10 Best LLM Tools To Run Models Locally (October 2026)

Running an LLM locally means choosing both a model and the software that loads it, serves it, or lets you work with it. Those are separate decisions. A desktop chat app, a team interface, and an inference engine can all be useful, but they solve different problems and often work together.

We independently evaluated the ten tools below for their local workflows, practical strengths, configuration burden, and fit for individual users or developers. Ollama remains our broad recommendation for a reusable local runtime; LM Studio is the strongest desktop-first alternative. The order reflects workflow fit rather than measured inference-speed benchmarks. A lower-ranked specialist may be the best choice for a technical deployment.

This guide compares tools, not model intelligence. For downloadable model choices, see our guide to open-source and open-weight LLMs. Also distinguish local capability from local execution: many applications now offer cloud models, web search, or remote tools alongside on-device inference.

Best Tools for Running LLMs Locally Compared

Which Kind of Local AI Tool Do You Need?

For individual desktop work: start with LM Studio or Jan for chat, AnythingLLM or GPT4All for document workflows, and Msty Studio for a more organized multi-model workspace. TextGen is worth considering when experimentation and detailed controls matter more than simplicity.

For applications and shared services: Ollama provides a convenient local model server, llama.cpp offers direct engine control, and LocalAI brings multiple kinds of inference behind a common API layer. Open WebUI is a shared interface that can sit above an engine. Installing a front end does not by itself install a capable model or provide the hardware to run it.

10 Best LLM Tools to Run Models Locally

1. Ollama

This demonstration shows an Ollama-connected workflow. AnythingLLM’s current built-in desktop engine has since changed; see its profile below for the distinction.

Best for: Local model serving and developer integrations. Ollama remains our first recommendation for readers who want a reusable foundation for local AI. It manages model downloads and serves models to applications, making it useful beyond a single chat window. Developers can connect a local endpoint to their own software, while other interfaces can use Ollama as the engine underneath document or chat workflows.

The advantage is a comparatively simple model-management workflow with a broad surrounding ecosystem. A user can start with one supported model, verify that it fits the machine, and reuse that setup across applications. This separates the choice of model server from the choice of interface. However, an API that accepts a familiar request format does not guarantee that every model supports the same tools, structured outputs, or image inputs.

The important distinction today is that Ollama offers both local and cloud models. Select a downloaded local model when on-device processing is the objective. Its documentation also provides a local-only configuration that disables cloud features and web search. We keep Ollama first for flexibility and integration, not because it is automatically the simplest document workspace or the fastest engine on every device. Check model placement and memory use before increasing context or concurrent requests.

Reference: Ollama documentation and product details.

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2. LM Studio

Best for: Desktop model discovery, chat, and local APIs. LM Studio is our strongest desktop-first alternative for people who want to discover, download, and work with local models through a visual application. It also offers a local server and developer tooling, making it useful for moving from an interactive experiment to an application integration. Readers who prefer inspecting models and settings in a graphical interface may reasonably choose it ahead of Ollama.

The product now prominently includes Bionic, its agent for work and code. That broadens the experience beyond conventional chat, but readers should distinguish agent capabilities from the underlying model runtime. Downloaded models can support local chat, document interaction, and local serving; an agent’s usefulness still depends on model capability and the tools it can access. LM Studio’s offline documentation explicitly describes those core local workflows.

Its main appeal is an approachable interface without giving up a path to development. The practical limits are model compatibility, available memory, and the distinction between local and hosted execution. A cloud option with a privacy policy is still cloud inference. Before relying on an offline setup, download the required model and runtime components and test the intended workflow disconnected. Compare the developer tools you actually need rather than choosing solely on the agent demonstrations shown on the homepage.

Reference: LM Studio documentation and product details.

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3. Open WebUI

Best for: A shared self-hosted AI interface. Open WebUI is best understood as a self-hosted interface and application layer, rather than a competing model checkpoint or a single inference engine. It connects with Ollama and compatible APIs, providing a common place for conversations, knowledge, and tools. That makes it particularly relevant when several people need access to an internally managed AI service.

