Generative AI

LLM Inference Optimization: Key Challenges and Solutions

The landscape of Large Language Models (LLMs) has transformed dramatically since the advent of ChatGPT in 2022, heralding a new era in the field of Generative AI and underscoring the vast capabilities of LLMs. The emergence of open-source models like Llama 2, Mistral-7B, and Deci’s DeciLM-7B in 2023 has further democratized access to these powerful tools, broadening their application across various sectors including healthcare, education, and business. Despite these advancements, the effective deployment of LLMs in real-world scenarios remains a complex task, especially when it comes to LLM inference optimization.

One of the primary challenges in harnessing the full potential of LLMs lies in their inference optimization, which is critical for achieving scalability and efficiency. The substantial computational demands, stemming from the size and intricacy of LLMs, present significant challenges compared to smaller NLP models. These challenges include high processing power requirements, extensive memory needs, and latency issues in real-time applications. This blog post delves into the inherent difficulties of LLM inference optimization, exploring why deploying and running LLMs in production settings is complex, and outlining strategies to enhance their scalability and efficiency.

Why is LLM inference more challenging than Traditional NLP model inference?

Before LLMs, there were smaller, traditional Natural Language Processing (NLP) models, which focused on specific tasks like text classification, sentiment analysis, and named entity recognition. These models required less computational power and could be deployed and run on modest hardware, with a straightforward inference process tailored to specific applications.

The introduction of LLMs, however, represents a paradigm shift. These models, characterized by their vast scale and complexity, are built with billions of parameters and trained on diverse and extensive datasets. This has led to a dramatic increase in the computational resources needed for both training and inference, often requiring high-end GPUs and specialized hardware to handle the sheer volume of data processing. Additionally, LLMs are designed to perform a wide range of language tasks with a single model, making their deployment more complex as they need to be finely tuned to balance general capabilities with task-specific performance. The inference process in LLMs also poses challenges in terms of latency and throughput, especially in real-time applications, and involves more sophisticated management of issues like contextual relevance and biases.

While NLP and LLMs are both centered around language processing, the evolution to LLMs has introduced new dimensions in terms of scale, computational demands, and versatility, significantly altering the landscape of deployment and inference in language-based AI models. This shift has particularly underscored the importance of LLM inference optimization. The increased scale and complexity of LLMs compared to traditional NLP models have created a need for more sophisticated approaches to optimize their inference processes.

LLM Inference Optimization Challenges

1. Autoregressive Generation

In order to stitch together human-quality text, LLMs commonly use autoregressive generation, where each word is predicted based on the ones before it. However, this process presents a major computational challenge for efficient inference. Each token prediction relies on the previously generated text, creating a sequential dependency. As text length grows, the generation becomes progressively slower, impacting the scalability and efficiency of LLMs.

Autoregressive generation where each generated word is predicted based on the preceding words in the sequence. Image source: Generation with LLMs

These challenges are rooted in attention mechanisms, which track token relationships and require substantial memory for intermediate states. The longer the sequence, the more memory and compute are required for inference, as memory scales linearly with sequence length and compute scales polynomially. Sequential dependency reduces the efficiency of processing multiple tasks at once and also prevents parallelization, limiting speed improvements even with advanced hardware.

Continuous batching, a potential LLM inference optimization solution, often brings its own overhead in managing varying batch sizes, sometimes offsetting performance benefits for diverse prompt lengths. At the same time, the amount of available memory sets a cap on how large these batches can be, which in turn limits the number of tasks that can be handled simultaneously. Additionally, the practice of setting aside large amounts of memory in advance to avoid running out during longer tasks often leads to unused resources when dealing with shorter ones. This not only increases costs but also results in less efficient use of the system’s resources.

Static batching (left) vs. continuous batching (right). In continuous batching, when a sequence produces its end-of-sequence token, a new sequence (like S5, S6, S7) takes its place. This method is supposed to achieve improved GPU efficiency, as the GPU doesn’t have to wait for all sequences to finish before initiating new ones.

2. Unpredictable Prompt Lengths

The unpredictable lengths of user prompts introduce a substantial challenge to inference,  particularly in balancing memory allocation and computational efficiency. With each prompt requiring a different amount of computational resources, LLMs must constantly adjust their memory usage and processing strategies, leading to challenges in maintaining consistent performance and efficiency. Longer prompts demand more memory and computational power, potentially causing slower inference times and increased latency, particularly noticeable in real-time applications. Conversely, shorter prompts may lead to underutilization of allocated resources, resulting in inefficiencies and increased operational costs.

This disparity in prompt lengths also makes LLM inference optimization more challenging, as LLMs must be versatile enough to handle long, complex inputs while remaining fast and resource-efficient for simpler tasks. The continuous fluctuation in prompt sizes makes it challenging to predict resource needs accurately, requiring a balance between ensuring sufficient capacity for the most demanding prompts and avoiding resource wastage.

The implications of this resource imbalance are multifaceted. It directly limits the scalability of the LLMs, as the system cannot efficiently manage memory across varying prompt sizes. This inefficiency is further exacerbated when multiple LLM instances run concurrently, leading to increased memory contention and potential degradation in processing speed. Consequently, users may experience longer wait times for responses, especially in high-demand scenarios where multiple LLMs are handling diverse and unpredictable prompt lengths simultaneously.

