Pruning & Sparsity Savings Calculator
Memory and FLOPs after unstructured, 2:4 semi-structured, or structured pruning — with realistic speedup caveats.
The pruning trap: unstructured 90% sparsity looks great on paper but GPUs can't skip scattered zeros — real speedup ≈ none without specialized kernels. 2:4 (two of every four weights zero) is what NVIDIA tensor cores actually accelerate.
Formula
About Pruning & Sparsity Savings Calculator
Pruning papers advertise 90% sparsity; deployment engineers ask what actually runs faster. The answer depends entirely on STRUCTURE: unstructured zeros compress checkpoints but GPUs still execute dense matmuls over them; NVIDIA's 2:4 pattern engages sparse tensor cores for a real ~1.6×; structured pruning (removing whole heads, channels or layers) yields a genuinely smaller dense model with proportional speedup — at the highest quality cost per parameter removed. This calculator prices all three honestly, which usually changes the decision.
How to use Pruning & Sparsity Savings Calculator
- 1Enter your values into Pruning & Sparsity Savings Calculator — sensible, domain-typical defaults are pre-filled so you see a real result immediately.
- 2The result recomputes live using the formula shown on the page; there is no button to press.
- 3Adjust any input to compare scenarios, then read the worked example to see the substituted numbers.
Why use Pruning & Sparsity Savings Calculator?
- ✓Computes Pruning & Sparsity Savings instantly in your browser — no sign-up, no upload, no server round-trip.
- ✓100% free and unlimited, with the exact formula shown: remaining = P×(1−s).
- ✓Runs entirely client-side, so every value you enter stays private on your device.
- ✓Live recompute as you type, with a worked example and authoritative references for trust.
Frequently asked questions
Why doesn't 90% unstructured sparsity speed up my GPU inference?+
Dense matmul kernels process tiles regardless of zeros; exploiting scattered sparsity needs gather/scatter that costs more than it saves below ~95% sparsity. Benefits are storage and CPU inference (where sparse kernels work). On GPUs, structure is everything.
What exactly is 2:4 sparsity?+
In every contiguous group of 4 weights, exactly 2 are zero — a constraint Ampere+ sparse tensor cores exploit for up to 2× matmul throughput (≈1.5–1.7× end-to-end). SparseGPT and Wanda can impose it one-shot on LLMs with modest perplexity cost at 50%.
How much quality does pruning cost on LLMs?+
One-shot 50% (2:4) on 7B+ models: typically +0.1–0.3 perplexity with the best methods, more visible on reasoning tasks. Structured removal of 25% of layers/heads usually needs healing fine-tuning to stay acceptable. Smaller models tolerate pruning far worse.
Pruning vs quantization — which gives more?+
Quantization, almost always: FP16→4-bit is a guaranteed ~3.3× memory and bandwidth win with mature kernels, versus 2:4's 1.6× compute win. They combine (Marlin 4-bit+2:4 kernels exist), but if you do one thing, quantize first.
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