The architecture lets a team keep the interface while changing the model backend. It can be useful for a small internal knowledge service, with an administrator managing connections and features rather than each employee maintaining a separate desktop setup. Current documentation also distinguishes desktop installation from production deployment options. Choose the deployment method around who will operate the service and how updates, storage, and access will be managed.

We rank it third for shared access, with the qualification that self-hosting the interface does not make a remote model local. Document extraction, embeddings, and other supporting services must also be checked. Its current license includes branding conditions, so teams planning to redistribute or rebrand it should read the exact terms. Open WebUI is a strong complement to a runtime, but unnecessary infrastructure for someone who only wants occasional private chat on one laptop.

Reference: Open WebUI documentation and product details.

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4. AnythingLLM

Best for: Document workspaces and on-device productivity. AnythingLLM is our most focused recommendation for readers who want to work with their own documents rather than spend time assembling a retrieval stack. Its desktop and self-hosted options combine model access, document knowledge, workspaces, and agent capabilities. The value is in organizing a repeatable workflow around files and information instead of treating every conversation as an isolated prompt.

A material change is the built-in desktop engine. AnythingLLM now says downloaded models run through llama.cpp managed by AMD’s Lemonade, replacing the Ollama copy previously bundled inside the app. This is a change to its integrated engine, not a reason to assume every separately configured provider connection has changed. Existing users should review the migration details before following older installation tutorials that describe the bundled runtime differently.

AnythingLLM earns fourth place for document-centered productivity and could be the first choice for a local research workspace. Retrieval still depends on extraction quality, the embedding configuration, and whether the relevant passage is actually selected. Test difficult files, including tables and scanned material, rather than judging it only on a clean text document. Choose the appropriate desktop or shared deployment and verify that every selected provider stays local when that is required.

Reference: AnythingLLM documentation and product details.

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5. Jan

Best for: An open-source desktop chat alternative. Jan is an open-source desktop alternative for users who want a familiar conversational interface and the ability to choose their model. The current product supports a mix of open models and online providers, making it suitable for someone who wants a single interface while retaining the option to run supported models locally. Its open development is also relevant to users who want to inspect the project behind the application.

Its clearest use case is personal desktop chat with deliberate model selection. Start with a model that fits the computer and establish a useful baseline before adding more services or agent features. A model appearing in a product’s provider list does not establish that its largest version can run on that device. The same interface may offer both a local checkpoint and a hosted service with very different hardware and data-flow implications.

We place Jan fifth as an approachable alternative rather than a complete shared-team platform. Evaluate the release you install against the features you need: the current homepage labels memory as coming soon, so we do not count it as an established advantage. Readers seeking extensive document administration or multi-user operations should compare dedicated workspace tools. For individual chat, the simpler scope can be a benefit when it reduces setup and maintenance.

Reference: Jan documentation and product details.

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6. Msty Studio

Best for: Comparing models in a reusable AI workspace. Msty Studio is a workspace for people who use several models and want to organize the work around them. It combines local and online model access with reusable prompts, personas, Knowledge Stacks, and Split Chats. Its strongest distinction in this list is the ability to compare responses and maintain recurring working context within a visual environment.

Split Chats can help a researcher compare how models answer the same question, but agreement between models is not independent verification. Knowledge Stacks provide another useful workflow: organize source material, ask questions against it, and inspect what supports the response. Msty’s wider product ecosystem now includes additional agent and governance offerings, so buyers should distinguish Studio’s features from the capabilities of other products and plans.

Msty ranks sixth for a more structured personal or team workspace. It makes less sense when the only requirement is a small local HTTP endpoint. Local engines and hosted providers can coexist in the same application, which makes provider selection important for sensitive documents. Check the platform, plan, and deployment model required for shared controls before assuming a personal desktop installation provides enterprise administration. Choose it for the surrounding workflow, not on the assumption that its interface changes the underlying model’s accuracy.

Reference: Msty Studio documentation and product details.

