Inference.net | Llm Serving

Picture this: You’ve integrated a large language model into your application, and it's not meeting your expectations. The output is delayed, and your users are frustrated. Performance bottlenecks are ruining the seamless experience you hoped to provide. The problem likely stems from the deployment of the LLM. LLM serving, or how large language models are deployed and scaled, is critical in determining performance. This blog will explore the ins and outs of LLM serving and offer tips to help you efficiently deploy and scale your LLM with low latency and optimal cost. You can integrate LLMs into your applications and avoid performance bottlenecks to deliver a smooth user experience. Additionally, understanding AI Inference vs Training is crucial to optimizing model performance and ensuring smooth deployment.

One way to achieve your goals is by leveraging AI inference APIs. These tools can help you efficiently deploy and scale your LLM, reducing latency and cutting costs.

What is LLM Serving and Its Importance

LLM serving is the backbone of deploying large language models in real-world applications. These models have transformed AI by powering natural language processing, code generation, and conversational AI. Deploying them at scale presents significant engineering challenges.

LLM serving ensures efficient, scalable deployment by handling live inference requests in production environments. It involves key processes such as model deployment, API integration, scalability management, and performance monitoring. Adequate LLM serving empowers organizations to deliver intelligent, humanlike interactions seamlessly by bridging the gap between computationally intensive models and real-world applications.

LLM Serving Metrics: Throughput and Latency

When deploying large language models, two critical performance metrics come into play: throughput and latency.

Throughput measures how many tokens the inference server generates per second across multiple user requests. A higher throughput means the system can efficiently handle more users and process responses faster.
Latency refers to the time taken to generate a complete response. In streaming mode, it measures explicitly the time to first token (TTFT)—how long it takes for the first token to appear after a request. Lower latency results in a more responsive user experience.

Simply put, latency is what users feel—the wait time for a response. Throughput determines how many users the system can serve efficiently and how fast new words appear in streaming mode. Optimizing both ensures a smooth and scalable user experience.

Solutions for Optimizing LLM Serving

Deploying large language models (LLMs) in a production environment is far from trivial. These models can range from several gigabytes to tens of gigabytes, requiring GPUs with sufficient memory to maintain accuracy and performance. While smaller models can run efficiently on personal hardware, enterprise-scale deployments, such as those on OpenShift, demand more robust solutions.

A deployed LLM is typically accessed by multiple applications and users simultaneously. Adding more resources isn’t always feasible due to infrastructure constraints unlike traditional scaling methods. Techniques like batching queries, caching, and buffering must be explicitly managed to optimize response times.

LLMs require fine-tuning at load time, making a one-size-fits-all model loader ineffective. Fortunately, various solutions exist to address these challenges, enabling efficient and scalable LLM serving in production environments.

Top 15 LLM Serving Frameworks and Solutions

1. Inference.net

Inference.net offers OpenAI-compatible serverless inference APIs for top open-source LLM models, giving developers the highest performance, lowest cost, and most flexibility in the market.

In addition to standard inference, Inference.net provides specialized batch processing for large-scale async AI workloads and document extraction capabilities designed explicitly for RAG applications. Start building with $10 in free API credits and experience state-of-the-art language models that balance cost-efficiency with high performance.

2. WebLLM

WebLLM is a high-performance, in-browser LLM inference engine powered by WebGPU hardware acceleration. AI models like Llama 3 can run directly in the browser without server dependencies. WebLLM supports real-time AI interactions with features like streaming responses, structured JSON generation, and logit-level control.

It offers full compatibility with the OpenAI API, allowing developers to integrate AI easily into web applications while ensuring privacy and efficiency through its modular design. WebLLM is ideal for building:

Chatbots
Assistants and more

3. LM Studio

LM Studio is a powerful desktop application that enables users to run large language models (LLMs) completely offline on their local machine. It supports various hardware configurations and allows users to experiment with different models and configurations.

