Five LLM Compression Methods Cut 70B Models to 17GB as 140GB FP16 Deployments Strain Budgets
Updated
Updated · KDnuggets · Aug 28
Five LLM Compression Methods Cut 70B Models to 17GB as 140GB FP16 Deployments Strain Budgets
2 articles · Updated · KDnuggets · Aug 28
Summary
A 70 billion-parameter model in FP16 needs about 140GB of VRAM and often four A100 GPUs, turning deployment into an $80,000-$100,000 hardware problem before serving begins.
Quantization shrinks weight precision while pruning removes weights outright; used together, they can reduce that same model to roughly 17-18GB, small enough for a single high-end GPU.
The five production methods highlighted are bitsandbytes NF4, GPTQ and AWQ for roughly 4x quantization, plus SparseGPT and Wanda for about 2x pruning at 50% sparsity.
AWQ is presented as the 2026 default for GPU serving, bitsandbytes as the only option here that also supports QLoRA fine-tuning, while GPTQ remains widely supported but can lag on code tasks.
The article warns that skipping compression raises cost and latency, while careless calibration or pruning can quietly damage accuracy, making task-specific benchmarking essential.