. When working with stable diffusion models, I've found pruning can really enhance processing speed without sacrificing quality. Not specific to vision components, but freeing up that VRAM is definitely a game changer. Have you noticed any impact on model training times with the reduced size?

I did something similar with a different model and the VRAM savings were around 10%. I also noticed a slight improvement in processing speed since there's less data for the model to handle per cycle. Did you observe any speedup in training or inference times?

That's interesting! When you say you removed the vision components, did you simply set parameters to zero, remove entire layers, or something else? I want to ensure modularity in case I need those capabilities later. A bit more detail on your approach would be really helpful!

That’s a solid 15% saving! I've worked on similar optimizations by pruning out other non-essential components, but I've never specifically targeted vision modules before. Have you noticed any changes in the throughput of the textual tasks after the adjustment? For some models I've worked with, there was a slight boost in processing speed after trimming down the architecture.

Interesting approach! When you mention editing the model's architecture, did you face any challenges with maintaining model integrity or encountering errors during inference? I'm considering stripping down models but concerned about potential edge cases when making such adjustments.

Interesting approach! I've been using ONNX to export PyTorch models and then use tools like TensorRT for further optimization. Have you considered using any of these methods to fine-tune or further compress your models? Sometimes, they can give additional VRAM and speed benefits. Would love to hear your thoughts!

I did something similar but instead of manually modifying the model architecture, I used the Hugging Face's 'transformers' library, which provides tools for model pruning. It worked pretty well for me and saved about 12% of VRAM. I'm curious if you faced any issues with maintaining the pre-trained weights' integrity?

Interesting approach! Instead of PyTorch, I used TensorFlow for a similar task and noticed around 12% VRAM savings, which aligned well with my needs. Have you considered using model distillation techniques as well? They can further reduce resource usage while maintaining performance.

I actually did a similar thing with a different model, the GPT-3.5, where I deactivated vision components to allocate more VRAM to text analysis. I saved about 10% in VRAM usage. In my experience, as long as the removed components aren't being used by your tasks, you shouldn't see reduced performance. However, sometimes there can be tiny overhead cases if other dependencies are affected.

Great strategy! I haven't worked with Qwen-3.6-35b-a3b specifically, but I've done something similar with BERT models by disabling portions of layers that aren't needed for text only tasks. Saved around 12% VRAM for my case. Didn't notice any performance lag with text, though it did speed up my training times noticeably.

I've done something similar with a different model and can confirm it's a great way to optimize VRAM for text-centric applications. Out of curiosity, have you noticed any latency improvements as a side effect of pruning the vision components?