Joined August 2023
5 Photos and videos
Today, we demoed @PrismML's Bonsai 8B model in @AnythingLLM desktop to see if the 1-bit mode arch is the revolution local AI needs. Turns out, this is not an April fools joke. 1.1GB memory footprint for a whole 8B model with high accuracy
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The Bonsai models scale from mobile to Desktop (1.7B, 4B, and 8B) so you can get dense intelligence on any hardware. You can find the full collection (including MLX) here. huggingface.co/collections/pโ€ฆ
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AnythingLLM is an app layer for local models. We believe that a local model can feel like a cloud model with the right tools and guardrails. We believe we can bring 80% of your daily inference tasks to the computer you are already on. anythingllm.com
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Over the weeked we passed by 50,000 GitHub stars on AnythingLLM! We are so lucky to have so many people inspired, using, forking, and contributing to our vision of an LLM agnostic tool suite you can run locally or for your organization. โญ๏ธ github.com/Mintplex-Labs/anyโ€ฆ
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We are a lucky partner to bench the new @NVIDIA DGX Spark before it came out. We build AI tools literally every day, so we were eager to get our hands on this to kick the tires.
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128GB unified memory, 20-core ARM CPU, 4.1TB of storage, GB10 Blackwell superchip, and more. If you are interested in local AI inference in a single self-contained compute device, our overview video is worth checking out @NVIDIA_AI_PC ๐Ÿ‘‰ youtu.be/zs-J9sKxvoM
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If you have specific models, benchmarks, and other details you want us to try out, let me know, and we can publish those in a follow-up video. GA for these devices is published to be on Wednesday, Oct 15th, so you can let us try out your builds prior to get ideas.
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AnythingLLM retweeted
Turn notes/textbooks into an AI tutor with @AnythingLLM on RTX AI PCs. Flashcards, quizzes, solutionsโ€”fast, private, local. ๐Ÿ‘‰ nvda.ws/4gPk4B8
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AnythingLLM RTX = ๐Ÿ”ฅ Checkout the latest blog on NVIDIA NIM AnythingLLM Desktop
Accelerate local LLMs and advanced AI workflows in @AnythingLLM with NVIDIA RTX. ๐Ÿ–ฅ๏ธ๐Ÿ’ฌ โšก Fast inference w/ NVIDIA NIM Microservices ๐Ÿ“ Local privacy ๐Ÿ” Easy to use UI Learn more in the #RTXAIGarage blog ๐Ÿ‘‰ nvda.ws/4kA8J9r
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AnythingLLM retweeted
NVIDIA & Microsoft Advance Development on RTX AI PCs.๐Ÿค TensorRT has been reimagined for RTX. Available via Microsoftโ€™s new inference stack, Windows ML, they streamline development and unlock peak performance. Explore #MSBuild & #COMPUTEX2025 news ๐Ÿ‘‰ nvda.ws/4k2tBWr
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AnythingLLM retweeted
AI at your fingertips. โœจ NVIDIA NIM microservices arrive on RTX AI PCs & workstations making AI tool creation easier than ever. Plus, Project G-Assist System Assistant expands PC AI abilities with a custom plugin builder. #RTXAIGarage: nvda.ws/4iZLlRn
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We โค๏ธ LanceDB
5 Feb 2025
โš”๏ธ ๐— ๐—ฒ๐—ฒ๐˜ ๐˜๐—ต๐—ฒ ๐—ž๐—ป๐—ถ๐—ด๐—ต๐˜๐˜€ ๐—ฎ๐˜ ๐—Ÿ๐—ฎ๐—ป๐—ฐ๐—ฒ๐—น๐—ผ๐˜ ๐—ฅ๐—ผ๐˜‚๐—ป๐—ฑ ๐—ง๐—ฎ๐—ฏ๐—น๐—ฒ - ๐—ง๐—ถ๐—บ โš”๏ธ @tcarambat is a fellow @ycombinator alumni, and has never been shy about his enthusiasm for LanceDB. Being a loyal ambassador, his team ships @AnythingLLM with LanceDB as a default for a seamless end-user experience!
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AnythingLLM retweeted
5 Feb 2025
โš”๏ธ ๐— ๐—ฒ๐—ฒ๐˜ ๐˜๐—ต๐—ฒ ๐—ž๐—ป๐—ถ๐—ด๐—ต๐˜๐˜€ ๐—ฎ๐˜ ๐—Ÿ๐—ฎ๐—ป๐—ฐ๐—ฒ๐—น๐—ผ๐˜ ๐—ฅ๐—ผ๐˜‚๐—ป๐—ฑ ๐—ง๐—ฎ๐—ฏ๐—น๐—ฒ - ๐—ง๐—ถ๐—บ โš”๏ธ @tcarambat is a fellow @ycombinator alumni, and has never been shy about his enthusiasm for LanceDB. Being a loyal ambassador, his team ships @AnythingLLM with LanceDB as a default for a seamless end-user experience!
