Has anyone tried training large models on consumer hardware? My experience with gradient checkpointing + ZeRO

Have you tried using quantization techniques along with this setup? While it might lower the model accuracy slightly, it's another potential approach to reduce memory usage, especially in early experimentation phases.

I've tried something similar on my GTX 1080 Ti, though not as intense as a 70B parameter model. I'm using FP16 instead of bfloat16 because of hardware limitations, and combined with ZeRO Stage 2, I managed to train smaller models effectively. For me, activation offloading helped save roughly 10GB of VRAM during peaks. Curious about the practical differences you've seen between mixed precision options – have you tested pure FP16 out of interest?

I've also been experimenting with large models on a single GPU—GTX 3090 in my case. ZeRO and mixed precision are huge game-changers. Haven’t tried activation offloading yet, though. What kind of CPU are you using for offloading, and how much does it impact your training speed?

Check out FairScale's FSDP implementations as well. I've had success with it on a much smaller scale model. I found squeezing model inference into 4GB VRAM with some degree of sacrifice in speed, but it worked! Would love to exchange thoughts on how offloading affects model convergence—do you notice it impacting the final model's performance?

Thanks for sharing your setup! I'm curious about the reliability of using CPU activation offloading. Have you encountered any issues with latency or increased training time because of this? I'm considering trying it out, but want to understand the potential bottlenecks beforehand.

I've also been using DeepSpeed on my single GPU setup! Instead of using activation offloading, I've been compressing my model weights and gradients with a technique called 'weight quantization.' It reduced memory footprint significantly, though it does impact model precision a bit. Curious if anyone else has tried quantization on large models?

Nice work! I tried something similar last month but gave up after my training kept getting killed by the OOM killer. Your memory breakdown is really helpful - I think I was underestimating how much the optimizer states blow up even with partitioning. One thing that helped me squeeze out a bit more memory was using gradient accumulation with a ridiculously small micro-batch size (like 1) and accumulating over 64+ steps. Also found that pinned memory allocation can sometimes help with the CPU<->GPU transfers, though YMMV. The 0.3 tok/s is actually not terrible for experimentation - beats waiting in Lambda Labs queues!

This is awesome! I've been trying similar stuff on my 3090 but kept running into OOM issues. Quick question - how much system RAM are you using for the offloading? I only have 32GB and wondering if that's my bottleneck. Also, are you using any specific batch size tricks or just micro-batching down to size 1?

This is good to know! How are you managing the gradient checkpointing in terms of time complexity? I tried something similar but found the slowdown too significant for my purposes, like it was increasing the training time by over 50%. Are there specific libraries you're using to optimize that aspect?

Thanks for sharing! I'm curious about how you handle data loading. Do you keep all the training data in RAM, or does that get offloaded too? With such large models, I find that data pipeline efficiency can become a potential bottleneck.

Interesting approach! I've found that using tensor parallelism alongside these techniques can sometimes improve performance slightly on consumer hardware. It might complicate the setup, but it's worth exploring if you're hitting a wall with your current configuration.

Nice work managing that on a single GPU! I've experimented with LLaMA fine-tuning and found that beyond 33B parameters, it's a real challenge without significant compromises. One trick I used was sharding the optimizer states across local storage to lessen the immediate GPU memory demand, but it did slow things down a bit due to IO latency. How do you handle activation offloading efficiently in your setup?

Really interesting! I haven't tried on such a large model, but I've experimented with a similar approach using MeTA's Hydra, which allows dynamic switching between CPU and GPU for better memory handling. It might not match ZeRO in efficiency, but it could be worth exploring alongside what you've done. Would love to know how it stacks up against DeepSpeed if anyone's compared them.

I'm amazed you're able to get that working on a 4090! I've been using a similar setup but with a 3090, and the biggest issue I face is managing the heat output. I've had to rig an external fan to keep things stable. Also, I find using a page file on an NVMe SSD helps a bit when activation offloading becomes bottlenecked by RAM limits.

Impressive breakdown! I'm curious, how often do you encounter issues with PCIe bandwidth when offloading activations to the CPU? I tried a similar setup and found the transfer times added noticeable overhead. Considering investing in a faster PCIe or an NVLink setup, but not sure if it’ll be worth it.

I've tried something similar on my 3080 and while it's definitely challenging, using ZeRO and gradient checkpointing has made it somewhat manageable for smaller models. How are you handling high disk I/O with activation offloading though? I noticed my disk becomes a bottleneck pretty quickly.

I've dabbled with training large models on a similar setup using a combination of model parallelism and tensor slicing. By offloading parts of the model in chunks to my CPU and utilizing PyTorch Distributed, I managed to train a 30B parameter model, though it required some serious synchronization work to prevent bottlenecks. Your approach with ZeRO and gradient checkpointing sounds more efficient for my setup. What's your memory bottleneck like?

Great to hear success stories with large model training on consumer-grade hardware! I've been attempting something similar but with an RTX 3080. I'm using Deepspeed's ZeRO stage 2 because my GPU memory is a bit more constrained. It's incredible how gradient checkpointing and bfloat16 have allowed me to manage larger models than I initially thought possible. I haven't tried activation offloading to the CPU yet, so that's next on my list. How did you find the performance trade-offs with that?

