Introducing FlashPack: Lightning-Fast Model Loading for PyTorch
The FlashPack package dramatically speeds up PyTorch model loading by flattening all weights into a single contiguous stream, memory-mapping the file, and overlapping disk, CPU, and GPU operations with CUDA streams. This approach yields 3-6× faster loading compared to traditional methods like loadstatedict(), reducing GPU idle time and improving overall performance, especially on syste...
https://blog.fal.ai/introducing-flashpack-lightning-fast-model-loading-for-pytorch
The FlashPack package dramatically speeds up PyTorch model loading by flattening all weights into a single contiguous stream, memory-mapping the file, and overlapping disk, CPU, and GPU operations with CUDA streams. This approach yields 3-6× faster loading compared to traditional methods like loadstatedict(), reducing GPU idle time and improving overall performance, especially on syste...
https://blog.fal.ai/introducing-flashpack-lightning-fast-model-loading-for-pytorch
fal.ai Blog | Generative AI Model Releases & Tutorials
Introducing FlashPack: Lightning-Fast Model Loading for PyTorch
When using machine learning models in the real world, performance isn’t just about how fast your GPU can crunch numbers — it’s also about how quickly you can get your model there. Every second spent waiting on a checkpoint to load is a second your GPUs sit…
The Building Blocks of Agentic AI: From Kernels to Clusters
The PyTorch Native Agentic Stack is a scalable, PyTorch-integrated framework designed for building and deploying autonomous AI agents across thousands of GPUs. It simplifies complex distributed reinforcement learning workflows by orchestrating large-scale models, providing abstractions for services, fault tolerance, and efficient state management to accelerate AI research and deployment.
https://ai.meta.com/blog/introducing-pytorch-native-agentic-stack
The PyTorch Native Agentic Stack is a scalable, PyTorch-integrated framework designed for building and deploying autonomous AI agents across thousands of GPUs. It simplifies complex distributed reinforcement learning workflows by orchestrating large-scale models, providing abstractions for services, fault tolerance, and efficient state management to accelerate AI research and deployment.
https://ai.meta.com/blog/introducing-pytorch-native-agentic-stack
Meta AI
The Building Blocks of Agentic AI: From Kernels to Clusters
At PyTorch Conference 2025 in San Francisco, we unveiled five new projects spanning kernel languages, distributed systems, reinforcement learning, agentic frameworks, and edge AI deployment.