Guide to Self-Hosting Large Language Models (LLMs): June 2025

Introduction

Self-hosting large language models (LLMs) provides significant benefits: data privacy, cost savings, offline access, and customization that is often impossible with cloud APIs. In 2025, a wide variety of free and open-source platforms make this more accessible than ever, whether you're running a small 7B model on a MacBook or deploying a 70B model on a rack-mounted GPU server.

This guide compares the top self-hosted LLM tools across performance, ease of use, scalability, hardware needs, and extensibility. This guide does not attempt to compare the various front-end user interfaces (UIs) such as Open WebUI and LibreChat, but does reference them, indicating that some LLMs may lack a friendly UI and you may want to consider installing one. For a comparison of UIs, read our Self Hosted AI UI Guide in June 2025.

✅ TLDR

• Best for Beginners: GPT4All or Jan
• Best for Technical Mac and Linux Users: Ollama
• Best for Enterprise or Scale: vLLM

📦 Self-Hosting LLM Solutions

Ollama

Ollama is one of the most user-friendly local LLM platforms, especially for macOS users. It abstracts away complex setup processes and presents a CLI tool where models can be pulled and run with a single command. It focuses on efficiency and low-latency on modest hardware. It does not have a "pretty" UI included, so reference the Self Hosted AI UI Guide in June 2025 for UI options.

• Pros:

  • Simple, Docker-like model management.
  • Great for Apple Silicon (M1–M4); supports Metal acceleration.
  • Community support for custom models like LLaMA 3, Mistral.
  • Works out-of-the-box without requiring a GPU.

• Cons:

  • Not designed for enterprise-level scaling or high concurrency.
  • Lacks native image/audio model support.

• Hardware: MacBook Pro, Mac Mini, or Linux PCs; GPU optional for performance.

• License: Free and open-source.

• UI Compatibility: Works with Open WebUI, LibreChat, LobeChat.

• Performance: Optimized for quantized models (e.g. 4-bit) on CPU. Handles 7B/13B models smoothly. Good for light personal use.

vLLM

vLLM is designed for maximum throughput, concurrency, and minimal latency when hosting large-scale LLMs. It uses innovative techniques like continuous batching and speculative decoding to squeeze maximum utility out of GPU resources. It does not have a UI included, so reference the Self Hosted AI UI Guide in June 2025 for UI options.

• Pros:

  • Near state-of-the-art performance for serving LLMs.
  • Supports OpenAI-compatible APIs.
  • Optimized for multi-user production setups.

• Cons:

  • Requires significant GPU resources.
  • Deployment is technical (better for ML engineers or infra teams).

• Hardware: High-end NVIDIA GPUs (e.g. A100, H100, or RTX 3090/4090). Multi-GPU setups for larger models.

• License: Apache 2.0.

• UI Compatibility: Works with LibreChat, Open WebUI.

• Performance: Exceptional. Capable of real-time chat services with high user concurrency.

GPT4All

GPT4All is a great entry point for non-technical users who want a downloadable, fully offline chat assistant. It comes as a desktop application and bundles models for easy local use.

• Pros:

  • One-click install for Windows, macOS, Linux.
  • Runs on CPU; no GPU required.
  • Includes document Q&A features (RAG).

• Cons:

  • Performance limited to small models.
  • Not extensible with other models or plugins.

• Hardware: Any consumer laptop with 8GB+ RAM.

• License: Apache 2.0; models vary.

• UI Compatibility: Self-contained; doesn’t support external UIs.

• Performance: Slow token generation; best for low-demand, single-user use.

Jan

Jan is a minimalist desktop chat client designed as an alternative to GPT4All or LM Studio. It prioritizes simplicity and privacy.

• Pros:

  • Clean and intuitive UI.
  • Works without internet access.

• Cons:

  • Limited to embedded models.

• Hardware: Any laptop with decent CPU and RAM.

• License: Open-source.

• UI Compatibility: Self-contained only.

LocalAI

LocalAI is an all-in-one LLM and multimodal hosting stack designed as a local drop-in replacement for the OpenAI API. It supports text generation, image creation, speech processing, and embeddings. Think of it as your local ChatGPT + DALL·E + Whisper in one stack.

• Pros:

  • Full OpenAI API compatibility.
  • Works with a range of LLMs and stable diffusion models.
  • Docker-based deployment, suited for teams.
  • Includes vector store (LocalRecall) and agent framework (LocalAGI).

• Cons:

  • Slightly less performant than vLLM for high-throughput text serving.
  • Still evolving, some multimodal features may require tinkering.

• Hardware: Works on CPU-only systems; GPU boosts performance. Best with 16GB RAM+ and optional NVIDIA GPU.

• License: MIT License (very permissive).

• UI Compatibility: Works with LibreChat, Open WebUI.

• Performance: Serves small-to-medium models well; handles multiple concurrent requests but isn’t built for extreme scale.

