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3 articles
This article explains how large language models can be optimized to run locally on 24GB GPUs using quantization and architectural efficiency. It explores the technical strategies behind models like Qwen3.6, Mistral Small, and DeepSeek-R1-Distill.
Learn how fused kernels and automatic mixed precision (AMP) techniques, such as those in NVIDIA Apex and PyTorch's torch.amp, can dramatically accelerate transformer training by optimizing computational efficiency and reducing memory overhead.
This article explains how to implement NVIDIA's Transformer Engine with mixed-precision, FP8 support, benchmarking, and fallback execution for optimizing transformer model performance.
