LLM (Large Language Model) Inference Task
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A LLM (Large Language Model) Inference Task is a machine learning inference task that utilizes a pre-trained large language model to generate outputs or predictions based on given inputs.
- AKA: LLM Inferencing, Large Language Model Prediction.
- Context:
- It can (typically) be performed by an LLM Inference System by implementing LLM Inference Algorithms.
- It can process user-provided text prompts to produce coherent and contextually relevant responses, such as answering questions or summarizing documents.
- It can involve two main phases: the prefill phase, where input tokens are processed, and the generation phase, where the language model produces output tokens sequentially.
- It can (often) require significant computational resources; including memory and processing power, to manage the model's parameters and perform calculations; especially with larger models, necessitating optimization techniques to enhance efficiency and reduce latency.
- It can be deployed across various platforms, including web and mobile applications, enabling on-device AI functionalities.
- It can range from simple implementations using APIs to complex setups involving custom model deployments, depending on the application's requirements.
- It can (typically) utilize architectures such as transformers, learning from vast amounts of text data for general-purpose language understanding and generation.
- It can (often) involve platforms and tools like BentoML and Ray Serve for efficient and scalable deployment, despite the computational intensity of handling matrices for attention mechanisms and memory management.
- ...
- Example(s):
- Llama 2 70B-based generation for conversational AI applications.
- Google LLM Inference API used for on-device Android LLM tasks.
- Cloud-based LLM inference pipelines optimized for low latency generation.
- ...
- Counter-Example(s):
- LLM Training Tasks, which involve updating model weights based on new data.
- Small-Scale Language Model applications, which use less computation and simpler inference models.
- Rule-Based NLP Systems, which do not employ statistical models for generation.
- ...
- See: LLM Inference Evaluation Task Large Language Model, Natural Language Processing Task, Machine Learning Inference, Model Optimization Task, Large Language Model Configuration Parameter, Machine Translation Task, Content Generation Task, Computational Efficiency, Transformer Architecture, Model Serving Platform.
References
2025a
- (Google AI, 2025) ⇒ Google AI. (2025). "LLM Inference API". In: Google AI Edge.
- QUOTE: The LLM Inference API lets you run large language models (LLMs) completely on-device, which you can use to perform a wide range of tasks, such as generating text, retrieving information in natural language form, and summarizing documents.
The task provides built-in support for multiple text-to-text large language models, so you can apply the latest on-device generative AI models to your apps and products.
- QUOTE: The LLM Inference API lets you run large language models (LLMs) completely on-device, which you can use to perform a wide range of tasks, such as generating text, retrieving information in natural language form, and summarizing documents.
2025b
- (Google AI, 2025) ⇒ Google AI. (2025). "LLM Inference API for Android". In: Google AI Edge.
- QUOTE: The LLM Inference API supports many text-to-text large language models, including built-in support for several models that are optimized to run on browsers and mobile devices.
These lightweight models can be used to run inferences completely on-device.
- QUOTE: The LLM Inference API supports many text-to-text large language models, including built-in support for several models that are optimized to run on browsers and mobile devices.
2024a
- (MLCommons, 2024) ⇒ MLCommons. (2024). "MLPerf LLaMA2-70B". In: MLCommons.
- QUOTE: The MLPerf benchmark suite includes performance metrics for the LLaMA2-70B model, a state-of-the-art large language model.
This model is designed for high-efficiency inference across diverse hardware platforms.
- QUOTE: The MLPerf benchmark suite includes performance metrics for the LLaMA2-70B model, a state-of-the-art large language model.
2024
- (GPT-4, 2024) ⇒ task of LLM (Large Language Model) inference.
- The task of LLM (Large Language Model) inference involves executing a model to perform specific tasks, such as text generation, based on input data. This computationally intensive process requires significant memory and processing power to manage the model's parameters and perform calculations. The inference task for LLMs like Llama 2 involves detailed computations, including handling of matrices for attention mechanisms and memory management to ensure efficient utilization of hardware resources
- A general overview of LLMs highlights their ability to achieve general-purpose language generation and understanding by learning from vast amounts of text data. These models are built on architectures such as transformers, and recent developments have expanded their capabilities to include various tasks without extensive fine-tuning, using techniques like prompt engineering.
- For serving LLM inference, platforms and tools are designed to streamline the process. For instance, BentoML offers functionalities for easy deployment and integration with frameworks like Hugging Face and LangChain. It supports model quantization, modification, and experimental fine-tuning. However, it lacks built-in distributed inference capabilities. Ray Serve is another tool that facilitates scalable model serving with optimizations for deep learning models, offering features like response streaming and dynamic request batching, which are crucial for efficiently serving LLMs.
2023a
- (NVIDIA, 2023) ⇒ NVIDIA. (2023). "Mastering LLM Techniques: Inference Optimization". In: NVIDIA Developer Blog.
- QUOTE: Optimizing inference for large language models involves techniques such as quantization, pruning, and efficient memory management.
These methods are essential for deploying LLMs on resource-constrained environments like edge devices.
- QUOTE: Optimizing inference for large language models involves techniques such as quantization, pruning, and efficient memory management.
2023b
- (Spector & Ré, 2023) ⇒ Benjamin Spector, and Christopher Ré. (2023). “Accelerating LLM Inference with Staged Speculative Decoding.” In: arXiv preprint arXiv:2308.04623. doi:10.48550/arXiv.2308.04623
2023b
- (NeuralBits, 2023) ⇒ NeuralBits. (2023). "Understanding LLM Inference". In: NeuralBits Substack.
- QUOTE: LLM inference refers to the process of generating outputs from a pre-trained large language model.
It encompasses tasks such as text generation, summarization, and question answering
- QUOTE: LLM inference refers to the process of generating outputs from a pre-trained large language model.