A quick translation and a payment-system design shouldn’t need the same model. GPUStack × Jev routes tasks across self-hosted Qwen and DeepSeek through one endpoint. Here’s the setup—and how we checked which model actually answered.

With the model running, let’s generate an image. Send your prompt and generation settings to /v1/images/generations. 👇The example request and output are below.Use your service URL, API key, and deployed model name.

One A800 80GB, from deployment to image generation and editing. 🍀Here’s our Qwen-Image-2.1 walkthrough with GPUStack and vLLM-Omni, starting with backend setup and model deployment. Preview: replacing coffee with books

Building a self-hosted AI inference stack? GPUStack unifies model deployment, GPU management, and API routing across engines like vLLM and SGLang—with monitoring and governance for day-to-day operations. Explore the architecture below. 👇

Seeing AI contribute new mathematics is pretty mind-blowing. A lot to take in 🥹🥹🥹

🧐Looking for a self-hosted AI inference platform? GPUStack brings model deployment and heterogeneous GPU management into one place, with pluggable inference engines including vLLM and SGLang. 👉Explore the project: github.com/gpustack/gpustack

🚀 GPUStack v2.3 preview is here! Try v2.3.0rc2: vGPU (K8s only), automatic PD disaggregation, cross-replica KV cache sharing & more. 😍What are you most excited to try? Share your feedback! ⚠️ Use a fresh environment. Upgrades aren't supported. #GPUStack #AI #OpenSource

Deploying DeepSeek-V4.1-Flash on Day 0 🚀 GPUStack × vLLM benchmarks across H20, H200, and H100—covering deployment setup and performance, all in one place. 🔗 Get started with GPUStack: github.com/gpustack/gpustack #GPUStack #DeepSeek #vLLM #LLMDeployment #AIInfrastructure

GPUStack Day 0 support for Kimi-K3 @Kimi_Moonshot 🚀 We benchmarked vLLM vs. SGLang on a single 8×NVIDIA B300 node: • 64K: vLLM was 1.50× faster • 200K: SGLang was 1.31× faster • The crossover came from Decode scalability and DCP—not Prefill #GPUStack #KimiK3 #vLLM #SGLang

Congrats on the release! @thinkymachines Exciting to see Inkling pushing open multimodal models forward. We just ran Inkling-BF16 on GPUStack with 8× H20-141G — great to see it working in a real-world deployment. #Inkling #GPUStack #AIInfra #OpenSourceAI #MultimodalAI #LLM