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…
]]>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).
]]>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. Performance depends both on the ability for the combined GPUs to process requests as “one mighty GPU” with ultra-fast GPU-to-GPU communication and advanced software able to take full…
]]>Large language models (LLM) are getting larger, increasing the amount of compute required to process inference requests. To meet real-time latency requirements for serving today’s LLMs and do so for as many users as possible, multi-GPU compute is a must. Low latency improves the user experience. High throughput reduces the cost of service. Both are simultaneously important. Even if a large…
]]>As of March 18, 2025, NVIDIA Triton Inference Server is now part of the NVIDIA Dynamo Platform and has been renamed to NVIDIA Dynamo Triton, accordingly. AI is transforming every industry, addressing grand human scientific challenges such as precision drug discovery and the development of autonomous vehicles, as well as solving commercial problems such as automating the creation of e-commerce…
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