Evidence note: This guide surveys open-source tools for running large language models on local hardware. Its descriptions of what each tool and model is, and what each claims to do, come from the projects’ own sources — their GitHub repositories, project websites, and encyclopedia entries — which are linked inline. Those self-descriptions and reference summaries have not been independently benchmarked or verified for this article; where a claim is a specific capability, number, or contested point, it is marked at that claim. This single note covers the general provenance so it is not repeated section by section.
Running LLMs Locally on Your Own Hardware: A Guide to the Open-Source Tools
Interest in running language models on personal machines — rather than calling a hosted API — has grown alongside the models themselves. The appeal is straightforward to state, even if each tool’s specifics vary: data stays on the device, there is no per-token bill, and the setup keeps working without a connection. The catch is that "local" spans a wide range, from a laptop CPU to a workstation with a discrete GPU or an NPU, and the tooling has fragmented to match. This guide walks the main open-source options and a few concrete uses, linking each project to its own source so the basis for every description is visible.
Why run large language models locally
Three motivations recur across these projects’ own framing: privacy, cost, and offline availability. Because inference happens on hardware the user controls, prompts and documents need not leave the machine — a property that matters for regulated data or simply for keeping personal notes personal. There is also no metered API cost once the model is downloaded, and, as several of the tools below advertise, an offline setup keeps functioning where connectivity is absent.
The trade-offs are just as real and should temper expectations: local models are bounded by local memory and compute, so the largest, strongest models are often impractical on consumer hardware, and quantized or smaller models trade some quality for fit. None of the tools below removes that ceiling; they mainly make it easier to get as close to it as a given machine allows.
Ollama: a platform for running large language models
Ollama’s Wikipedia entry describes it as a software platform for running large language models locally (described by the linked reference; not independently verified here) (Wikipedia). In practice it is one of the more common entry points for people new to local inference, packaging model download, storage, and a local serving endpoint behind a single command-line interface. Whether it is the right first stop depends on the hardware and the model, but its role as a general-purpose local runner is the reason it opens this list.
LocalAI: an open-source engine that runs models without a GPU
The LocalAI project describes itself as an open-source engine that can run models on a range of hardware, including without a dedicated GPU (LocalAI on GitHub). The "no GPU required" positioning is the load-bearing claim here: it is what distinguishes CPU-first tools from those that assume accelerator hardware. That claim is the project’s own; real-world throughput on CPU depends heavily on the model size and quantization (capability claim per the repository; performance not benchmarked for this article). LocalAI also positions itself as a drop-in local API, which is useful when the goal is to point existing software at a local backend rather than a remote one.
GPT4All: open-source local LLMs on any device, cleared for commercial use
GPT4All is presented by its repository as a way to run local LLMs across devices, as open source, and as available for commercial use (GPT4All on GitHub). The commercial-use point is worth singling out at the claim level, because licensing is where "open source" projects most often carry conditions: the permission stated here is the project’s own representation, and any specific deployment should still be checked against the licenses of both the tool and the individual model weights it loads (licensing claim per the repository; verify per-model terms independently). Its emphasis on running on ordinary devices places it near LocalAI in the CPU-friendly part of the spectrum.
torchchat: running PyTorch LLMs on servers, desktop, and mobile
torchchat, from the PyTorch project, is described as a way to run PyTorch LLMs locally across servers, desktop, and mobile (torchchat on GitHub). The span from server to phone is the notable part: mobile inference implies an on-device export path rather than a persistent server process. Readers interested in how on-device model execution works in practice may find our coverage of fast, on-device agentic AI running on ExecuTorch a useful companion, as it examines the mobile-export side of this same ecosystem.
intel/ipex-llm: accelerating inference and finetuning on Intel iGPU, NPU, and Arc/Flex/Max GPUs
Intel’s ipex-llm project describes itself as accelerating local LLM inference and finetuning on Intel XPU hardware — integrated GPUs and NPUs on local PCs, as well as discrete GPUs in the Arc, Flex, and Max lines (ipex-llm on GitHub). This is the Intel-specific counterpart to the more general runners above: its value is tied to having the relevant Intel hardware. The breadth of that hardware list is the project’s own claim, and actual acceleration will vary by chip, driver, and model (hardware-support claim per the repository; not measured here).
