Optimizing LLMs for Performance and Accuracy with Post-Training Quantization

Quantization is a core tool for developers aiming to improve inference performance with minimal overhead. It delivers significant gains in latency, throughput,…

Quantization is a core tool for developers aiming to improve inference performance with minimal overhead. It delivers significant gains in latency, throughput, and memory efficiency by reducing model precision in a controlled way—without requiring retraining. Today, most models are trained in FP16 or BF16, with some, like DeepSeek-R, natively using FP8. Further quantizing to formats like FP4…

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Source:: NVIDIA