Complex Logic Techniques in Forward Passes and Their Impact on LLM Inference Efficiency

In LLMs, complex logic forward passes using techniques like beam search and sampling introduce notable challenges for runtime inference in real-world applications. Beam search, aimed at finding the most probable output sequence, and sampling, which randomly generates tokens based on probability distributions, both significantly increase the computational overhead. Beam search requires the model to explore and evaluate multiple potential paths or sequences before generating a response, making the process much more resource-intensive than simple greedy decoding. This intricacy leads to longer inference times, a critical issue for applications needing real-time responses, such as interactive chatbots or on-the-fly translation services. Moreover, the increased computational demand can strain infrastructure, escalating operational costs and necessitating more powerful hardware. Balancing the desire for high-quality, contextually nuanced outputs provided by these methods with the practical constraints of speed and cost is a persistent challenge.

The Challenge of Updating CUDA Kernels for Optimizing LLM Inference

Parallel processing of tasks is essential in complex computations like those in LLMs. CUDA kernels, specialized functions executed on NVIDIA GPUs for parallel processing, play a crucial role in the performance of LLMs and are essential for harnessing the power of GPUs to accelerate the computational processes in LLMs. However, implementing these kernels for LLMs is increasingly challenging due to the rapid pace of research-based improvements in the field. As LLM architectures grow more complex and computational demands escalate, updating CUDA kernels to optimize for these advancements becomes a daunting task. Each new breakthrough in LLM technology often necessitates a tailored approach to CUDA kernel development, involving intricate programming to ensure efficient parallelization and optimal resource utilization. The expertise required to continually adapt these kernels to the latest research findings, while also maintaining high performance and efficiency, creates a significant lag between research development and practical application and constitutes a significant challenge for LLM inference optimization.

A fused CUDA kernel integrating tiling and recomputation, resulting a 7.6x speed up. Image source: FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness

Python’s Parallelization Limitations

The predominant use of Python codebases for LLMs, while popular for its simplicity and readability, is not inherently designed for parallelization, a key optimization technique for GPU utilization. This limitation stems partly from the Global Interpreter Lock (GIL) in Python, which restricts the parallel execution of multiple threads in a single process, thereby hindering true parallelism. As a result, LLMs, which require extensive computational resources and high-speed processing for inference, cannot fully leverage the advantages of multithreading or multi-core processing. This inefficiency makes every line of Python code computationally expensive, especially for large-scale models dealing with vast datasets and complex algorithms. Overcoming this challenge often involves using workarounds like multi-processing or integrating Python with more performance-oriented languages or libraries, which can add complexity and overhead to the development and maintenance of these models.

Hardware Constraints in LLM Inference Optimization

The inference capabilities of LLMs are significantly impacted by hardware constraints, particularly the limitations of current GPUs in terms of Video Random Access Memory (VRAM). A key LLM inference optimization strategy is to process multiple requests simultaneously through large batching. However, due to the immense size and complexity of LLMs, this approach demands a substantial amount of VRAM. The current generation of GPUs, despite being advanced, often falls short in this aspect. They do not possess sufficient VRAM to accommodate the large batches required for optimal LLM inference, leading to a bottleneck in processing efficiency. This limitation not only restricts the speed and throughput of LLM operations but also poses challenges in scaling their applications for real-world scenarios, where rapid response times and handling multiple requests concurrently are essential. Therefore, the hardware constraints, particularly the VRAM capacity of existing GPUs, play a critical role in shaping the practical deployment and operational effectiveness of LLMs, underlining the need for continued advancements in hardware technology to fully leverage the potential of these sophisticated AI models.

Why Traditional Optimization Techniques Fail with LLMs

One primary optimization technique–quantization–which involves compressing model parameters to reduce size and increase inference speed, often falls short with LLMs. These models have complex, intricate structures that do not lend themselves well to straightforward compression methods like quantization. The attempt to reduce their size can lead to a significant loss of the nuance and complexity that characterize their performance, resulting in a substantial drop in model accuracy.

Model Quantization: single precision, half precision, 8-bit integer

Additionally, conventional compilation strategies, which typically optimize a computation graph for a specific hardware setup, are not fully equipped to handle the dynamic nature of generative models like LLMs. These models often engage in iterative and stochastic processes, especially evident in tasks like sentence generation or image creation. Their computational paths vary and evolve during the inference process, making it challenging to fit them into a static computation graph. This dynamic and variable nature of LLMs demands a level of flexibility and adaptability that conventional static compilation strategies cannot provide without sacrificing the model’s expressivity or performance.

How Infery-LLM Can Help

Deci’s Infery-LLM is an SDK designed to enhance the performance and reduce the costs of LLMs by making them easier to optimize and deploy. It allows seamless deployment on various hardware and integrates advanced optimization techniques, such as selective quantization and continuous batching for higher throughput. Discover more about Infery’s LLM inference optimization techniques in our comprehensive article.

With Infery-LLM, setting up inference processes is simple, requiring only three lines of code, which makes it highly accessible for integration into any project with minimal programming effort. Infery-LLM boosts efficiency, demonstrated by its ability to achive significantly higher throughput than alternative libraries for LLM inference and serving, such as vLLM.

For example, running with Infery-LLM, models such as Deci-Nano and DeciLM-7B achieve a throughput that’s 2.6x and 2.4x higher, respectiely, than the similar Mistral 7B with vLLM. The throughput boost is even higher compared to Googl’s Gemma-7b-it and Meta’s Llama 2 7b chat.

The increase in throughput directly leads to substantial cost savings, making Infery-LLM not only a performance enhancer but also a cost-effective solution for LLM deployment.

We invite you to witness the impact of Infery-LLM yourself by trying it out for free in our playground, where you can see our models in action.

For those interested in exploring our Virtual Private Cloud (VPC) and on-premises deployment options, we encourage you to book a 1:1 session with our experts.

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					from transformers import AutoFeatureExtractor, AutoModelForImageClassification

extractor = AutoFeatureExtractor.from_pretrained("microsoft/resnet-50")

model = AutoModelForImageClassification.from_pretrained("microsoft/resnet-50")