Burn 0.22 just shipped. The deep learning framework written in Rust got significantly faster to build, train, and deploy. 🦀 The headline change: backend generics are gone from user-facing code. Before: pub struct Model<B: Backend> { linear: Linear<B> } After: pub struct Model { linear: Linear } The backend is now selected through the device, not wired through every type. This breaks the compilation dependency chain that previously propagated through the entire application. Real measured rebuild times: → CNN project: 28.42s → 4.57s (6.2x faster) → Transformer project: 14.73s → 1.00s (14.7x faster) That's not a microbenchmark, those are real model-edit rebuilds on release builds. Training performance on CUDA also improved significantly: → CNN: 80% higher throughput, 49% less peak VRAM (956 MiB → 486 MiB) → Transformer: 3.6% faster, 18% less VRAM The VRAM reduction comes from adaptive memory pools that tune allocation sizes based on workload statistics, no manual configuration needed. What else is in 0.22: LoRA and QLoRA fine-tuning : attach low-rank adapters to frozen base weights, with per-parameter-group optimizer config. QLoRA keeps base weights quantized to reduce storage. ONNX export : export a Burn model to ONNX with one call. Early stage, limited operator coverage, but the direction is there. Remote compute via Iroh : run inference and training on another machine's GPU. Peer-to-peer, encrypted, authenticated. Repeated graph sequences are sent once and replayed by ID. CubeCL Environment : ship pre-warmed compilation and autotuning caches with your application. Works across native, WASM, and no_std targets. Graph capture and replay on CUDA and HIP : reuse kernel launch sequences without repeating launch preparation. NDArray and LibTorch backends are deprecated. Candle is removed. Requires Rust 1.95+. The team also hints at a native AI engine (LLM inference server + chat app + OpenAI-compatible API, all in Rust) shipping soon on top of Burn. Worth watching. 🔗 tracel.ai/blog/release-0.22.… #RustLang #MachineLearning #DeepLearning #OpenSource #GPU