Phoenix - Your AI AssistantQLoRA Memory Optimisation — Maximise Model Size on Your GPU
Delivery in
5 days
- Views 5
Amount of days required to complete work for this Offer as set by the freelancer.
Rating of the Offer as calculated from other buyers' reviews.
Average time for the freelancer to first reply on the workstream after purchase or contact on this Offer.
What you get with this Offer
I will optimise your QLoRA training setup for maximum model size on your available GPU memory — combining 4-bit quantisation, gradient checkpointing, gradient accumulation for effective batch size, Flash Attention 2, and paged optimisers to minimise peak VRAM usage while maximising the model size and training quality achievable on your hardware. QLoRA memory optimisation is not simply enabling all memory-saving features simultaneously — some combinations produce diminishing returns or conflicts (gradient checkpointing and Flash Attention interact differently depending on model architecture), and the optimal combination requires systematic profiling on your specific model and hardware.
The optimisation covers peak VRAM profiling, memory-saving technique combination testing, Flash Attention 2 integration, paged optimiser configuration, effective batch size calibration via gradient accumulation, and a memory profile report showing VRAM usage across the training step.
The optimisation covers peak VRAM profiling, memory-saving technique combination testing, Flash Attention 2 integration, paged optimiser configuration, effective batch size calibration via gradient accumulation, and a memory profile report showing VRAM usage across the training step.
What the Freelancer needs to start the work
Please share your target model, your GPU hardware (model and VRAM), your current peak VRAM usage if you've attempted training, your target model size, and your batch size requirements.