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7. LocalAI

Best for: Self-hosted APIs across multiple AI modalities. LocalAI is aimed at developers and operators who want to host AI services behind familiar APIs. Its modular backends support more than language-model chat, spanning speech, vision, image generation, embeddings, and other workloads. It is especially useful when an application needs several kinds of inference and the team wants to manage them through a common service layer.

The architecture separates the client-facing API from the selected backend. Different models can use different engines, with configuration determining what runs underneath. This can reduce application-side integration work, but compatibility should be tested against the specific endpoints and features the application uses. A successful chat request alone does not establish equivalent streaming, tool calls, audio behavior, or error handling across providers.

We place LocalAI seventh because its breadth is more valuable to an engineering team than to a person seeking quick desktop chat. CPU execution makes some deployments possible without a dedicated GPU, but it does not make every model practical on modest hardware. Start with one workload, measure its latency and memory use, and add backends as needed. Multi-user or distributed operation adds capacity-planning and maintenance responsibilities that a simple local prototype does not reveal.

Reference: LocalAI documentation and product details.

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8. llama.cpp

Best for: Direct inference and hardware control. llama.cpp is the choice for readers who want to work directly with the inference engine rather than rely entirely on a desktop wrapper. Its C and C++ implementation, GGUF support, quantization tooling, hardware backends, command-line tools, and server make it an important foundation for local model deployment. Many more approachable applications depend on this layer or related components underneath their interfaces.

Direct use is valuable when model compatibility, offloading, memory behavior, or runtime configuration is the real problem. It gives a technical user a clearer path to controlling the engine and comparing configurations. That control also creates responsibility: the selected build must support the model architecture and the relevant acceleration backend. A GGUF filename is not a guarantee that an older installed binary can load a newly released model correctly.

Its eighth-place position reflects accessibility for a broad audience, not its technical importance. For an inference developer it may belong at the top. Start with a known supported model and a documented build, then change one setting at a time. Record context size, quantization, hardware, and runtime version alongside performance results. Without those details, a tokens-per-second figure is not a useful comparison. Readers who simply want private chat will usually benefit from a friendlier application first.

Reference: llama.cpp documentation and product details.

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9. GPT4All

Best for: Straightforward local document chat. GPT4All remains a focused desktop option for people who want local conversation and document access on their own computer. Its LocalDocs feature is central to that appeal: readers can ask questions about a collection of files without first designing a separate retrieval service. Nomic continues to provide desktop downloads for major operating systems, including a Windows ARM option on its current product page.

This is a practical starting point for an individual who wants to explore a personal document collection. The relevant evaluation is not only whether a model produces a convincing answer, but whether the application retrieves the right source material and makes that result useful. Test files representative of the actual collection, including ambiguous names, long documents, and information spread across more than one source.

We rank GPT4All ninth as a simpler specialist rather than the default foundation for every local AI application. Do not interpret claims about ordinary-computer support as evidence that a large model will respond comfortably on any laptop. Check the supported models, available acceleration, and document-processing behavior of the installed version. If the primary need is a shared workspace, more extensive model experimentation, or a configurable service backend, compare the other tools before building a workflow around it.

Reference: GPT4All documentation and product details.

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10. TextGen

Best for: Advanced desktop experimentation and optional training. TextGen, from the oobabooga project, suits users who want more control over local-model experimentation inside a desktop interface. The project offers chat, vision with supported models, tool calling, file attachments, and API access. It can be a useful middle ground between a tightly simplified chat application and assembling an inference environment entirely from command-line components.

The installation distinction matters. Portable builds are available for Windows, macOS, and Linux and support GGUF models through llama.cpp. The project’s fuller installation adds capabilities such as additional backends, LoRA training, image generation, and extensions. Those should not be presented as features automatically included in every lightweight download. Choose the installation path around the work you intend to do and the dependencies you are prepared to maintain.

