Is this you? As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.
Claim your profile
Articles by Nick Comly
Optimize AI Inference Performance with NVIDIA Full-Stack Solutions
The explosion of AI-driven applications has placed unprecedented demands on both developers, who must balance delivering cutting-edge performance with managing operational complexity and cost, and AI infrastructure. NVIDIA is empowering developers with full-stack innovations—spanning chips, systems, and software—that redefine what’s possible in AI inference, making it faster, more efficient, and more scalable than ever before.
Streamlining AI Inference Performance and Deployment with NVIDIA TensorRT-LLM Chunked Prefill
In this blog post, we take a closer look at chunked prefill, a feature of NVIDIA TensorRT-LLM that increases GPU utilization and simplifies the deployment experience for developers. This builds on our previous post discussing how advanced KV cache optimization features in TensorRT-LLM improve performance up to 5x in use cases that require system prefills. When a user submits a request to a model, it goes through two distinct computational phases: prefill and decode.
5x Faster Time to First Token with NVIDIA TensorRT-LLM KV Cache Early Reuse
In our previous blog post, we demonstrated how reusing the key-value (KV) cache by offloading it to CPU memory can accelerate time to first token (TTFT) by up to 14x on x86-based NVIDIA H100 Tensor Core GPUs and 28x on the NVIDIA GH200 Superchip. In this post, we shed light on KV cache reuse techniques and best practices that can drive even further TTFT speedups. LLM models are rapidly being adopted for many tasks, including question-answering, and code generation.
3x Faster AllReduce with NVSwitch and TensorRT-LLM MultiShot
Deploying generative AI workloads in production environments where user numbers can fluctuate from hundreds to hundreds of thousands – and where input sequence lengths differ with each request – poses unique challenges. To achieve low latency inference in these environments, multi-GPU setups are a must – irrespective of the GPU generation or its memory capacity.
NVIDIA GH200 Superchip Accelerates Inference by 2x in Multiturn Interactions with Llama Models
Deploying large language models (LLMs) in production environments often requires making hard trade-offs between enhancing user interactivity and increasing system throughput. While enhancing user interactivity requires minimizing time to first token (TTFT), increasing throughput requires increasing tokens per second.
Boosting Llama 3.1 405B Throughput by Another 1.5x on NVIDIA H200 Tensor Core GPUs and NVLink Switch
The continued growth of LLMs capability, fueled by increasing parameter counts and support for longer contexts, has led to their usage in a wide variety of applications, each with diverse deployment requirements. For example, a chatbot supports a small number of users at very low latencies for good interactivity. Meanwhile, synthetic data generation requires high throughput to process many items at once.
Low Latency Inference Chapter 2: Blackwell is Coming. NVIDIA GH200 NVL32 with NVLink Switch Gives Signs of Big Leap in Time to First Token Performance | NVIDIA Technical Blog
Many of the most exciting applications of large language models (LLMs), such as interactive speech bots, coding co-pilots, and search, need to begin responding to user queries quickly to deliver positive user experiences. The time that it takes for an LLM to ingest a user prompt (and context, which can be sizable) and begin outputting a response is called time to first token (TTFT).
Boosting Llama 3.1 405B Performance up to 1.44x with NVIDIA TensorRT Model Optimizer on NVIDIA H200 GPUs
The Llama 3.1 405B large language model (LLM), developed by Meta, is an open-source community model that delivers state-of-the-art performance and supports a variety of use cases. With 405 billion parameters and support for context lengths of up to 128K tokens, Llama 3.1 405B is also one of the most demanding LLMs to run. To deliver both low latency to optimize the user experience and high throughput to optimize cost, a high-performance, full-stack platform is required.
Low Latency Inference Chapter 1: Up to 1.9X Higher Llama 3.1 Performance with Medusa on NVIDIA HGX H200 with NVLink Switch
As large language models (LLMs) continue to grow in size and complexity, multi-GPU compute is a must-have to deliver the low latency and high throughput that real-time generative AI applications demand.
Achieving High Mixtral 8x7B Performance with NVIDIA H100 Tensor Core GPUs and TensorRT-LLM
As large language models (LLMs) continue to grow in size and complexity, the performance requirements for serving them quickly and cost-effectively continue to grow. To deliver high LLM inference performance, an efficient parallel computing architecture and a flexible and highly-optimized software stack are required.
NVIDIA TensorRT 10.0 Upgrades Usability, Performance, and AI Model Support
NVIDIA today announced the latest release of NVIDIA TensorRT, an ecosystem of APIs for high-performance deep learning inference. TensorRT includes inference runtimes and model optimizations that deliver low latency and high throughput for production applications. This post outlines the key features and upgrades of this release, including easier installation, increased usability, improved performance, and more natively supported AI models.
NVIDIA TensorRT-LLM Enhancements Deliver Massive Large Language Model Speedups on NVIDIA H200
Large language models (LLMs) have seen dramatic growth over the last year, and the challenge of delivering great user experiences depends on both high-compute throughput as well as large amounts of high-bandwidth memory. NVIDIA TensorRT-LLM provides optimizations for both peak throughput and memory optimization, delivering massive improvements in LLM inference performance.
Optimizing Inference on Large Language Models with NVIDIA TensorRT-LLM, Now Publicly Available
Today, NVIDIA announces the public release of TensorRT-LLM to accelerate and optimize inference performance for the latest LLMs on NVIDIA GPUs. This open-source library is now available for free on the /NVIDIA/TensorRT-LLM GitHub repo and as part of the NVIDIA NeMo framework. Large language models (LLMs) have revolutionized the field of artificial intelligence and created entirely new ways of interacting with the digital world.
NVIDIA TensorRT-LLM Supercharges Large Language Model Inference on NVIDIA H100 GPUs
Large language models offer incredible new capabilities, expanding the frontier of what is possible with AI. But their large size and unique execution characteristics can make them difficult to use in cost-effective ways. NVIDIA has been working closely with leading companies, including Meta, Anyscale, Cohere, Deci, Grammarly, Mistral AI, MosaicML, now a part of Databricks, OctoML, Tabnine, and Together AI, to accelerate and optimize LLM inference.
Show More
loading
Actions
Is this you?
As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.Get in touch with Nick
Contact Nick, search articles and posts on X, monitor coverage, and track replies from one place.
Learn more about Muck Rack