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NVIDIA's TensorRT Model Optimizer significantly boosts performance of Meta's Llama 3.1 405B large language model on H200 GPUs. (Read More)
NVIDIA releases TensorRT Model Optimizer v0.15, offering enhanced inference performance through new features like cache diffusion and expanded AI model support. (Read More)
NVIDIA's H100 Tensor Core GPUs and TensorRT-LLM software demonstrate record-breaking performance for the Mixtral 8x7B model, leveraging FP8 precision. (Read More)
NVIDIA introduces TensorRT 10.0 with weight-stripped engines, offering >95% compression for AI apps. (Read More)
NVIDIA introduces TensorRT for RTX, an optimized AI inference library for Windows 11, enhancing AI experiences across creativity, gaming, and productivity apps. (Read More)
NVIDIA's latest TensorRT update introduces FP4 image generation for RTX 50 series GPUs, enhancing AI model performance and efficiency. Explore the advancements in generative AI technology. (Read More)
NVIDIA TensorRT optimizes Adobe Firefly, cutting latency by 60% and reducing costs by 40%, enhancing video generation efficiency with FP8 quantization on Hopper GPUs. (Read More)
Microsoft and NVIDIA collaborate to significantly boost Meta Llama model performance on Azure AI Foundry using NVIDIA TensorRT-LLM optimizations, enhancing throughput, reducing latency, and improving cost efficiency. (Read More)
Discover how NVIDIA's TensorRT-LLM boosts Llama 3.3 70B model inference throughput by 3x using advanced speculative decoding techniques. (Read More)
NVIDIA's TensorRT-LLM now supports encoder-decoder models with in-flight batching, offering optimized inference for AI applications. Discover the enhancements for generative AI on NVIDIA GPUs. (Read More)
NVIDIA introduces KV cache early reuse in TensorRT-LLM, significantly speeding up inference times and optimizing memory usage for AI models. (Read More)
NVIDIA introduces TensorRT-LLM MultiShot to improve multi-GPU communication efficiency, achieving up to 3x faster AllReduce operations by leveraging NVSwitch technology. (Read More)
NVIDIA's TensorRT-LLM introduces multiblock attention, significantly boosting AI inference throughput by up to 3.5x on the HGX H200, tackling challenges of long-sequence lengths. (Read More)
NVIDIA NIM streamlines the deployment of fine-tuned AI models, offering performance-optimized microservices for seamless inference, enhancing enterprise AI applications. (Read More)
Explore NVIDIA's methodology for optimizing large language models using Triton and TensorRT-LLM, while deploying and scaling these models efficiently in a Kubernetes environment. (Read More)
NVIDIA introduces new KV cache optimizations in TensorRT-LLM, enhancing performance and efficiency for large language models on GPUs by managing memory and computational resources. (Read More)
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