Together GPU Clusters Gains Autoscaling & Self-Healing

🔑 Enhanced Key Takeaways

• •Together GPU Clusters support NVIDIA H100, H200, B200, and GB200 GPUs with non-blocking Quantum-2 InfiniBand and NVLink networking for ultra-low-latency AI workloads[1][2][3].
• •Clusters provision from 8 to over 4,000 GPUs, with flexible hourly on-demand or reserved pricing and minimum 3-day rentals starting at single 8-GPU nodes[2][3].
• •Preloaded with GPU Operator, NVIDIA Network Operator, InfiniBand, and acceptance testing including hardware checks and stress tests before availability[1][3].

🛠️ Technical Deep Dive

• •Optimized with NVIDIA Cloud Partner reference architecture featuring Blackwell and Hopper GPUs, supporting Kubernetes (Kubeadm-based with node autoscaling, managed Grafana observability, HA control plane) or Slurm on Kubernetes for orchestration[1][2][3].
• •Users select NVIDIA driver and CUDA versions, with adjustable storage from 1TB, and capabilities like cluster recreation with remounting original data for episodic training[1][2].
• •Together Kernel Collection and ThunderKittens enable 90% faster training on B200 vs H100, achieving 15,264 tokens/second/GPU for 70B Llama model using TorchTitan + TKC[3].

🔮 Future ImplicationsAI analysis grounded in cited sources

⏳ Timeline