Items tagged with “Transformers”.
NVIDIA HGX B200 lowers embodied carbon intensity by 24% vs. HGX H100, while delivering higher AI performance and energy efficiency. This article reviews the PCF-backed improvements, new hardware features, and implications for developers and enterprises.
AWS announces the Falcon-H1 instruction-tuned models from TII (0.5B–34B) on Amazon Bedrock Marketplace and SageMaker JumpStart, with multilingual support, a hybrid architecture, and deployment guidance.
Jetson Thor delivers edge-scale AI, enabling fast, multi-model generative reasoning with a Blackwell GPU, MIG, FP4/FP8, and 128 GB memory for next-gen robotic platforms.
NVIDIA extends NVFP4 from inference to pretraining, enabling 4-bit precision to accelerate large-scale transformer pretraining while preserving FP8/BF16-like accuracy. Early results on a 12B model show stable convergence and strong downstream performance, with up to 7x GEMM speedups on Blackwell Ult
The Nemotron Nano 2 9B is an open enterprise‑grade reasoning model with a Hybrid Transformer–Mamba backbone and a configurable thinking budget, delivering leading accuracy and 6x throughput for edge and hybrid deployments.
Open Nemotron Nano 2 9B delivers leading accuracy and up to 6x throughput with a Hybrid Transformer–Mamba backbone and a configurable thinking budget, aimed at edge, PC and enterprise AI agents.
NVIDIA Streaming Sortformer is an open, production-grade, low-latency speaker diarization model that sorts speakers by arrival order and integrates with NVIDIA NeMo and Riva for real-time transcription pipelines.
NVIDIA and OpenAI optimize gpt-oss-120b and gpt-oss-20b for accelerated inference on Blackwell, achieving up to 1.5M tokens per second on GB200 NVL72, with Day 0 support across cloud to edge.
Overview of Arc Institute's Virtual Cell Challenge, its STATE baseline, and the implications for in silico cell perturbation modeling and context generalization in transcriptomics.
Curated overview of key studies showing how AI systems reproduce and amplify gender bias, with concrete measures, benchmarks, and mitigations across embeddings, vision, NLP, and generative models.
Mamba replaces Transformer attention with State Space Models (SSMs) to achieve linear scaling in sequence length, enabling fast inference and competitive language performance up to million-token contexts.
A deep dive into Mamba, a State Space Model (SSM) based backbone designed for long-context sequences, offering Transformer-like performance with improved efficiency.
xT enables end-to-end modeling of gigapixel-scale images on modern GPUs using nested tokenization, region encoders, and long-context vision, delivering high fidelity and context on images up to 29,000×25,000 pixels.
End-to-end modeling of extremely large images on contemporary GPUs via nested tokenization and region/context encoders, delivering richer context with lower memory footprints.
New work demonstrates vec2text can recover substantial input from text embeddings, challenging assumptions that embeddings are a secure, lossy representation.