As large language models (LLMs) undergo rapid advancements, businesses are keenly focused on developing generative AI-powered applications that optimize throughput, reduce operational costs, and enhance user experiences by minimizing latency. This blog post delves into the essential performance metrics of throughput and latency when working with LLMs, exploring their significance, trade-offs, and how NVIDIA NIM microservices can be leveraged to optimize both metrics effectively.
Understanding Key Metrics for Cost Efficiency
When a user submits a request to an LLM, the system processes that request and starts generating a response by outputting a series of tokens. It is common for multiple requests to be sent to the system concurrently, which helps minimize wait times. Throughput refers to the number of successful operations completed in a given timeframe. For enterprises, throughput is crucial for determining how well they can manage simultaneous user requests. In the context of LLMs, throughput is quantified as tokens per second, making higher throughput not only a cost-saver but also a potential revenue generator.
Elevating throughput offers a competitive advantage by supporting the scalability of high-performance applications through technologies such as Kubernetes. This shift leads to lower server costs and accommodates a larger user base.
Latency, which measures the delay in data transfers, is evaluated in two key ways: time to first token (TTFT) and inter-token latency (ITL). Ensuring low latency is essential for maintaining a smooth user experience while optimizing the overall efficiency of the system.
Imagine a model receiving multiple concurrent requests (L1 – Ln) over a time span from T_start to T_end. Each line indicates the individual latency for each request; having more lines in each row that are shorter represents higher throughput and lower latency overall.
Key Latency Metrics Explained
TTFT measures the duration it takes for the model to produce the first token after receiving a request. This metric is vital as it impacts how quickly users receive initial information; shorter TTFT values generally result in better user experiences, especially for applications like customer service or e-commerce. Ideally, TTFT should be kept within a few seconds.
ITL, on the other hand, denotes the time interval between generating subsequent tokens. This is particularly important in applications that require seamless and continuous text generation, where ITL should ideally be lower than the average human reading speed to facilitate a smooth reading experience.
The Balancing Act Between Throughput and Latency
The relationship between throughput and latency is intrinsically tied to the number of concurrent requests and the latency budget, which is determined by the specific use case of the application. Handling numerous requests at once can increase throughput but may result in higher latency for each individual user request.
Under a defined latency budget—the acceptable amount of wait time users are willing to tolerate—enterprises can optimize throughput by managing the number of concurrent requests effectively. This latency budget can place constraints on TTFT and end-to-end latency.
As the number of concurrent requests increases, it is possible to add more GPUs by deploying multiple instances of the model service. This approach is essential in maintaining both throughput and user experience, especially during peak periods, such as managing a chatbot during Black Friday sales.
How NVIDIA NIM Enhances Throughput and Latency
NVIDIA has introduced a powerful solution to help enterprises maintain high throughput and low latency—NVIDIA NIM. This set of microservices is crafted to optimize performance, ensuring security, ease of use, and flexibility in deploying models across various platforms. NIM delivers substantial reductions in total cost of ownership (TCO) through efficient AI inference that scales with available infrastructure.
- Optimized model performance through runtime refinement.
- Intelligent model representation tailored for specific applications.
- Customized throughput and latency profiles to suit various requirements.
NVIDIA TensorRT-LLM enhances model performance by tweaking parameters such as GPU count and batch size, which help in refining the system to achieve optimal latency and throughput. As part of NVIDIA AI Enterprise, NIM is rigorously tuned to ensure high-performance configurations for different models.
Additional techniques like Tensor Parallelism and in-flight batching (IFB) significantly increase throughput while reducing latency by permitting the parallel processing of simultaneous requests and maximizing GPU use.
NIM Performance: Real Impact on Throughput and Latency
Utilizing NVIDIA NIM can lead to remarkable enhancements in both throughput and latency. For instance, the NVIDIA Llama 3.1 8B Instruct NIM has shown a staggering 2.5x improvement in throughput, coupled with a 4x faster TTFT and a 2.2x quicker ITL, outperforming the finest open-source alternatives available.
In real-world scenarios, a live demo depicted the performance difference between chatbot responses when running with NIM versus without it. The NIM-enabled model generated responses 2.4 times faster than its non-NIM counterpart, showcasing the value of optimized technologies like TensorRT-LLM, in-flight batching, and tensor parallelism.
Start Harnessing NVIDIA NIM Today
NVIDIA NIM is redefining standards in enterprise AI performance, offering unmatched capabilities, user-friendliness, and cost efficiency. Whether your goal is to improve customer support, refine operations, or foster innovation in your sector, NIM delivers the robust, scalable, and secure solution needed.
Experience the exhilarating high throughput and low latency achievable with the Llama 3 70B NIM. For insights on benchmarking NIM on your infrastructure, you can refer to resources that provide comprehensive guides for LLM performance, enabling you to explore the full potential of NVIDIA NIM.
0 Comments