LM Studio offers a user-friendly chat interface and an OpenAI-compatible local server, making it versatile for developers who want to integrate LLMs into their applications or experiment with various models.

4. Ollama

Ollama is a powerful, open-source LLM serving engine that enables local inference. This capability allows users to run language models directly on their machines without relying on cloud services. This capability enhances privacy, reduces latency, and provides greater control over the models used. It is an ideal solution for developers and organizations leveraging AI while maintaining data security.

5. vLLM

vLLM (Virtual Large Language Model) is an advanced open-source library designed for high-performance inference and serving of Large Language Models (LLMs). It leverages innovative features such as PagedAttention for efficient memory management, continuous batching for optimal GPU utilization, and support for various quantization methods to enhance inference speed.

vLLM is compatible with an OpenAI-like API and integrates seamlessly with the Hugging Face ecosystem, making it a versatile tool for AI practitioners.

6. LightLLM

LightLLM is a Python-based framework for fast and efficient inference of Large Language Models (LLMs). Renowned for its lightweight design, easy scalability, and high-speed performance, LightLLM leverages the strengths of several well-regarded open-source implementations, such as:

FasterTransformer
TGI
vLL
FlashAttention

The framework supports advanced features that optimize GPU utilization and memory management, making it ideal for development and production environments.

7. OpenLLM

OpenLLM is a versatile platform that simplifies the self-hosting of large language models (LLMs). It allows developers to run state-of-the-art open-source models as OpenAI-compatible APIs, like:

Llama
Qwen
Mistral, and more

With built-in chat interfaces, optimized inference backends, and seamless integration with Docker, Kubernetes, and BentoCloud, OpenLLM streamlines deploying, managing, and interacting with custom and popular LLMs.

8. HuggingFace TGI

Hugging Face Text Generation Inference (TGI) is a robust and scalable solution designed to serve large language models efficiently. Optimized for inference workloads, TGI supports various open-source and custom models, providing fast and scalable text generation services.

It is particularly suited for high-performance environments where speed and resource efficiency are critical and integrates seamlessly with Hugging Face’s model hub.

9. GPT4ALL

GPT4All by Nomic is a series of models and an ecosystem for training and deploying models locally on your computer. The platform enables users to run large language models (LLMs) efficiently on desktops and laptops, emphasizing privacy by keeping data on the device. Inspired by OpenAI’s ChatGPT, the GPT4All desktop application offers a familiar interface for interaction.

Integrating Nomic’s embedding models allows users to easily pull information from local documents into their chats, providing a streamlined experience. GPT4All supports popular model architectures like LLaMa and Mistral and utilizes the efficient llama.cpp and Nomic's C backend, making it accessible for users across various skill levels.

10. llama.cpp

llama.cpp is an optimized, dependency-free C/C++ implementation designed for running large language models (LLMs) like Llama locally. As the default implementation for GGML-based models, it forms the backbone of many tools in the LLM ecosystem.

This library supports various bindings, including Python, enabling seamless interaction with different models. Engineered for high performance, llama.cpp runs efficiently across diverse hardware configurations, from Apple Silicon to x86 architectures. It also includes advanced features like multi-level integer quantization and custom CUDA kernels for NVIDIA GPUs, making it a powerful solution for local and cloud-based LLM deployments.

11. Triton Inference Server with TensorRT-LLM

NVIDIA’s Triton Inference Server is an enterprise-grade platform designed to streamline the deployment of large language models (LLMs) in production. It supports frameworks like TensorFlow, PyTorch, and ONNX, ensuring efficient model serving with high performance.

When paired with TensorRT-LLM, an open-source framework optimized for LLM inference, developers can fine-tune models for maximum efficiency. TensorRT-LLM accelerates inference using optimized kernels, paged attention, and efficient key-value (KV) caching, making it ideal for high-throughput applications.

