LLM Inference - Optimizing Latency, Throughput, and Scalability

Deploying Large Language Models (LLMs) for inference is a complex yet rewarding process that requires balancing performance, cost, and scalability. Optimizing and sizing LLM inference systems involves understanding tradeoffs, selecting the right tools, and leveraging NVIDIA’s advanced technologies like TensorRT-LLM, Triton Inference Server, and NVIDIA Inference Microservices (NIM). This guide explores the key techniques and strategies for efficient LLM deployment.

One of the most critical considerations in LLM inference is the tradeoff between latency and throughput. These two metrics are inversely related: improving one often comes at the expense of the other. For example, with concurrency set to 250, throughput can be up to 50 times higher than with concurrency set to 1, while latency only increases by a factor of 5. By relaxing latency requirements, you can significantly boost throughput and reduce Total Cost of Ownership (TCO). This tradeoff is particularly important when designing systems for applications like chatbots versus batch processing tasks.

As LLMs grow in size due to scaling laws, tensor parallelism (TP) becomes essential for distributing computations across multiple GPUs. Even if a model fits on a single GPU, TP can still provide significant benefits. Deploying a model in TP2 mode across 2 GPUs doubles memory bandwidth and compute resources compared to running the same model on a single GPU in TP1 mode. TP2 improves latency for individual requests but incurs communication overhead between GPUs. For optimal performance, consider using NVLink-enabled servers like DGX or HGX systems or PCIe-connected H100 NVL cards.

The Hopper architecture introduced FP8 precision, which offers significant advantages over FP16. FP8 halves data storage needs compared to FP16 while doubling processing speed. The Transformer Engine dynamically scales tensors to maintain accuracy when using FP8. This makes FP8 especially useful for large-scale deployments where reducing memory usage and maximizing throughput are critical.

Leverage NVIDIA's tools for optimized inference workloads. TensorRT-LLM optimizes models for specific hardware constraints like latency or throughput, while Triton Inference Server simplifies deployment with features like dynamic batching and multi-framework support. NIM provides prebuilt microservices for quick deployment with out-of-the-box optimizations. For models larger than 13B parameters, use NVLink-enabled systems to handle increased memory requirements and inter-GPU communication efficiently.

Choose the appropriate mode based on your application. Streaming mode prioritizes Time-to-First-Token (TTFT) for real-time applications like chatbots, while sequential mode optimizes End-to-End Latency (E2E) for tasks requiring complete responses before consumption. Output tokens dominate both cost and latency; input tokens are comparatively cheaper. Strict latency limits reduce throughput but may be necessary for certain real-time applications.

Production applications often experience fluctuating demand throughout the day. Use 95% of the expected peak requests per second as a reference point to balance underutilization during valleys and capacity constraints during peaks. If only average requests per second are available, use a Poisson distribution to estimate peak demand. This approach ensures cost-efficiency while maintaining acceptable latency during high-demand periods.

On-premise deployment costs include GPU server purchase price (amortized over several years), datacenter hosting costs (electricity, space rental, staff), and NVIDIA AI Enterprise License per GPU (annual cost). Cloud APIs offer flexibility but can lead to higher long-term costs due to token-based pricing models. While cloud APIs simplify deployment, they provide less control over latency and throughput compared to on-prem solutions.

In-Flight Batching (IFB) dynamically combines requests at different stages (prefill and decoding) into a single batch, maintaining nearly constant batch sizes for higher GPU utilization while reducing latency by allowing new requests to enter ongoing batches without waiting for current ones to complete. Chunked context processing splits long input sequences into chunks for efficient processing, balancing compute-bound prefill with memory-bound decoding.

Client-side concurrency maintains stable latencies by sending concurrent requests from clients at a fixed concurrency level (C). This ensures consistent performance even under varying workloads. Larger models require more memory and have higher latency—choose model size based on application needs.

Sizing LLM inference systems involves navigating tradeoffs between latency, throughput, hardware constraints, and deployment costs. By leveraging NVIDIA’s advanced tools like TensorRT-LLM and NIM alongside optimization techniques such as tensor parallelism, IFB, and precision scaling (FP8), you can deploy scalable and efficient inference systems tailored to your application’s requirements. Whether you're building real-time chatbots or processing large-scale datasets offline, these best practices will help you design robust AI-powered solutions that balance performance with cost-effectiveness!