A fast GPU memory copy library based on NVIDIA GPUDirect RDMA technology

Thin, unified, C++-flavored wrappers for the CUDA APIs

Training neural networks in TensorFlow 2.0 with 5x less memory

A Toolkit for Training, Tracking, Saving Models and Syncing Results

A memory profiler for NVIDIA GPUs to explore memory inefficiencies in GPU-accelerated applications.

OpenCV & Spout C++ library. Shared GPU memory and processing at reach.

Rust embedded things running on the seL4 microkernel for the Raspberry Pi 3

A tiny, useful command-line tool to show each user gpu usage, pid under each gpu, provide more details than nvidia-smi/gpustat

Python library for memory-constrained activation checkpoint optimization, recomputation scheduling, and GPU training performance analysis.

A simple tool to find out GPU VRAM requirements for running LLMs

Accurate VRAM calculator for Local LLMs (Llama 4, DeepSeek V3, Qwen 2.5). Calculates GGUF quantization, GQA context overhead, and offloading limits

Demonstration of generating mini-batches in Tensorlfow from GPU memory.

A prefix-cache advisor for LLM serving infrastructure that recommends KV-cache capacity and eviction policies from your request traces/logs.

Budget-aware Vulkan memory allocation for V, with block suballocation, upload rings, fragmentation diagnostics, and sustained-load validation.

Dynamic GPU Layer Swapping: Train large models on consumer GPUs with intelligent memory management

A fork of Kubernetes with support of schedulable resource of NVIDIA GPU memory

Phase-manifold registration of 4D-CT: full-resolution (1 mm) deformable registration of 144 million voxels in ~2.4-3.3 GB of GPU memory, with an executable verification suite.

Detailed VRAM profiler for transformer inference with per-layer breakdown, activation analysis, and a predictive memory model that predicts VRAM with <1.2% error. Shows that FFN layers dominate static memory and that measured runtime VRAM exceeds KV-cache estimates by 2-4x.