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.
We evaluated the top 4 self-hosted tools based on their usability, performance, and GitHub stars:
LLM Compatibility Calculator
Enter your configuration details below to instantly 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.
Self-hosted LLMs landscape
Top 4 self-hosting tools: Differentiating features
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
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 in a container image for easier deployment, these models enable users to run model inference directly on their own hardware, offering greater control and flexibility than closed-source alternatives.
Advantages of self-hosted LLMs
Full control and deeper customization
Self-hosting an LLM gives users direct access to the model weights and system configuration. This level of control allows organizations to select the right model for their specific needs, modify its behavior, or even fine-tune it using their own training data. When compared with cloud-based services, local LLMs allow more flexible experimentation because there are no imposed limits on context window size, inference settings, environment variables, or integration methods.
This is especially useful for engineers building LLM apps who need tight control over memory usage, latency, or chat history processing.
Enhanced data privacy
When models run on your own hardware, sensitive information stays within your infrastructure. This is valuable for workloads involving internal documents, knowledge bases, or regulated data. A self-hosted LLM does not require sending inputs to a third-party provider, removing the need to rely on external compliance practices. The result is greater control over privacy and reduced exposure to data leaks.
Cost-effectiveness in the long run
Self-hosting an LLM can appear expensive at first because of hardware requirements, such as consumer-grade GPUs or small servers. However, once the system is in place, the cost of running inference locally may become cheaper than paying recurring API usage fees, especially for teams generating high-volume requests.
Running LLMs on open-source LLMs also avoids vendor lock-in and gives users freedom to switch to smaller models or larger models, depending on their cost and performance goals.
Flexibility with open-source models
Many open-source LLMs are available on platforms like Hugging Face, offering users a wide range of model sizes, architectures, and quantized versions to explore. Self-hosting allows developers to test different parameter counts, experiment with efficient quantization formats such as GGUF, and deploy models in Docker containers or other lightweight environments. This freedom makes it easier to scale, test new ideas, and adapt the system to specific use cases.
User-friendly local tools
Modern tools make local LLMs more accessible. Applications such as LM Studio, Ollama, Open WebUI, or similar desktop apps provide a straightforward web interface or single-command deployment workflow. These tools simplify managing available models, running inference, and monitoring performance without needing deep infrastructure expertise. For many users, this lowers the barrier to exploring and experimenting with their own LLM locally.
Disadvantages of self-hosted LLMs
Significant hardware investment
Running larger models or hosting a high-throughput hosted LLM on your local machine requires strong hardware. GPU memory becomes the main limitation, especially for larger models with higher parameter counts.
Even with optimizations such as quantized versions or smaller models, some tasks still demand GPUs with 16–48 GB of VRAM, which may not be feasible for smaller teams. Using edge devices is possible, but performance often declines when model size exceeds what the device can handle.
Complex deployment and maintenance
Self-hosting involves more than downloading a model file. Users must handle dependencies, memory optimization, monitoring, environment variables, and updates. Troubleshooting issues such as kernel mismatches, CUDA errors, or model incompatibilities may require specialized knowledge. Unlike cloud-based services, where the provider handles infrastructure, self-hosted setups demand ongoing attention to maintain optimal performance.
Limited access to proprietary models
Leading proprietary models (e.g., GPT-4.5, Grok 3, or other closed-source systems) cannot be downloaded or run as self-hosted LLMs. They are only available through their vendor’s API, often through an OpenAI-compatible API endpoint. This means users who choose an entirely local deployment may miss out on specific capabilities, especially when proprietary models outperform open-source alternatives for particular tasks.
Performance tuning becomes your responsibility
Achieving better performance on a self-hosted system is not automatic. Users must tune inference settings, adjust batching strategies, manage model sharding, and ensure efficient hardware utilization.
When the system slows down, the burden of diagnosing memory bottlenecks, low throughput, or suboptimal GPU usage falls entirely on the user. Cloud providers usually handle these optimizations internally, so teams switching to local LLMs should expect to invest time into maintaining speed and reliability.
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 offloading 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 by converting high-precision values (such as 0.9877 in the Original Matrix) to lower-precision representations (such as 1.0 in the Quantized Matrix). This process reduces model size and can speed up computation, albeit at the potential cost of 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, with two GPUs, the model parameters are distributed across both GPUs, while each maintains its own KV cache.
Off-loading
Parameter offloading 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 offloading, users can 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, enabling the deployment of 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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