This is the third post in the large language model latency-throughput benchmarking series, which aims to instruct developers on how to determine the cost of LLM inference by estimating the total cost of ownership (TCO). See LLM Inference Benchmarking: Fundamental Concepts for background knowledge on common metrics for benchmarking and parameters. See LLM Inference Benchmarking Guide: NVIDIA…
]]>With the growth of large language models (LLMs), deep learning is advancing both model architecture design and computational efficiency. Mixed precision training, which strategically employs lower precision formats like brain floating point 16 (BF16) for computationally intensive operations while retaining the stability of 32-bit floating-point (FP32) where needed, has been a key strategy for…
]]>In recent years, large language models (LLMs) have achieved extraordinary progress in areas such as reasoning, code generation, machine translation, and summarization. However, despite their advanced capabilities, foundation models have limitations when it comes to domain-specific expertise such as finance or healthcare or capturing cultural and language nuances beyond English.
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