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7 Drop-In Replacements to Instantly Speed Up Your Python Data Science Workflows

You’ve been there. You wrote the perfect Python script, tested it on a sample CSV, and everything worked flawlessly. But when you unleashed it on the full 10…

You’ve been there. You wrote the perfect Python script, tested it on a sample CSV, and everything worked flawlessly. But when you unleashed it on the full 10 million row dataset, your laptop fan started screaming, your console froze, and you had enough time to brew three pots of coffee before seeing a result. What if you could get massive speedups on those exact same workflows with a simple…

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Misc

Optimizing LLMs for Performance and Accuracy with Post-Training Quantization

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

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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