How to maximize ROI on your LLM inference costs

Continuous LLM performance improvements drive ROI: NVIDIA consistently optimizes large language models (LLMs) to enhance throughput and reduce latency, maximizing return on infrastructure investments for real-time applications.

• NVIDIA regularly optimizes state-of-the-art community models, including Meta’s Llama, Google’s Gemma, Microsoft’s Phi, and their own NVLM-D-72B.
• These optimizations allow customers to serve more complex models and reduce the infrastructure needed to host them.
• Performance improvements occur at every layer of the technology stack, including the TensorRT-LLM library.

Significant performance gains: Recent advancements in NVIDIA’s platforms have resulted in substantial improvements in LLM performance.

• Minimum latency performance for the open-source Llama 70B model has improved 3.5x in less than a year.
• In the MLPerf Inference 4.1 benchmark, the new Blackwell platform delivered 4x more performance than the previous generation.
• Blackwell’s submission was the first to use FP4 precision, reducing memory footprint and boosting computational throughput.
• Hopper performance has increased 3.4x in MLPerf on H100 in the last year due to software advancements.
• NVIDIA’s peak performance today on Blackwell is 10x faster than it was just one year ago on Hopper.

TensorRT-LLM: Purpose-built for LLM acceleration: This library incorporates state-of-the-art optimizations for efficient LLM inference on NVIDIA GPUs.

• Built on top of the TensorRT Deep Learning Inference library, it leverages existing optimizations while adding LLM-specific improvements.
• Regular updates to NVIDIA software libraries allow customers to extract more performance from their existing GPUs.

Llama model optimization: NVIDIA continues to refine various versions of Meta’s Llama models, including the latest 3.1 and 3.2 versions and larger sizes like 405B.

• Optimizations include custom quantization recipes and efficient parallelization techniques.
• NVIDIA’s hardware, such as the H200 Tensor Core GPU with fourth-generation NVLink, provides the necessary GPU-to-GPU bandwidth for these parallelism techniques.

Balancing latency and throughput: Different parallelization techniques are employed based on specific application requirements.

• Tensor parallelism is crucial for lowest latency scenarios, delivering over 5x more throughput.
• Pipeline parallelism brings 50% more performance for maximum throughput use cases.
• Production deployments often combine both techniques to maximize throughput within a given latency budget.

Ongoing software optimization cycle: NVIDIA’s continuous software tuning throughout the lifecycle of their architectures translates to additional value for customers.

• Customers can create more capable models and applications while using less infrastructure, enhancing their ROI.
• NVIDIA continues to optimize new LLMs and generative AI models for their platforms.
• Technologies like NIM microservices and NIM Agent Blueprints make deployment easier.

Analyzing deeper: As the AI landscape rapidly evolves, NVIDIA’s commitment to ongoing optimization plays a crucial role in driving the adoption and scalability of LLMs. By consistently improving performance across various model sizes and types, NVIDIA not only enhances the capabilities of existing hardware but also pushes the boundaries of what’s possible in AI applications. This approach ensures that organizations can continue to extract value from their AI investments while staying at the forefront of technological advancements.