SGLang

SGLang is a fast-serving framework for large language models and vision-language models, co-designed with a special frontend language to make interactions faster and more controllable. It focuses on high-throughput inference and advanced features like prefix-cache reuse, continuous batching, and parallelism to maximize performance. Targeted at enterprise-scale deployments, it can serve both text and multimodal models with state-of-the-art efficiency. (No built-in UI is provided; see the Self Hosted AI UI Guide in June 2025 for front-end options.)

• Pros:

  • Highly optimized runtime (RadixAttention caching, zero-overhead scheduler, speculative decoding, etc.) for minimal latency.
  • Supports complex LLM workflows via a flexible “Structured Generation” scripting language.
  • Extensive model compatibility (Llama, Mistral, DeepSeek, multimodal LLaVA, etc.) out-of-the-box.
  • Strong community and industry adoption (deployed across major firms on thousands of GPUs).

• Cons:

  • Geared toward advanced users – deploying and tuning SGLang requires ML engineering expertise.
  • Full benefits seen only on powerful hardware; less useful on low-end machines.
  • The custom DSL for prompt programming has a learning curve for newcomers.

• Hardware: Best with high-end NVIDIA or AMD GPUs (supports both); scalable from single GPU to multi-node clusters for very large models. CPU-only mode exists but is slow for anything sizeable.

• License: Apache 2.0.

• UI Compatibility: OpenAI-compatible REST API for integration. Works with any UI that supports OpenAI API (e.g. LibreChat, Open WebUI) or custom interfaces.

• Performance: Exceptional. SGLang often outperforms other frameworks in throughput and latency, thanks to its low-level optimizations. It’s capable of serving large models with real-time speed and high concurrency in production settings.

Aphrodite Engine

Aphrodite Engine is an open-source LLM inference engine designed for large-scale deployments and originally used to power the PygmalionAI chat service. It emphasizes serving many concurrent users by combining techniques from projects like vLLM (for fast attention and batching) and ExLlama (for efficient quantized model handling). It provides an OpenAI-compatible API endpoint to host virtually any Hugging Face model with ease. (No native GUI is included; see the Self Hosted AI UI Guide in June 2025 for UI options.)

• Pros:

  • Built for massive concurrency – can handle thousands of users with low latency, using continuous batching and paged attention (from vLLM).
  • Broad model support and easy integration: runs nearly any HF-format LLM (GPT-J, Llama-family, etc.), with extensive quantization compatibility for smaller footprints.
  • OpenAI API drop-in replacement, making it simple to plug into existing applications or UIs.
  • Wide hardware compatibility – supports NVIDIA and AMD GPUs, CPUs, and even specialized accelerators like TPUs and AWS Inferentia.

• Cons:

  • Deployment and configuration are technical; intended for experienced users or devops teams (though Docker/Pip makes basic setup easier).
  • Uses an AGPL license, which may be restrictive for commercial modifications or closed-source use cases.
  • Requires robust hardware for optimal performance; running large models on minimal gear will be slow or impractical.

• Hardware: Optimized for GPUs – works with high-memory NVIDIA or AMD cards, and can utilize multi-GPU setups for big models. Also supports CPU-only mode and exotic hardware (TPUs, XPUs), but with reduced performance.

• License: AGPL-3.0.

• UI Compatibility: OpenAI-compatible REST API. Can be used with any OpenAI-API UI (e.g. LibreChat, Open WebUI) or custom apps. No dedicated GUI (primarily headless server).

• Performance: Excellent. Achieves throughput and response times on par with other top inference engines. Uses state-of-the-art optimizations like speculative decoding and GPU kernel fusion to accelerate generation. Suitable for production chat services and high-load scenarios, as proven by its use in the Pygmalion web app.

LM Studio

LM Studio is a full local AI toolkit for running open-source models with a user-friendly desktop UI. It lets you discover, download, and chat with models (in GGUF or Apple’s MLX format) on your own machine, no internet required. Despite its simplicity for beginners, it also offers advanced features like parameter tuning, a built-in retrieval Q&A (document chat) mode, and the ability to serve models as a local API endpoint for developers. (It includes a dedicated UI, but also supports external clients via an OpenAI-like API.)

• Pros:

  • Very easy setup and clean interface – no technical expertise needed to start chatting with LLMs.
  • Cross-platform support: runs on Windows, macOS (native Apple Silicon support via MLX), and Linux PCs.
  • Offline-first privacy: all data and model processing stays local (no cloud required).
  • Extra features: multi-model management (load several models concurrently), chat history and prompt saving, and local document QA (RAG) built in.

• Cons:

  • The GUI application is not fully open-source (free for personal use, but a paid license is needed for business use).
  • Limited to text models – does not support image generation or other modalities yet.
  • Performance is constrained by local hardware; large models (beyond ~13B parameters) may be too slow or not run at all on typical laptops.
  • Fewer extension/plugin options compared to developer-focused frameworks (core functionality is what’s provided in-app).

