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#PyTorch — 3 articles ✕

Models· Important· 🧪 Worth Testing

AWS and i14 ditch the KV cache with a 3B linear-attention model on Trainium2

AI research company i14 partnered with AWS to port its Laplace 3B model, a 24-layer architecture built on Gated Linear Attention (GLA) instead of standard transformer attention, onto AWS Trainium2 using native PyTorch (TorchNeuron Native). Rather than a KV cache that expands with every token, the model keeps a constant 29.5 MB recurrent state per sequence. On a single trn2.48xlarge instance, the team reached about 120,000 training tokens per second and served 11,544 output tokens per second across 1,280 simultaneous sequences through an unmodified vLLM engine.

AWS Machine Learning
Research· Important

Google and Meta bring native TPU support to PyTorch's TorchTitan training framework

Google introduced TorchTPU, a native PyTorch backend for Cloud TPUs (torch.device("tpu")), and co-developed a fork of Meta's TorchTitan trainer adapted for TPU training. Previously, running PyTorch training on TPUs meant rewriting code in JAX or relying on the older torch_xla path; now only minimal device-initialization changes are needed. The fork provides tested recipes for pre-training large dense models (Llama 3 70B/405B, Qwen 3 8B/32B), Mixture-of-Experts models (Qwen 3 235B), and diffusion transformers (FLUX.1 12B), plus a 'Graph Trainer' that captures an entire training step as one XLA program for better compiler optimization.

Google Developers
Models· Important

Google launches native PyTorch inference engines for vLLM and SGLang on TPU

Google announced new vLLM and SGLang inference engines built directly on PyTorch for TPU, developed with Inferact and RadixArk respectively. The engines run on a new TorchTPU layer that makes TPUs a native PyTorch device, replacing the previous JAX-based backend approach. Google reports a public benchmark result of 2.01 cents per million tokens in high-concurrency configurations via the SemiAnalysis InferenceX project, plus support for agentic workloads, long context, disaggregated serving and multimodal models.

Google Developers