While pre-training trillion-parameter foundation models remains the domain of mega-labs, fine-tuning reasoning models on proprietary business data is where practical AI value is generated.
Traditional Hugging Face trainer implementations suffer from massive activation memory overhead and un-fused gradient computations that cause GPU out-of-memory errors on anything larger than 8B parameters.
On NovaPod, our curated Unsloth Studio and Axolotl environments incorporate hand-written OpenAI Triton forward and backward kernels that reduce activation memory by up to 80% without any loss in model perplexity.
Combined with NovaPod's persistent `/workspace` network volumes, training checkpoints are preserved continuously, ensuring that interrupted spot runs can be resumed instantly with zero lost training epochs.
np launch \
--gpu H100_SXM \
--template novapod/unsloth-studio:2026.10 \
--disk 150GB \
--name llama-3-reasoning-lora