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“Open Source” — 4 articles ✕

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
Models· Important

Reflection releases Beam, a 501B open-weight MoE model with 1M-token context

Reflection, a new US AI lab, has launched Beam, its first model, as an open-weight mixture-of-experts system with 501B total parameters (23B active per token) and a 1M-token context window, released under Apache 2.0. The model is built for coding and agentic use cases, ships with a custom Rust-based inference harness, and has day-0 support in transformers, vLLM and SGLang. Reflection reports strong results on coding, reasoning and tool-calling benchmarks across BF16, FP8 and NVFP4 checkpoints.

Hugging Face
Models· Important· 🧪 Worth Testing

IBM combines transcription, decision-making and chat in a single Granite Switch model

IBM has released Granite Switch 4.1 in 8B and 30B sizes, a modular system that runs chat, decision-making and speech transcription from one hosted endpoint instead of separate models. The update adds a general-purpose Decision adapter, a specialized Guardian adapter for safety-related classifications, and an embedded ASR model, Granite-Speech-5.0-Turbo-CTC, which tops the OpenASR Leaderboard. Despite bundling three capabilities, the combined model uses roughly the same VRAM as the base model alone.

Hugging Face
Models· Important

Mistral releases Timestral, a time-series forecasting model family

Mistral AI has launched Timestral, its first family of time-series foundation models, available in four sizes from 5M to 209M parameters. The largest model ranks first on all three major public forecasting benchmarks (GIFT-Eval, TIME, fev-bench), beating previous state-of-the-art models that have more parameters. The models produce probabilistic, multivariate forecasts, support past and known-future covariates, and run efficiently on CPUs without needing a GPU.

Mistral AI