dstack
Vendor-agnostic orchestration for training, inference and agentic workloads across NVIDIA, AMD, TPU, and Tenstorrent on clouds, Kubernetes, and bare metal.
- Distribute model training across on-prem GPU clusters and cloud instances
- Switch between cloud providers without changing training scripts
- Scale inference workloads across mixed AMD and NVIDIA hardware
Eliminates vendor lock-in for GPU/accelerator workloads by providing a unified API across NVIDIA, AMD, TPU, and Tenstorrent. Reduces infrastructure provisioning time from weeks to hours across clouds, Kubernetes, and on-premises hardware.
ML teams and infrastructure leads who need to train and deploy models on multiple hardware vendors or cloud providers without rewriting orchestration logic.
https://github.com/dstackai/dstack
By dstackai
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Trust Signals Auto-scanned
Community Pulse Active
Discussed on Hacker News, Reddit
- Show HN: dstack – an open-source tool to build data applications easily — Hacker News · 134 pts
- Show HN: Dstack – an open-source engine for running GPU workloads — Hacker News · 101 pts
- Publishing, tracking and sharing data visualizations — Reddit · 36 pts
20 mentions across 2 sources
Reviewer notes
Auto-scanned review. These are observations, not a security certification.
Scored from trust signals (evidence-eval-v1): 2,108 GitHub stars; 67 contributors; last commit 26d ago; license MPL-2.0.
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Evaluation
Scored from trust signals (evidence-eval-v1): 2,108 GitHub stars; 67 contributors; last commit 26d ago; license MPL-2.0.