AnythingLLM. The name may sound still unfamiliar, but in the growing ecosystem of tools that seek to bring control of artificial intelligence to the user, it is one that aims to gain a lot of weight. At a time when most experiences with language models occur through cloud platforms, proposals emerge that propose an alternative: to execute LLMs locally, with privacy, speed and flexibility. And in that direction points to the first version of AnythingLLM, a desktop app that has just been launched with the direct backing of NVIDIA.
AnythingLLM is not a simple front-end for pre-trained models. It is a solution designed to integrate custom workflows with artificial intelligence, manage multiple data sources, execute RAG agents (recovery augmented generation) and maintain all this processing in the local user environment. It can function as a standalone application or integrate into the browser, and its open architecture allows you to combine locally hosted models with others accessible through the cloud (such as GPT-4 or Claude 3). All this, with an accessible interface and a powerful extension system.
The big leap, however, now comes with its native integration with the NVIDIA platform. On the one hand, the full support for RTX GPUs accelerates the execution of models such as Llama 3, DeepSeek or Mistral, with reduced response times and greater capacity to handle extensive contexts or complex tasks. On the other hand, the incorporation of NVIDIA’s NIM microservices opens the door to a simpler and more optimized execution: these are lightweight containers that encapsulate ready-to-use AI models, with immediate compatibility with tools such as AI Workbench and the possibility of integrating them into automated flows using Blueprints.
At the technical level, improvements are evident. According to data provided by NVIDIA, a configuration with an RTX 5090 GPU can run LLMs such as Call 3.1 8B with a speed up to 2.4 times higher than an Apple M3 Ultra. All this taking advantage of the fifth generation Tensor Cores and compatibility with frameworks such as GGUF, Ollama or Llama.cpp, widely used by the local AI community. In addition, thanks to the combined use of local memory and hardware acceleration, latency is significantly reduced, even in multitasking sessions with concurrent agents.
The experience doesn’t just improve in speed. With NIM, AnythingLLM also becomes a more accessible platform for developers and businesses. Models can be deployed as microservices with RESTful APIs, without the need for complex configurations, and the environment supports the extension through graphical tools such as AI Workbench or non-code-designed flows. This means that both a freelance developer and an IT team can design and deploy custom solutions on AnythingLLM without leaving their usual ecosystem.
In perspective, this movement consolidates AnythingLLM’s position as one of the most promising environments for those looking to work with generative AI outside the margins of the SaaS. Its focus on privacy, control and extensibility make it an ideal ally for sensitive uses or offline environments, while its new compatibility with NVIDIA technology ensures competitive performance at a professional level.
Beyond technical enthusiasm, the relevant thing is the change of mentality that proposals like this one underpins: AI does not have to live locked up on the servers of large platforms. You can, and perhaps should, move on your leap to the desk, to the user’s environment. Because with tools like AnythingLLM, the future of artificial intelligence can also be written on local.
