Fine-tuning can run out of memory before available compute is fully used.
A training job needs memory for more than model weights. It also needs gradients, optimizer state, activations, intermediate data, input sequences, and batches.
Increase model size, sequence length, or batch size, and fast memory can disappear before the system has exhausted its compute capacity.
Pascari aiDAPTIV™ can help make memory-constrained fine-tuning practical by extending available capacity for model and training state beyond GPU memory alone.
In supported training integrations, aiDAPTIV can stage model and training state through GPU memory, system memory, and cache memory rather than requiring the complete working set to remain in GPU memory at once.
Exact handling varies by model, runtime, training method, hardware configuration, sequence length, and batch size. Depending on the workload, data may be retained, moved through the memory system, or recomputed to balance capacity and execution time.
Explore larger candidate models or more demanding fine-tuning configurations while keeping data in the environment you control.
Parameter-efficient tuning can still run into memory limits around model size, sequence length, and batch configuration.
Keep domain data local when cloud training is not appropriate.
Fine-tuning performance depends on the model, runtime, training method, hardware configuration, sequence length, batch size, and data movement through the memory tiers.
The value is added training capacity when GPU memory alone is the limiting factor.
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