I'm doing something similar with my RTX 3090 using just PyTorch and the DeepSpeed library. I haven't attempted a model as large as yours, but I've managed up to 25B parameters. The key for me was aggressively using both model parallelism and CUDA kernels for some of the compute-heavy operations. Seeing a memory footprint much smaller than what you're dealing with, thanks to ZeRO R&D. Would love to know if you've noticed any performance trade-offs with activation offloading!

Wow, that's pretty impressive for a single RTX 4090! I've been working with slightly smaller models, around 10B parameters, using similar techniques on my GTX 3090. One thing I found helpful was taking frequent checkpoints to avoid redoing a lot of lost work in case of failure. Curious if you're encountering any stability issues with bfloat16 though?

I've been attempting something similar on my AMD RX 7900 XTX! I use pretty much the same techniques but I've also introduced a custom CUDA extension to handle some specific tensor operations more efficiently. I’ve noticed an increase to around 0.5 tokens/sec. Has anyone tried using activation compression? Curious if it’d play nicely with these techniques.

I've successfully trained 13B models on my setup using a dual RTX 3070 configuration. In my case, leveraging tensor rematerialization in addition to your mentioned methods really helped to further reduce memory pressure. Also, NVIDIA's Triton for custom kernels sped up a few specific ops, which was a nice bonus.

I experimented with training on a 3090 a while back, utilizing some similar techniques, but I ended up turning to model parallelism instead to split the model across multiple GPUs. This worked for me because I had access to a couple of extra cards borrowed from a friend. Curious if anyone else has played around with model parallelism solutions like Megatron-LM for a single machine setup and how it compared to what's being discussed here.

I'm using a similar setup on my RTX 3090, focusing mainly on the ZeRO optimizer with DeepSpeed. I'm seeing slower throughput, around 0.2 tokens/sec, but it's amazing that we can even attempt these large models on consumer GPUs! I'm curious about your activation offloading strategy – are you using a specific library for that, or just manual CPU memory management?

Wow, that's impressive! I've only ever managed to push 20B models on my 3090 using some of these techniques. Your breakdown really helps, especially with activation offloading. I haven't tried ZeRO with DeepSpeed yet, but it sounds like a game changer. How long does a typical training session last for this setup?

Wow, that's impressive! I'm running a similar setup on an RTX 3090 and facing some limitations, especially with activation offloading. I haven't tried using ZeRO Stage 3 yet — do you find the added complexity is worth the memory savings compared to Stage 2? Would love to hear your thoughts on the trade-offs.

I've had luck with utilizing the PyTorch memory profiler to really understand what parts of the model are consuming the most memory and adjusting accordingly. One thing I found helpful was manually pruning parts of the model that weren't crucial to the fine-tuning task.

I've been using PyTorch with model parallelism to handle large models on a single GPU. Instead of ZeRO, I've tried using NVIDIA's Apex library for mixed precision, which simplifies things for me. It sounds like your approach is very modular and methodical, though. Have you considered integrating model parallelism, or is DeepSpeed taking care of all distribution needs?

Very cool to see your approach! Have you looked into using swap space on an NVMe SSD for additional virtual memory? I found it can be a lifesaver when offloading, although it puts wear on the SSD. I'm curious if there's a noticeable hit on your training speed when activation offloading to CPU kicks in.

I've been tinkering with a similar setup but on an RTX 3090. I can relate to the struggle of squeezing out performance from consumer hardware for these large models. I've found that tensor decomposition techniques like QLORA have helped reduce the model size notably, without sacrificing too much performance. Have you explored any form of layer pruning to cut down memory usage further?

I've been using a similar setup with my own RTX 3090, but I've implemented some additional strategies. Have you considered using EfficientNet's layer-wise adaptive compression during gradient checkpointing? It can reduce memory usage a bit more without sacrificing too much on performance. Also curious if you've tried playing with any custom learning rate schedules to mitigate the slower training due to memory constraints.

That's really impressive! I've also been working with a similar setup on my 3090. I can confirm that gradient checkpointing and activation offloading make it feasible to fine-tune large models on consumer hardware. However, ZeRO Stage 3 has been a bit tricky for me to set up. Any tips on configuring it properly with DeepSpeed?

I've been playing with something similar using an RTX 3080. With 10GB less VRAM, I had to get creative. Gradient checkpointing is essential for me, too. I also played around with flash attention, which surprisingly helped reduce memory usage a bit more. For me, the bottleneck is usually when optimizer states try to fit in available GPU memory. Using ZeRO was a game changer, though, maintaining decent performance without crashing every few minutes.

I've been experimenting with a 30B model on a 3090, and I definitely agree that gradient checkpointing is a game changer. It allows for reducing the active memory footprint considerably. I haven't tried ZeRO yet, but your experience is convincing me to give it a shot. For now, CPU offloading has been my main strategy to get through the memory bottleneck.