Cost Optimization Demo

Turning memory waste into reclaimed GPU capacity

An illustrative look at how memory-bottleneck analysis maps directly to lower GPU spend.

2.79 GB

VRAM reclaimed per node

~24%

illustrative spend reduction

memory dimensions analyzed

Stack PyTorchONNXCUDA

This is a demonstration case study using illustrative data. Figures are examples, not customer claims.

Problem

An infrastructure org runs a large inference fleet and suspects it’s over-provisioned, but “the GPUs feel busy” and nobody can point at where the waste is.

Infrastructure

Mixed PyTorch and ONNX inference workloads
Fleet of data-center GPUs
No visibility into per-kernel memory behavior

Baseline

Kernalin’s memory analyzer profiles the hottest kernels and finds a recurring pattern: 30% of reserved VRAM stranded in inactive split blocks (fragmentation), an L2 hit rate of 22% (poor cache locality), and thousands of allocations per iteration.

Optimization

The analyzer’s top action is concrete: PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to reclaim the stranded memory, plus buffer reuse and operator fusion to cut allocation churn.

Verification

Each kernel change is verified for correctness and benchmarked, so the memory win doesn’t come at the cost of a subtle numerical regression.

Result

Roughly 2.79 GB of VRAM is reclaimed per node, letting the same workload fit on smaller instances — an illustrative ~24% reduction in monthly GPU spend, with every optimization backed by a verified benchmark rather than a hopeful guess.