The use of LLMs has become inevitable, but relying solely on cloud-based APIs can be limiting due to cost, reliance on third parties, and potential privacy concerns. That’s where self-hosting an LLM for inference (also called on-premises LLM hosting or on-prem LLM hosting) comes in.
Integrations (wide compatibility)
Developers (high performance)
Local RAG applications
Beginner-friendly experimentation
LLM Compatibility Calculator
Enter your configuration details below to instantly to estimate the RAM needed based on model parameters, quantization method, and your hardware specs:
The available quantization methods and precision bits for vendors are taken from Hugging Face transformers library documentation.1
You can read more about the optimization techniques to host LLMs locally.
Top self-hosting tools analyzed
Tool | Ease of Use | Performance | Github Stars |
|---|---|---|---|
Ollama | Medium | Medium | 136k |
vLLM | Low | High | 44k |
AnythingLLM | High | Medium | 43k |
LM Studio | High | Low | 3k |
Ollama
Ollama is an open-source tool that simplifies running LLMs locally on macOS, Linux, and Windows. It bundles models and configurations, making setup straightforward for various popular LLMs.
Ollama prioritizes ease of use and privacy via offline operation and supports integrations with developer tools like LangChain and user-friendly interfaces like Open WebUI, which provides a chat-based graphical experience for interacting with the locally hosted models.
It allows users and developers to easily run and interact with LLMs on their personal machines, including multimodal models, making it ideal for local development and privacy-conscious usage.
vLLM
vLLM is a high-performance engine designed for fast and memory-efficient large language model serving. It uses techniques like PagedAttention and continuous batching to maximize throughput and reduce memory requirements during inference.
It supports distributed execution and various hardware (NVIDIA, AMD, Intel) and offers an OpenAI-compatible API for integration. vLLM targets developers and researchers focused on optimizing LLM deployment in production environments. It excels at scalable, high-speed model serving.
AnythingLLM
AnythingLLM is an open-source desktop tool for running large language models (LLMs) on macOS, Windows, and Linux. It enables users to apply RAG to process documents like PDFs, CSVs, and codebases, retrieving relevant information for chat-based interactions without coding.
It operates offline by default for privacy and integrates RAG to enhance responses using user-provided data. AnythingLLM suits developers and beginners exploring document-driven LLM use cases, with additional support for AI agents and customization through a community hub.
LM Studio
LM Studio is a beginner-friendly desktop application for discovering, downloading, and experimenting with large language models locally across macOS, Windows, and Linux. It features an intuitive graphical interface for managing models from sources like Hugging Face and interacting via a chat UI or a local server.
LM Studio simplifies experimentation with features like offline RAG and leverages efficient backends like llama.cpp and MLX. It caters primarily to beginners and developers wanting an easy-to-use environment for exploring local LLMs.
Open-source large language models
Provider | Model |
|---|---|
Alibaba | Qwen2.5-Omni-7B |
Alibaba | Qwen2.5-VL-72B-Instruct |
DeepSeek | DeepSeek-R1 |
DeepSeek | DeepSeek-V3-0324 |
Google | Gemma-3-27b-it |
Google | Gemma-2-27b-it |
Meta | Llama-4-Maverick-17B-128E-Original |
Meta | Llama-4-Scout-17B-16E-Original |
Meta | Llama-3.3-70B-Instruct |
Mistral | Mistral-Small-3.1-24B-Instruct-2503 |
Open-source LLMs are models whose architecture and model files (containing weights, often with billions of more parameters) are publicly available, allowing anyone to download, modify, and use them.
Platforms like Hugging Face serve as central repositories, making it easy to access these models for tasks like building a self-hosted LLM solution. Often packaged within a container image for easier deployment, these models enable users to run model inference directly on their own hardware, offering greater control and flexibility compared to closed-source alternatives.
Advantages of self-hosted LLMs
- Full control and deeper customization: By hosting models on your own infrastructure, you gain complete control over how the system is configured and utilized. This allows you to fine-tune the models beyond the limitations of standard APIs, tailoring them to specific use cases or integrating them into unique workflows.
