An open-source alternative to Kubernetes and Slurm for AI container orchestration, supporting NVIDIA, AMD, TPU, and Intel across cloud and on-prem.
Externer Link zu dstack
Munich, DE
dstack hat dies direkt geteilt
dstack 0.20.17 is out 🚀 This is a big release for SGLang inference on dstack: Prefill-Decode disaggregation is now much easier to deploy. More details plus NVIDIA and AMD benchmarks are coming soon. Plus, the Kubernetes backend now supports persistent volumes. https://lnkd.in/dc34Zf82
Teaming up with Crusoe to host an AI infra meetup in SF during @AMD Dev Day 🌁 For OSS AI researchers and ML engineers building on GPUs — lightning talks on training, inference, and open models. Join us Apr 30, 5pm 👉🏼 luma.com/ykt5b294
dstack 0.20.17 is out 🚀 This is a big release for SGLang inference on dstack: Prefill-Decode disaggregation is now much easier to deploy. More details plus NVIDIA and AMD benchmarks are coming soon. Plus, the Kubernetes backend now supports persistent volumes. https://lnkd.in/dc34Zf82
dstack hat dies direkt geteilt
Currently teaching agents to use dstack dev environments for automated tuning, and debugging No more manual tuning of inference parameters when deploying Qwen 🤯
dstack hat dies direkt geteilt
A lot of teams still do not know what to use for GPU orchestration. Kubernetes has become the default, but it usually means stitching together too much glue just to get development and inference working. Graphsignal uses dstack as a more lightweight, GPU-native orchestration layer for both development and inference.
A lot of teams still do not know what to use for GPU orchestration. Kubernetes has become the default, but it usually means stitching together too much glue just to get development and inference working. Graphsignal uses dstack as a more lightweight, GPU-native orchestration layer for both development and inference.
dstack hat dies direkt geteilt
New: a case study on how Graphsignal uses dstack for development and inference benchmarking. Graphsignal builds tooling to profile model inference, and uses dstack across a fleet of NVIDIA DGX Spark devices and Verda to keep the workflow consistent across on-prem and cloud: https://lnkd.in/dy8gaFKF
New: a case study on how Graphsignal uses dstack for development and inference benchmarking. Graphsignal builds tooling to profile model inference, and uses dstack across a fleet of NVIDIA DGX Spark devices and Verda to keep the workflow consistent across on-prem and cloud: https://lnkd.in/dy8gaFKF
See you today at Beyond Summit by TensorWave. Marc will be on the panel for "Building the Open Source Ecosystem" at 12:30pm. Talking about AMD, GPU infrastructure, orchestration, serving, and hardware enablement — and the open, interoperable stack that removes bottlenecks for AI deployment at scale.
