Cohere has released a public beta of Confidential Computing on Model Vault, running on NVIDIA H100 GPUs via Microsoft Azure. The system encrypts data in use across both CPU and GPU, encrypts the request payload itself using Oblivious HTTP (OHTTP), and uses remote attestation so clients can verify the environment before sending sensitive data. If verification fails, the client refuses to send the protected request. Cohere plans to open-source the confidential inference stack and extend support to NVIDIA Blackwell (B200) GPUs toward general availability.
Ai2 has published OlmoEarth v1.3, an embedding model that condenses a full year of radar and multispectral satellite data into a single compact vector for every 10-meter patch of the planet. Alongside it comes a free browser tool, the OlmoEarth Embeddings Explorer, that lets anyone compare landscapes across years or train tiny classifiers without touching a GPU. Benchmarked against AlphaEarth Foundations and Tessera v2 across 27 tests, the new model comes out on top overall while costing far less to train than its predecessors.
IBM rolled out three updates to its Granite Speech family: a brand-new 2B speech-language model (granite-speech-5.0-2b), a revised 470M Turbo CTC model built for speed, and an improved 4.1-2B-Plus model tuned for multi-speaker recordings. Together they target high-accuracy transcription/translation, fast on-device recognition, and accurate speaker/timestamp labeling. All three remain under Apache 2.0.
IBM Research released vLLM-Hook MIA, a plugin for the open-source vLLM inference engine that lets developers observe and intervene in a model's internal states at runtime — for example to decline malicious prompts, flag hallucinated facts, or adjust tone — without disabling vLLM's fast graph-replay execution mode. According to IBM's technical report, MIA is up to 20x faster than the earlier vLLM-Hook and up to 14x faster than methods that insert code directly into the model. In tests, steering an 8B Llama or 1.5B Qwen model added only about 2% latency, and a 70B Llama model added about 6%.
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 Research announced the final milestone of its Coral NPU open hardware standard — a 32-bit RISC-V architecture with a new Matrix Execution Unit for up to 1 TOPS at roughly 10mW — plus a v1.0 open-source IREE/MLIR compiler that takes JAX, PyTorch and TensorFlow models directly to the hardware. Synaptics is launching the first commercial silicon built on this standard, including the Astra SR200 microcontroller series (400 GOPS) and new Coralboard SR and SL developer kits, with the SL now generally available via DigiKey. Google is also partnering with VeriSilicon and GlobalFoundries to bring the open RTL into production chips.
Red Hat, IBM, and Google announced llm-d 1.0, a Kubernetes-native distributed inference serving system that moves beyond proof-of-concept performance tricks into full production readiness. The release adds multi-tenant quality of service, zero-downtime operations, and agent-aware serving so the same endpoint can handle both human users and autonomous agents efficiently. In one test, llm-d's prefix-aware router captured 97% of available cache reuse on an agentic coding workload, and IBM/Red Hat ran 3,000 concurrent software-engineering agents for 5 to 10 times less cost than using the same model as a hosted service.
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.