A Coding Implementation to Compress and Benchmark Instruction-Tuned LLMs with FP8, GPTQ, and SmoothQuant Quantization using llmcompressor
FP8 vs GPTQ vs SmoothQuant. We benchmarked three quantization strategies on instruction-tuned LLMs—here's which cuts latency without tanking accuracy.
Why it matters
Quantization techniques directly impact model deployment costs and inference speed. This technical deep-dive benchmarks three production-ready compression strategies, helping engineers choose the right tradeoff between model size, latency, and quality for their inference workloads.
The key facts
8 to knowQuantization methods compared: FP8 dynamic, GPTQ W4A16, SmoothQuant with GPTQ W8A8
Baseline: FP16 instruction-tuned LLM
Metrics tracked: disk size, generation latency, throughput, perplexity
Tool: llmcompressor framework used for post-training quantization
Technical focus: production-ready model compression for inference optimization
Baseline: FP16 instruction-tuned model
Tool: llmcompressor for post-training quantization
Tutorial/coding implementation focus — practical deployment angle
Go to the source
MarkTechPostmarktechpost.com
Publisher excerpt: In this tutorial, we explore how to apply post-training quantization to an instruction-tuned language model using llmcompressor. We start with an FP16 baseline and then compare multiple compression strategies, including FP8 dynamic quantization, GPTQ W4A16, and SmoothQuant with GPTQ W8A8. Along the…