- Enhanced data security: Keeping the model and data within your own systems eliminates the need to send sensitive information to external providers. This approach reduces the risk of data exposure and ensures compliance with strict data protection requirements.
- Cost-effectiveness: Although the initial investment can be high, self-hosting may result in long-term savings. By leveraging your own infrastructure and open-source models, you can avoid recurring subscription fees associated with managed services.
- Flexibility with open models: A wide range of open-source LLMs are available for deployment. Hosting these models yourself provides freedom from vendor lock-in and enables experimentation with different architectures and capabilities.
- User-friendly management: Some self-hosting solutions offer management interfaces, such as web-based dashboards, which simplify deployment, monitoring, and interaction with hosted models.
Disadvantages of self-hosted LLMs
- Significant hardware investment: Running advanced LLMs requires powerful servers with large GPU capacity. Procuring and maintaining this hardware can be expensive and may not be practical for smaller organizations.
- Complex LLM deployment: Setting up models involves more than just downloading code. Dependencies must be managed, configurations need to be adjusted, and technical troubleshooting is often necessary. This creates a steep barrier for teams without prior experience.
- Limited access to proprietary models: Proprietary models such as GPT-4.5 or Grok 3 are not available for self-hosting. They are accessible only through managed APIs provided by the vendors, which means you may miss out on the latest advancements if you choose self-hosting exclusively.
- Performance optimization burden: Maintaining acceptable response times and throughput is an ongoing responsibility. This requires continuous tuning of hardware, software, and model configurations. Unlike managed services, where providers handle optimization, self-hosted deployments place this responsibility entirely on the user.
Optimizing LLMs for self-hosting
Running AI models like large language models on your own hardware can be challenging due to their size and resource needs, but several techniques help manage their model weights effectively. Methods such as quantization, multi-GPU support, and off-loading improve efficiency, making it possible to host these models at home or work.
Quantization
Quantization, as illustrated in the figure below, often involves reducing the precision of model weights in machine learning by converting high-precision values (like 0.9877 in the Original Matrix) to lower-precision representations (like 1.0 in the Quantized Matrix). This process decreases model size and can speed up computation, albeit with a potential loss in accuracy.
Figure 1. Example of a random matrix of weights with four-decimal precision (left) with its quantized form (right) by applying rounding to one-decimal precision.2
Multi GPU support
As illustrated in the figure, distributing the large ‘Model Parameters’ across multiple GPUs (GPU 1 and GPU 2) allows users to run larger, more capable models on hardware they manage, overcoming single-GPU memory limitations and making self-hosting feasible. This effectively pools resources, optimizing the use of available hardware to meet the demanding requirements of modern LLMs.
Figure 2. Comparison of GPU memory allocation for a large language model. On the left, a single GPU holds both the model parameters and the KV cache. On the right, using two GPUs, the model parameters are distributed across both GPUs, while each GPU maintains its own separate KV cache.
Off-loading
Parameter off-loading optimizes LLMs for self-hosting by addressing the limited memory available on consumer GPUs. This technique involves dynamically moving parts of the large model, such as inactive “expert” parameters in MoE models, between the fast GPU memory and slower system RAM. By doing this, offloading allows users to run large, powerful models on accessible hardware that wouldn’t otherwise have enough dedicated GPU memory, making self-hosting feasible.3
Model sharding
Sharding, as illustrated in the image below, divides the complete “Large Language Model” into several smaller, more manageable “Model pieces.” This technique allows the distribution of these pieces across multiple devices (like GPUs) or even different types of memory within a self-hosted setup. By breaking down the model, sharding overcomes the memory limitations of individual hardware components, making it possible to run large models on personally managed infrastructure.
Figure 3. The diagram shows how a complete LLM can be divided into smaller segments or “Model pieces” to create a sharded version, facilitating distribution across multiple hardware resources or memory tiers for efficient processing and management.4
FAQ about self-hosted LLMs
Reference Links
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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