NVIDIA doubled Blackwell performance through continuous software optimization, refining kernels, compiler paths, and inference runtimes so the same hardware delivers significantly more useful AI throughput over time. Initial gpt-oss-120b performance on an NVIDIA DGX Blackwell B200 system with the NVIDIA TensorRT LLM library was market-leading, but NVIDIA’s teams and the community have significantly optimized TensorRT LLM for open-source large language models. The TensorRT LLM v1.0 release is a major breakthrough in making large AI models faster and more responsive for everyone. Through advance
Telecommunications ArchivesBlackwell’s leadership comes from extreme hardware-software codesign. It’s a full-stack architecture built for speed, efficiency and scale: The Blackwell architecture features include: NVFP4 low-precision format for efficiency without loss of accuracy Fifth-generation NVIDIA NVLink that connects 72 Blackwell GPUs to act as one giant GPU NVLink Switch, which enables high concurrency through advanced tensor, expert and data parallel attention algorithms Annual hardware cadence plus continuous software optimization — NVIDIA has more than doubled Blackwell performance since launch using software
Telecommunications Archives…The best AI factories use accelerated computing to increase tokens per watt — optimizing AI performance while dramatically increasing energy efficiency across AI factories and applications. GB300 NVL72 — which features the Blackwell Ultra…
…Full-stack software optimization offers the key to improving AI inference performance and achieving this goal. Optimizing AI Inference for Cost-Effective User Throughput Businesses are often challenged with balancing the performance…
…Google and NVIDIA have optimized Gemma 4 for NVIDIA GPUs, enabling efficient performance on NVIDIA RTX-powered PCs and workstations, NVIDIA DGX Spark personal AI supercomputers and NVIDIA Jetson Orin Nano edge…
…Recent MIT research found that infrastructure and algorithmic efficiencies are reducing inference costs for frontier-level performance by up to 10x annually. To understand how infrastructure efficiency improves tokenomics, consider the analogy…
…generated stickers — but Snap is also continuously rolling out behind-the-scenes updates such as performance optimizations and compatibility updates for new operating system versions. The A/B testing for all these…
…Taking advantage of the Blackwell architecture’s FP4 precision, along with the NVIDIA QUASAR Quantization System, the submission revealed powerful performance while meeting the benchmark’s accuracy requirements. Relentless Optimization NVIDIA platforms…
Cost per token is the one TCO metric that directly accounts for hardware performance, software optimization, ecosystem support and real-world utilization — and NVIDIA delivers the lowest cost per token in the…
…Advancing Performance Across Open-Source AI Models A breadth of highly optimized open models that would’ve previously required a data center to run can now be accelerated at the desktop on…
…Proven first in production with performance optimized on NVIDIA Spectrum-X Ethernet hardware and now released as an open specification through the Open Compute Project, MRC demonstrates the power of the Spectrum…
…AI factories integrate high-performance AI infrastructure, high-speed networking and optimized software to produce intelligence at scale. These components are designed to be flexible and programmable, allowing businesses to prioritize the…