VRAM Ghost Busting: Who You Gonna Close()?
The article discusses GPU memory management for AI workloads, explaining how to efficiently allocate and deallocate VRAM (video RAM) to prevent memory fragmentation and leakage. It introduces techniques like proper CUDA caching, tensor garbage collection, and using context managers to ensure memory is freed promptly, helping developers optimize VRAM usage in machine learning applications.
Background
H Company (hcompany.ai) is a startup building an "infinite" virtual RAM product for LLM inference, claiming to sidestep GPU memory limits. The post discusses a bug (dangling GPU memory) they found in PyTorch's CUDA caching allocator plugin system — a longstanding pain point for ML engineers — and the engineering effort needed to fix it in their own framework. For non-specialists: LLMs run on GPUs with fixed VRAM; when that fills up, models crash or slow to a crawl. H Company's claim is that by managing memory more aggressively, they can run larger models on fewer or cheaper GPUs. This matters because GPU memory is the #1 bottleneck for deploying AI models — cheaper inference means broader access.