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AnythingLLM retweeted
Accelerate DeepSeek reasoning models with RTX AI PCs. Learn more here ๐Ÿ‘‰ nvda.ws/4aCXk4p
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AnythingLLM retweeted
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๐Ÿ›ก๏ธย Becoming a Legendary Lancelot ๐Ÿ›ก๏ธ Introducing the Lancelot Round Table - where #opensource heroes transform AI's future. With 2 million monthly downloads, we are seeking bold innovators to reshape how the world thinks about AI infrastructure. We're not just building a database, but a project that will change everything. blog.lancedb.com/the-lanceloโ€ฆ

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"Now, with generative AI and RTX AI PCs, anyone can be a developer. A new wave of low-code and no-code tools, such as AnythingLLM, [...], enable enthusiasts to use AI models in complex workflows via simple graphical user interfaces." New wave of on-device AI tools ๐ŸŒŠ
AI that's NIMble.๐Ÿ–ฅ๏ธ We're accelerating generative AI with NVIDIA NIM Microservices, AI Blueprints and NIM-ready RTX AI PCs to help developers and enthusiasts build AI agents, like Project R2X, and enhance creative workflows on PC. Learn more๐Ÿ‘‰ nvda.ws/4aefadJ
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AnythingLLM retweeted
"Now, with generative AI and RTX AI PCs, anyone can be a developer. A new wave of low-code and no-code tools, such as AnythingLLM, [...] enable enthusiasts to use AI models in complex workflows via simple graphical user interfaces." Crazy mention for @AnythingLLM in the CES 2025 press release ๐Ÿš€
AI that's NIMble.๐Ÿ–ฅ๏ธ We're accelerating generative AI with NVIDIA NIM Microservices, AI Blueprints and NIM-ready RTX AI PCs to help developers and enthusiasts build AI agents, like Project R2X, and enhance creative workflows on PC. Learn more๐Ÿ‘‰ nvda.ws/4aefadJ
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AnythingLLM retweeted
From Generative to Agentic AI, 2024 was a big year for AI advancements. ๐Ÿ™Œ๐Ÿค– Dive into how AI transformed creativity, gaming, productivity, and more this year in our final #AIDecoded blog of 2024 ๐Ÿ‘‰ nvda.ws/406zYAi
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AnythingLLM retweeted
Now you can build AI Agents offline, locally on your RTX PC All with the help of Anything LLM, here's how Anything LLM released a new update for their AI Agent Application that can run on your @nvidia RTX-enabled PCs. It is built on llama.cpp, which means it is fully optimized for use with GeForce RTX and NVIDIA RTX GPUs. (llama.cpp is an open-source C library that facilitates the efficient deployment and inference of LLMs) ๐Ÿ“Œ Here's more about the application: 1. Local Integration: Runs entirely on your machine, supporting file formats like PDFs, Word, and CSVs. 2. Workspaces: Organize projects or topics into separate workspaces for clarity. 3. AI Agents: Perform complex tasks with context-aware AI agents. 4. Customization: Supports commercial and open-source LLMs with flexible settings. 5. Performance: Optimized for NVIDIA RTX AI PCs for fast, efficient processing. 6. Multi-modal Support: It can handle text and images or audio, making it suitable for various applications. 7. Anything LLM Community Hub: Share prompts, commands, and skills with the AI community to improve your Agent responses. AnythingLLM also enables developers to build and customize agentic AI using models like Llama, Mistral, and tools like Ollama and LMStudio. Even using RTX-powered AI PCs with Tensor Cores, ensures top performance for running advanced AI models. ๐Ÿ“Œ Even now you can build different AI Agents like: 1. ArXiv Search Tool: Find research papers and articles on ArXiv automatically via LLM search. 2. E-Mail Draft: Generate and open draft emails for Gmail or Outlook using the application 3. AirTable integration: You can even read or write data to Airtable via API Currently, it supports good AI Agent use cases like Web Search and Scraping, Document Management and File Handling. ๐Ÿ”— Try it out yourself: blogs.nvidia.com/blog/ai-decโ€ฆ ๐Ÿ”— Check out the GitHub: github.com/Mintplex-Labs/anyโ€ฆ ๐Ÿ”— Check out @NVIDIAAIDev Blueprint: nvidia.com/en-us/ai-data-sciโ€ฆ Which AI Agen are you building with AnythingLLM? Let me know in the comments below ๐Ÿ‘‡ Please make sure to, โ™ป๏ธ Share ๐Ÿ‘ React ๐Ÿ’ญ Comment to help more people learn #AIAgents #NVIDIA #ai #Genai
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6/ @NVIDIA_AI_PC introduces Agentic AI: Your ultimate taskmaster. From managing schedules to automating workflows, Agentic AI is powered by RTX GPUs on local PCs with apps like @AnythingLLM for blazing speed and full privacy.โ€จ x.com/nvidia_ai_pc/status/18โ€ฆ

๐Ÿ“‹ Meet Agentic AI: Your ultimate taskmaster. From managing schedules to automating workflows, Agentic AI is powered by RTX GPUs on your local PC with apps like @AnythingLLM for blazing speed and full privacy. ๐Ÿ” Check out this week's #AIDecoded blog: nvda.ws/3OsMJi1
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