Lemonade by AMD: a fast local LLM server for GPU and NPU
Lemonade is presented on its own site as a fast, open-source local LLM server that uses both GPU and NPU (lemonade-server.ai). It sits alongside ipex-llm as the vendor-aligned option on the AMD side, targeting systems with AMD GPUs and NPUs. The "fast" descriptor is a vendor characterization rather than a figure that can be compared here; concrete speed depends on the specific accelerator and model (performance descriptor per the project site; no benchmark provided).
AnythingLLM: a local-first agent experience
AnythingLLM’s repository describes it as providing a local-first agent experience (AnythingLLM on GitHub). Where the tools above focus on serving a model, this one aims higher up the stack — chatting with documents and running agent-style workflows over a local model. Retrieval quality in that kind of document-chat setup rests heavily on the embedding step; readers building such a system may want our explainer on multi-vector, late-interaction embedding models, which covers a retrieval approach relevant to local document assistants.
The models you can run: Qwen, Gemma, Gemini, Claude, and DeepSeek
The tools are only half the picture; they load models. According to the linked reference entries, Qwen is described as a family of large language models developed by Alibaba (Wikipedia), Gemma as a family of large language models from Google (Wikipedia), Gemini as a large language model from Google (Wikipedia), Claude as a large language model and AI chatbot from Anthropic (Wikipedia), and DeepSeek as a Chinese artificial intelligence company (Wikipedia).
One practical distinction deserves emphasis, and it is a load-bearing one for this whole topic: running a model locally requires access to its weights under a suitable license. The sources here establish who makes each model, but they do not establish that every model named can be downloaded and run locally — some of these are typically offered as hosted services rather than distributed as local weights, and which is which is not settled by the references above (availability of local weights not corroborated here; check each model’s own release and license before assuming it runs locally). For hosted-side context on one of them, our look at Gemini Omni 1.1 Flash and its build controls covers the API side rather than the local one.
Practical uses: an offline talking LLM on a personal computer
Moving from tools to uses, one demonstrated pattern is a local "talking" LLM — a voice-in, voice-out loop running on a personal computer. The open-source local-talking-llm project is one such example (local-talking-llm on GitHub). The appealing property, that it can run without an internet connection, follows from the same local-first logic as the runners above, though the practical experience depends on the machine handling speech-to-text, the model, and text-to-speech together (offline operation is the project’s premise; latency and quality not measured here).
Labeling data with a local model, such as categorizing bank transactions with Llama2
Another concrete use is data labeling and analysis. The local-llms-analyse-finance project demonstrates using a local model — a Llama2 model in its example — to automatically categorize bank transaction data (project on GitHub). This is a natural fit for the privacy argument: financial records are exactly the kind of data one might prefer never to send to a hosted service. The example shows the pattern rather than guaranteeing accuracy on any given dataset, and categorization quality would need to be validated against the specific transactions involved (the worked example is the project’s; its accuracy is not independently assessed here).
Building a fully local LLM voice assistant to control a smart home
A more ambitious use combines the voice loop with home automation. One documented build describes a fully local LLM voice assistant wired to control a smart home (johnthenerd’s write-up). It is a useful reference precisely because it is a single practitioner’s account of an end-to-end system — the kind of detail that shows what integrating a local model with real devices actually involves. As a single author’s project, it documents one working configuration rather than a general recipe guaranteed to transfer to other setups.
Where a local file suite like Nextcloud fits alongside your models
Local models rarely live alone; they sit next to the files they work on. Nextcloud is described by its reference entry as a free and open-source file hosting software suite (Wikipedia). In a local-first setup it plays the complementary role to the tools above: a self-hosted place for the documents, notes, and media that a local assistant might read, keeping both the data and the model on infrastructure the user controls. Whether and how a given LLM tool integrates with it is a separate, project-specific question that the sources here do not settle.
The through-line across all of these is that "run it locally" has matured from a single hard path into a spread of options tuned to different hardware — CPU-only engines, Intel and AMD accelerator stacks, mobile export, and agent front-ends — paired with a growing set of openly available models. What the current sources establish is what each project claims to be and do; the practical questions that decide a real deployment — measured speed, model quality at a given size, and license fit for a given use — remain to be tested against the specific hardware and data in front of you.