🚀GPUStack supports all Qwen3 models — on day 0!
✅ Mac/Windows/Linux (Apple/NVIDIA/AMD GPU)
✅ Mixed clusters via llama-box (llama.cpp)
✅ Scalable Linux clusters via vLLM + Ray
Run Qwen3 anywhere — open-source & production-ready.
#Qwen3#GPUStack
🚀GPUStack supports all Qwen3 models — on day 0!
✅ Mac/Windows/Linux (Apple/NVIDIA/AMD GPU)
✅ Mixed clusters via llama-box (llama.cpp)
✅ Scalable Linux clusters via vLLM + Ray
Run Qwen3 anywhere — open-source & production-ready.
#Qwen3#GPUStack
Thanks for the great work. To use the update in GPUStack, just define your llama-box backend version to v0.0.112. GPUStack will automatically download and use the new version for you.
Thanks for the great work. To use the update in GPUStack, just define your llama-box backend version to v0.0.112. GPUStack will automatically download and use the new version for you. https://t.co/5eTBjx4sd8
🚀 Want to run DeepSeek-R1 across Mac, Windows, and Linux with Apple, AMD, and Nvidia GPUs? Try GPUStack v0.5! No blind forced distribution - we auto-calculate resource needs and pick the optimal deployment. Flexibility meets power! 💪
#DeepSeekR1#AMD#MacOS#GPUs
🚀 GPUStack 0.4.0 is here! Now with support for image generation & audio models, inference engine version management, offline support, and more. Ready to power your AI workflows like never before!
Learn more here 👇
gpustack.ai/gpustack-v0-4-…#AI#LLMs#flux1
Want to run GPUStack in Docker? 🚀 Learn how to set up NVIDIA Container Runtime and effortlessly deploy GPUStack with Docker in this tutorial👇
gpustack.ai/how-to-set-up-…#LLM#GPU#NVIDIA
Unlock the power of a private ChatGPT and knowledge base with @AnythingLLM + GPUStack! 🎉 Learn how to build your own AI assistant here: gpustack.ai/building-your-…
🚀 GPUStack 0.3.2 is out! Support for new reranker models: gte-multilingual-reranker-base and jina-reranker-v2-base-multilingual.
Learn more here👇
github.com/gpustack/gpust…
Want to build a RAG-Powered Chatbot with Chat, Embed, and Rerank endpoints entirely on your MacBook or anywhere? Just try github.com/gpustack/gpust… backed by llama.cpp. Thanks a lot to @ggerganov and the llama.cpp community for the great work.
Previously, RAG systems were the standard method for retrieving information from documents. However, if you are not repeatedly querying the same document, it may be more convenient and effective to just use long-context LLMs. For example, Llama 3.1 8B and Llama 3.2 1B/3B now…
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