OpenAI has released initial performance results for Jalapeño, its first custom inference chip, demonstrating significant gains in speed and power efficiency compared to existing commercial systems. The company states that the new architecture allows for higher throughput and lower latency simultaneously, addressing a common trade-off in current hardware.
Nvidia researcher N. Horrocks introduces extended detector error models (EDEMs) designed to handle fault-tolerant quantum programs with stabilizer circuits and stochastic Pauli faults. These models allow error analysis to compose in sequence and parallel, mirroring the structure of physical circuit realizations.
Researchers propose Onyx, a cost-efficient approach to disk-oblivious approximate nearest neighbor (ANN) search that balances bandwidth and access count by inverting the design of state-of-the-art ORAM-ANN systems.
Researchers introduce Privatar, a framework that offloads avatar reconstruction from headsets to untrusted local devices while safeguarding against data interception. The system leverages domain-specific knowledge of avatar reconstruction to achieve provably private offloading with minimal performance cost.
Researchers from NVIDIA have introduced GPIR, a system that accelerates Private Information Retrieval (PIR) on GPUs by addressing the high server-side computation and memory traffic inherent in lattice-based protocols. The system employs a stage-aware hybrid execution model that dynamically switches between operation-level and stage-level kernels to maximize on-chip data reuse.
A new study presents a unified system-level characterization of secure multiparty computation (MPC) and fully homomorphic encryption (FHE) for privacy-preserving machine learning inference. The work evaluates two MPC variants—arithmetic/binary sharing conversion and function secret sharing—alongside FHE across multiple CNN and Transformer models.
A position paper by NVIDIA researchers outlines a vision for securing AI agents against indirect prompt injection attacks, where malicious instructions embedded in untrusted data trigger dangerous actions. The authors argue that effective defense requires moving beyond isolated model fixes to comprehensive system-level architectures.
Nvidia has officially announced its intention to acquire the machine learning platform Hugging Face in a deal valued at 12.9 billion dollars.
Researchers have developed a post-training pipeline for large language models that enables them to achieve gold-medal performance in competitive programming. By combining supervised fine-tuning, reinforcement learning, and a new feedback-driven strategy called GenCorrect, the system outperforms top human contestants on the IOI 2026 problem set.
NVIDIA has developed post-training pipelines for its Nemotron-3-Nano-CC and Nemotron-3-Ultra-CC language models to excel in competitive programming. By combining large-scale problem curation, synthetic reasoning traces, supervised fine-tuning, and reinforcement learning, the team created specialized systems capable of high-level algorithmic reasoning.
NVIDIA has developed a post-training pipeline for its Nemotron-3 language models, enabling them to achieve gold-medal level performance in the International Olympiad in Informatics (IOI). The approach combines large-scale problem curation, synthetic reasoning traces, supervised fine-tuning, and reinforcement learning.
Nvidia has agreed to acquire the machine learning platform Huggingface in a deal valued at $12.9 billion.
Nvidia has agreed to acquire the open-source model repository Hugging Face in a deal valued at $12.9 billion.
Nvidia has agreed to buy the open-source model repository Hugging Face for $12.9 billion, according to a report from The Information.
Huawei has paused its international expansion of artificial intelligence chips because domestic demand within China is exceeding the available supply. This strategic shift means that competitors AMD and Nvidia no longer face immediate pressure from Huawei's global market entry.
NemotronLabs has introduced VoiceChat, an open-weight full-duplex speech-to-speech model designed to integrate listening, transcription, reasoning, and speaking within a unified streaming architecture. The system combines a streaming speech encoder and decoder-only language model with parallel specialized output streams for agent text and structured function calls, alongside an auxiliary RNN-T branch for incremental user transcription.
NemotronLabs has introduced NemotronLabs VoiceChat, an open-weight full-duplex speech-to-speech model that integrates native tool-calling capabilities within a unified streaming architecture. The system combines a streaming speech encoder and decoder-only language model with parallel output streams for agent text and structured function calls, alongside an auxiliary RNN-T branch for incremental transcription and a streaming TTS decoder.
The kernels team at Together AI has optimized the ThunderKittens library to leverage new features in the NVIDIA Vera Rubin NVL72 platform, specifically targeting improved performance for NVFP4 and FP8 matrix multiplications.
A user has published code and weights for running NVIDIA's 64-billion parameter Cosmos3 model locally using INT4 quantization. The implementation supports both Text-to-Image and Image-to-Video tasks on Apple Silicon via MLX as well as CUDA.
NVIDIA has introduced PAIR, a tool designed to help users connect old hardware into a personal AI cluster. The initiative is aimed at leveraging existing devices to run AI workloads locally.