Choosing the right large language model (LLM) is essential to the success of any generative AI approach because there are an increasing number of models available. When a model is utilized without being trained, inference is the process by which outputs are generated in real time. As LLMs become more widely used, teams have more difficulties with latency, GPU constraints, and scalability expenses.
While there are a number of ways to assess an LLM’s capabilities, including benchmarking, as explained in our earlier guidance, testing a model’s inference is one of the approaches that is most relevant to practical applications. That is, how fast it produces answers. The definition of LLM inference, important optimization strategies, infrastructure issues, and how TrueFoundry facilitates effective inference scaling are all covered in this paper.
LLM Inference Performance Monitoring: What Is It?
Entering a prompt and having an LLM respond is known as LLM inference. Using the patterns and relationships it was exposed to during training, a language model makes inferences or predictions to produce a suitable output. As opposed to training, which modifies model weights, inference is a forward-pass process that uses the input prompt to determine the subsequent token or series of tokens.
It is vital to measure LLM inference because it enables you to evaluate an LLM’s effectiveness, dependability, and consistency—all of which are critical factors in figuring out how well it will function in practical situations and deliver the desired value in a reasonable amount of time.
The computational cost of inference is high, particularly for big models such as Mistral, LLaMA 3, or GPT-4. One token is generated at a time by these autoregressive models, which makes the process sequential and challenging to parallelize.
Furthermore, inference cost is directly impacted by model size. Larger models react more slowly and demand more GPU memory and processing resources. LLM inference is essentially where the rubber hits the road. It is the point where infrastructure, user expectations, and model performance converge, making scalability and optimization crucial for practical applications.
Which Performance Metrics Are Most Crucial for LLM Inference?
Latency and throughput are the measures that we are most interested in when assessing a large language model’s inference capabilities.
1. Latency
The amount of time it takes an LLM to produce a response to a user’s prompt is known as latency. It is primarily in charge of creating a user’s perception of how quick or effective a generative AI application is, and it offers a means of assessing a language model’s pace. A model’s latency can be measured in several ways, such as:
The Time To First Token (TTFT) measures how long it takes a user to begin receiving a response from a model after they enter their prompt. It is based on how long it takes to process the user’s input and produce the initial completion token.
On the other hand, TPOT is the average time required to produce a completion token for every user who queries the model at a specific moment. Sometimes, inter-token latency (ITL) is another name for this.
Total generation time is the end-to-end latency of an LLM, which is the period between the user’s initial input of a prompt and the completion of the model’s output. Often, when people talk about latency, they’re really talking about total generation time.
Total generation time = TTFT + (TPOT x number of generated tokens)
2. Throughput
The number of requests or output that an LLM can process in a specific amount of time is indicated by its throughput. Requests per second or tokens per second are the two common metrics used to quantify throughput.
Requests per second: This measure measures how well the model manages concurrency and is based on the total generation time of the model as well as the number of simultaneous requests.
Tokens per second: This is a more widely used statistic for gauging throughput because requests per second are impacted by total generation time, which is based on the length of the model’s output and, to a lesser extent, its input.
3. Requesting Batching
Batching has been one of the most popular and efficient ways to boost an LLM’s throughput. Less frequently, parameters must be loaded since batching entails gathering as many inputs as feasible to process simultaneously rather than loading the model’s parameters for every user prompt. There are also constraints on the maximum batch size that can be used before triggering memory overflow; however, the larger the batch size, the greater the latency drop-off.
The Operation of LLM Inference
Let’s first take a quick look at the two steps that an LLM takes to accomplish inference: the prefill phase and the decoding phase.
The LLM must first parse the text from a user’s input prompt by turning it into a sequence of prompt or input tokens during the prefill phase. Each model has a different tokenizer, or the precise method by which an LLM separates text into tokens. Each token is created and then converted into a vector embedding, which is a numerical representation that the model can comprehend and use to conclude.
After that, the LLM creates several vector embeddings that show how it responded to the input prompt during the decoding stage. They are subsequently transformed into output tokens, also known as completion tokens, which are produced one at a time until they meet a stopping requirement, like the token limit number or one of a set of stop words. To complete a response, a model needs the same number of propagations as completion tokens because LLMs provide one token for each forward propagation, such as pass or iteration.
LLM Inference Optimization Advantages
Latency and inference cost soon become limiting constraints as businesses implement LLMs in production. Significant performance and commercial benefits can result from the application of appropriate inference optimization techniques.
1. Faster Response Time: Consider a scenario in which a stakeholder or client submits a complex question via one of your digital channels and has to wait for a few minutes. This will irritate and even upset them, particularly if the issue is about important things like disaster relief or medical treatment.
2. Environmental Sustainability: There are advantages to efficient inference for sustainability as well. Optimizing compute cycles and energy consumption reduces the environmental impact of operating LLMs, increasing the eco-consciousness of GenAI applications. Improving LLM inference is essential to creating scalable, high-caliber AI systems; it’s not just about speed.
3. Improved User Experience: Quick and reliable responses increase user happiness and help retain users. Latency has a direct effect on users’ perceptions of product quality in use cases including search augmentation, live recommendations, and summarization. Optimization guarantees a smooth and dependable real-time interaction.
4. Improved Personalization: In addition to speed, the modern consumer wants personalization. User pleasure is greatly increased by LLMs’ ability to deliver these more contextualized, tailored interactions at the same pace as non-optimized models, if not faster, thanks to strategies like knowledge distillation and pipeline parallelism.
Difficulties with Monitoring LLM Inference Performance
As useful as it is to understand a model’s latency and throughput, getting this information isn’t always simple. The following are a few difficulties in quantifying LLM inference:
1. High Computational Costs: Optimizing LLM inference requires specialized accelerators, TPUs, or high-end GPUs. Particularly when used on a large scale, they can be costly. Continuous GPU/TPU utilization may lead to hefty expenses even for cloud implementations.
2. Various token lengths for each model: Results from inference performance testing are usually shown in terms of token-based metrics, such as tokens per second; however, token lengths differ depending on the LLM. As a result, metrics aren’t always comparable between different model types.
3. Inconsistency in testing: there may be variations in the manner inference tests are carried out, depending on factors like the kind and quantity of GPU used, the quantity and type of prompts, whether the LLM is inferred locally or via an API, etc. Since the tests were carried out in various settings, these factors can all have an impact on a model’s inference metrics and making like-for-like comparisons challenging.
Final Thoughts
The performance of LLM inference must be tracked and optimized to create AI applications that are scalable, responsive, and easy to use. Important parameters, such as throughput, latency, and batching efficiency, have a direct impact on system scalability, operational expenses, and user satisfaction.
Even while optimization has many advantages, such as quicker reaction times, sustainability, and customization, performance monitoring has drawbacks, such as inconsistent testing conditions, high computational costs, and different token lengths.
In order to overcome this, companies need to use infrastructure platforms such as TrueFoundry and implement standardized testing procedures. Reliable, real-time intelligence that satisfies contemporary user expectations at scale is the fundamental goal of efficient LLM inference, not merely speed.