TextGen ranks tenth as a specialist for experimentation, not because it lacks capability. A technical user testing models and generation settings may prefer it to several higher-ranked choices. Its additional controls are less helpful to someone who only wants a reliable document assistant with minimal setup. Keep model, backend, prompt-template, and sampling settings consistent when comparing outputs. Optional web search or extensions also change the behavior of an otherwise local workflow, so evaluate the configuration rather than relying on a blanket privacy label.

Reference: TextGen documentation and product details.

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How Much Memory Do You Need for a Local LLM?

Model weight size is only the starting point. As a rough arithmetic illustration, eight billion parameters stored at four bits each represent about 4 GB of raw weight data before format overhead. That does not mean an 8B model will run comfortably on a computer with only 4 GB of available memory. The runtime, operating system, context cache, and other applications also need space.

Use the actual model file and runtime requirements. Quantization reduces weight storage, with a quality tradeoff that depends on the method and task. Longer context and parallel requests can substantially increase memory requirements. A mixture-of-experts model’s small activated-parameter count is not the amount of memory needed to hold all its weights.

Distinguish system RAM from GPU memory. A model partly offloaded to a GPU may fit while running much more slowly than one held entirely in suitable accelerator memory. Unified-memory computers have a different allocation model, but the operating system still consumes part of that shared pool. Check the exact hardware and backend supported by the application rather than treating all machines with the same RAM total as equivalent.

Start with a modest context. Load a supported quantized model, run representative prompts, and inspect memory use before raising the context limit. Test both a fresh load and a longer conversation. Ollama’s memory and concurrency documentation explains why simultaneous requests and context settings affect the resources required. None of the rankings above implies a universal speed advantage.

How to Verify That Your Workflow Stays Local

Check the generation model, embedding model, document parser, reranker, speech service, and any tools separately. A local chat interface can still send a document to a remote embedding or parsing service. Web search and remote tools also require network activity even when the language model itself is running on the computer.

After downloading the necessary components, test the core workflow without an internet connection. This helps establish offline functionality, but it is not a complete network or telemetry audit. For Ollama, the documented OLLAMA_NO_CLOUD=1 setting disables cloud features after a restart. For LM Studio, its offline-operation guide identifies which core functions work without connectivity.

When serving colleagues, decide who can access the endpoint and who owns updates, backups, and stored documents. Keep a personal local server private unless shared access is intentional and configured. Review software terms separately from model licenses; downloading an open-weight model does not make every use unrestricted. Open WebUI’s current licensing page, for example, explains its branding conditions.

Frequently Asked Questions

Which tool is best for a beginner?

LM Studio is a strong starting point for visual model discovery and desktop chat. Jan is another focused desktop option. Choose AnythingLLM or GPT4All when working with documents is the main objective. Ollama becomes especially useful when several applications need the same local model service.

Do I need a dedicated GPU?

Not for every model or workflow. CPU inference is possible with supported runtimes, but response speed and useful model size vary considerably. A small model that responds comfortably is often a better starting point than a larger one that forces excessive memory offloading.

Are these tools themselves LLMs?

No. They are applications, interfaces, or runtimes that use models. Changing the interface does not automatically improve a model’s reasoning. Prompt formatting, context management, retrieval, and tools can affect the result, but the selected checkpoint and hardware remain fundamental.

Is an OpenAI-compatible endpoint a complete replacement for a hosted API?

It can simplify integration, but compatibility is specific to the endpoints and features implemented. Test streaming, structured outputs, tools, image handling, and failure behavior with the actual model. A shared API shape does not establish equal capability or reliability.

Which Local LLM Tool Should You Choose?

Start with Ollama when you want a reusable local service, or LM Studio when the desktop experience matters most. Open WebUI is a strong interface for shared deployments, while AnythingLLM centers the workflow on documents. Jan offers an open-source desktop alternative, and Msty Studio is useful for organizing knowledge and comparing several models.

Choose LocalAI for broader self-hosted inference APIs and llama.cpp for direct engine control. GPT4All remains a focused local document-chat option; TextGen suits deeper experimentation and optional training workflows. The right setup is the one that runs your chosen model reliably, keeps the intended data local, and requires a level of administration you can sustain.