• Hardware: No dedicated GPU required. Optimized for Apple M-series chips (leverages CPU/GPU via MLX) and for modern x86 CPUs with AVX2 instructions. High RAM (16GB+) helps with bigger models. (NVIDIA/AMD GPU acceleration on PC is experimental via Vulkan in certain versions.)

• License: Free for personal use (proprietary UI). Core engine/CLI are MIT open-source. Commercial use requires a license from the vendor.

• UI Compatibility: Provides its own graphical chat UI. Additionally, it can run an OpenAI-compatible HTTP server on localhost for integration with external applications or UIs. (For example, you can point the OpenAI Python SDK to localhost to use LM Studio’s model server.)

• Performance: Good with smaller models on CPU – e.g. 7B chats at acceptable speeds, especially on M1/M2 Macs using the optimized MLX engine. However, generation is noticeably slower than GPU-hosted solutions for larger models. Best suited for single-user or low-demand scenarios, not heavy concurrent usage.

OpenLLM

OpenLLM is an open-source platform by BentoML that lets you run any open-source LLM as an OpenAI-compatible API service with a single command. It streamlines local deployment of models like Llama 3, Mistral, Qwen, etc., packaging them into a self-hosted server that includes a web-based ChatGPT-style UI and endpoints for programmatic access. Under the hood, OpenLLM incorporates performance optimizations (borrowing from vLLM and other backends) to support fast, production-grade inference in both local and cloud environments.

• Pros:

  • Easy usage – simple CLI (openllm serve <model>) pulls and serves models without manual setup.
  • High performance – optimized for throughput and concurrency. In benchmarks, OpenLLM handled significantly more requests per second than Ollama on the same hardware (8× throughput in one test).
  • Supports a wide range of models out-of-the-box, including latest Llama 3.x series, Mistral, Qwen, and their quantized versions.
  • Built-in web UI for chatting, plus a CLI chat mode, making testing and interaction straightforward.
  • Production-ready integrations: Docker/Kubernetes deployment supported, and one-command deploys to BentoCloud for managed hosting.

• Cons:

  • Primarily a developer tool – requires using the terminal/command-line to launch and manage models (no native desktop app).
  • Model downloading and initial startup can be heavy (it uses Docker/Bento behind scenes), which might be overkill for casual users.
  • While the included UI is handy, it’s basic compared to dedicated chat apps; for richer experience you may still pair it with another front-end.
  • As with all solutions, running big models locally demands strong hardware (OpenLLM itself doesn’t magically enable 70B models on a laptop).

• Hardware: Flexible. Small models can run on CPU, but for best results use an NVIDIA GPU (it will utilize Tensor Cores for speed). Multi-GPU support is available for larger models (e.g. serving a 70B model across 4×80GB GPUs). Essentially, any setup from a single PC to a multi-GPU server or cloud instance can work.

• License: Apache 2.0.

• UI Compatibility: Offers a built-in browser UI at the /chat endpoint for quick interaction. Also exposes a standard OpenAI API endpoint (/v1), so it works seamlessly with existing OpenAI-compatible clients (third-party UIs, SDKs, etc.). For example, UIs like LibreChat or Open WebUI can treat OpenLLM as if it were the OpenAI API.

• Performance: Excellent. Designed for high-load scenarios – it scales to serve multiple users with minimal latency. OpenLLM’s optimized backend yields low time-to-first-token and high token throughput, rivalling dedicated inference engines. In side-by-side tests, it maintained much faster generation under load than simpler local servers. This makes it suitable not just for personal use but also as an engine behind production applications.

🧠 Which should I use?

1. Non-Technical Computer User

Don't want to hop into the terminal? No problem, I wouldn't force that on you. Install GPT4All or Jan just like you would any other application to start chatting!

• Solution: GPT4All or Jan
• Why: Fully offline, plug-and-play, great for air-gapped environments.

2. Technical MacBook User (M1–M4)

Not afraid of a little bit of work in the terminal? Ollama is your best choice.

• Solution: Ollama + an AI UI (see Self Hosted AI UI Guide in June 2025)
• Why: Native Metal support, low overhead, great UI pairing.

3. Small Business with Low-End Devices

• Solution: Ollama or vLLM + an AI UI (see Self Hosted AI UI Guide in June 2025), all running on a server in your office or in the cloud.
• Why: Centralized access, compatible with thin clients.

4. Enterprise Deployment

• Solution: vLLM + an AI UI (see Self Hosted AI UI Guide in June 2025), all running on a server in your office or in the cloud.
• Why: Scalable, OpenAI API-compatible, secure on-prem hosting.

All tools mentioned are free and open-source. Choose your stack based on performance needs, hardware availability, and user base size. And remember: even a laptop can run powerful LLMs today.

Want help setting up your local AI? Learn about AI Deployment Options.

Happy self-hosting!