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2025

DeepSeek R1 inference performance: MI300X vs. H200

DeepSeek-R1, with its innovative architecture combining Multi-head Latent Attention (MLA) and DeepSeekMoE, presents unique challenges for inference workloads. As a reasoning-focused model, it generates intermediate chain-of-thought outputs, placing significant demands on memory capacity and bandwidth.

In this benchmark, we evaluate the performance of three inference backends—SGLang, vLLM, and TensorRT-LLM—on two hardware configurations: 8x NVIDIA H200 and 8x AMD MI300X. Our goal is to compare throughput, latency, and overall efficiency to determine the optimal backend and hardware pairing for DeepSeek-R1's demanding requirements.

This benchmark was made possible through the generous support of our partners at Vultr and Lambda , who provided access to the necessary hardware.

  • in Fleets
  • 3 min read

Supporting Intel Gaudi AI accelerators

At dstack, our goal is to make AI container orchestration simpler and fully vendor-agnostic. That’s why we support not just leading cloud providers and on-prem environments but also a wide range of accelerators.

With our latest release, we’re adding support for Intel Gaudi AI Accelerator and launching a new partnership with Intel.

  • in Fleets
  • 3 min read

Efficient distributed training with AWS EFA

Amazon Elastic Fabric Adapter (EFA) is a high-performance network interface designed for AWS EC2 instances, enabling ultra-low latency and high-throughput communication between nodes. This makes it an ideal solution for scaling distributed training workloads across multiple GPUs and instances.

With the latest release of dstack, you can now leverage AWS EFA to supercharge your distributed training tasks.

Auto-shutdown for inactive dev environments—no idle GPUs

Whether you’re using cloud or on-prem compute, you may want to test your code before launching a training task or deploying a service. dstack’s dev environments make this easy by setting up a remote machine, cloning your repository, and configuring your IDE —all within a container that has GPU access.

One issue with dev environments is forgetting to stop them or closing your laptop, leaving the GPU idle and costly. With our latest update, dstack now detects inactive environments and automatically shuts them down, saving you money.

Orchestrating GPUs in data centers and private clouds

Recent breakthroughs in open-source AI have made AI infrastructure accessible beyond public clouds, driving demand for running AI workloads in on-premises data centers and private clouds. This shift offers organizations both high-performant clusters and flexibility and control.

However, Kubernetes, while a popular choice for traditional deployments, is often too complex and low-level to address the needs of AI teams.

Originally, dstack was focused on public clouds. With the new release, dstack extends support to data centers and private clouds, offering a simpler, AI-native solution that replaces Kubernetes and Slurm.

  • in NVIDIA, AMD
  • 2 min read

Supporting NVIDIA and AMD accelerators on Vultr

As demand for AI infrastructure grows, the need for efficient, vendor-neutral orchestration tools is becoming increasingly important. At dstack, we’re committed to redefining AI container orchestration by prioritizing an AI-native, open-source-first approach. Today, we’re excited to share a new integration and partnership with Vultr .

This new integration enables Vultr customers to train and deploy models on both AMD and NVIDIA GPUs with greater flexibility and efficiency–using dstack.