12. DeepSpeed MII

The DeepSpeed Model Implementation for Inference (MII) aims to make low-latency, low-cost inference of powerful models feasible and easily accessible.

The DeepSpeed Model Implementation for Inference (MII) aims to make low-latency, low-cost inference of powerful models feasible and easily accessible.

13. RayLLM with RayServe

Built on Ray Serve, RayLLM benefits from a distributed computing framework that provides specialized libraries for data streaming, training, fine-tuning, hyperparameter tuning, and serving, simplifying the development and deployment of large-scale AI models.

RayLLM supports the deployment of multi-model endpoints and provides server capabilities. In contrast, engine capabilities are supplied by integrations such as continuous batching, paged attention, and other optimization techniques through TGI and vLLM integration.

14. TensorRT-LLM

TensorRT-LLM is an open-source library designed to accelerate and optimize inference on the latest LLMs using NVIDIA Tensor Core GPUs. It integrates TensorRT’s Deep Learning Compiler, optimized FasterTransformer kernels, pre- and post-processing, and multi-GPU/multi-node communication into a streamlined Python API for defining, optimizing, and executing LLMs in production.

TensorRT-LLM leverages tensor parallelism to enable efficient inference across multiple GPUs and servers with minimal developer intervention. It includes highly optimized, ready-to-run versions of leading LLMs like:

Meta Llama 2
OpenAI GPT-2/GPT-3
Falcon
Mosaic MPT

The library also offers a C++ runtime for executing LLM engines, featuring token sampling, KV cache management, and in-flight batching (continuous batching). This technique reduces queue wait times, eliminates the need for padding requests, and maximizes GPU utilization.

TensorRT-LLM simplifies LLM deployment, providing peak performance and flexibility without requiring deep C++ or NVIDIA CUDA expertise.

15. Text Generation Inference (TGI)

At HuggingFace, a Rust, Python, and gRPC server powers HuggingChat, the Inference API, and the Inference Endpoint. Text Generation Inference utilizes tensor parallelism (Accelerate) for faster inference on multiple GPUs.

It supports continuous batching for increased throughput, quantization, Paged and FlashAttention, token streaming using Server-Sent Events (SSE), and many more. Logit warper (different parameters such as temperature, repetition penalty, top-k, top-n, etc.) supports an optimized set of specific LLMs. The license for usage has been changed. It is not free of charge for commercial use.

Which LLM Serving Model to Use?

Model Performance: The Big Picture

When choosing a large language model for serving tasks, you'll want to pick one that performs well for your specific use case. Naturally, performance can vary greatly based on the task at hand, so it helps to be specific about your needs and pick a model that has been benchmarked on similar tasks. Several benchmarks have been published, as well as constantly updated rankings.

Dataset Quality: What Was the Model Trained On?

It also matters what data the model was trained on.

Was it curated or just raw data from anywhere?
Does it contain NSFW material?
What’s the license?

Some datasets are provided for research only or non-commercial use, while others are more permissive and allow for all sorts of applications.

Model License: What Can You Actually Do With It?

Models themselves also have licenses, which can be quite different from the data licenses mentioned above. Some are fully open-source, while others claim to be. They may be free to use in most cases, but have some restrictions attached to them (looking at you, Llama 2).

Model Size: Does It Fit Your Hardware?

The size of the model may be the most restrictive point for your choice. The model simply must fit on the hardware you have at your disposal, or the amount of money you are willing to pay.

Start Building with $10 in Free API Credits Today!

Inference delivers OpenAI-compatible serverless inference APIs for top open-source LLM models, offering developers the highest performance at the lowest cost in the market. Beyond standard inference, Inference provides specialized batch processing for large-scale async AI workloads and document extraction capabilities designed explicitly for RAG applications.

Start building with $10 in free API credits and experience state-of-the-art language models that balance cost-efficiency with high performance.

What is LLM Serving & Why It’s Essential for Scalable AI Deployment

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What is LLM Serving and Its Importance