# GB200

GB200 NVL72 is a rack-scale system for scalable LLM inference and advanced AI data center needs.

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## Accelerating Dropless MoE Training in JAX with NVIDIA Transformer Engine

DevFeed: [Accelerating Dropless MoE Training in JAX with NVIDIA Transformer Engine](<https://devfeed.tech/articles/accelerating-dropless-moe-training-in-jax-with-nvidia-transformer-engine-21079.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/accelerating-dropless-moe-training-in-jax-with-nvidia-transformer-engine/>)

Author: Tanya Lenz

Published: 2026-09-14T16:39:15Z

Content type: article

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [Mixture of Experts (MoE)](<https://devfeed.tech/topics/mixture-of-experts-moe.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>), [Routing (disambiguation)](<https://devfeed.tech/topics/routing.md>), [GB200](<https://devfeed.tech/topics/gb200.md>), [Network](<https://devfeed.tech/topics/network.md>), [Python](<https://devfeed.tech/topics/python.md>), [deepseek](<https://devfeed.tech/topics/deepseek.md>), [mixtral](<https://devfeed.tech/topics/mixtral.md>), [qwen](<https://devfeed.tech/topics/qwen.md>)

Tags: [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [ai](<https://devfeed.tech/tags/ai.md>), [communication](<https://devfeed.tech/tags/communication.md>), [deepseek](<https://devfeed.tech/tags/deepseek.md>), [developer-tools-techniques](<https://devfeed.tech/tags/developer-tools-techniques.md>), [gb200](<https://devfeed.tech/tags/gb200.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [mixtral](<https://devfeed.tech/tags/mixtral.md>), [mixture-of-experts-moe](<https://devfeed.tech/tags/mixture-of-experts-moe.md>), [mlops](<https://devfeed.tech/tags/mlops.md>), [moe](<https://devfeed.tech/tags/moe.md>), [networks](<https://devfeed.tech/tags/networks.md>), [nvfp4](<https://devfeed.tech/tags/nvfp4.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [performance](<https://devfeed.tech/tags/performance.md>), [python](<https://devfeed.tech/tags/python.md>), [qwen](<https://devfeed.tech/tags/qwen.md>), [router](<https://devfeed.tech/tags/router.md>), [routing](<https://devfeed.tech/tags/routing.md>), [tensors](<https://devfeed.tech/tags/tensors.md>), [token](<https://devfeed.tech/tags/token.md>), [tokens](<https://devfeed.tech/tags/tokens.md>), [training-ai-models](<https://devfeed.tech/tags/training-ai-models.md>)

### AI overview

This article explains how NVIDIA Transformer Engine and JAX kernel optimizations accelerate dropless Mixture of Experts (MoE) training. It describes bottlenecks from token routing, expert dispatch and gathering, all-to-all communication, and ragged expert matrix operations. In DeepSeek-V3 training on NVIDIA GB200, the optimized approach increased performance from 103 to 1,068 TFLOPS per GPU, a 10.4x improvement.

### Source excerpt

Mixture of experts (MoE) has become one of the defining architectural trends in large-scale AI model training. DeepSeek, Qwen, and Mixtral are examples of MoE...

## Micron 6600 ION 245TB: Swap the Hard Drives, Power an NVL72 for Free

DevFeed: [Micron 6600 ION 245TB: Swap the Hard Drives, Power an NVL72 for Free](<https://devfeed.tech/articles/micron-6600-ion-245tb-swap-the-hard-drives-power-an-nvl72-for-free-12385.md>)

Original publisher: [Read original article](<https://www.storagereview.com/review/micron-6600-ion-245tb-swap-the-hard-drives-power-an-nvl72-for-free>)

Author: Brian Beeler

Published: 2026-09-03T17:00:39Z

Content type: article

Language: en

Sources: [StorageReview.com](<https://devfeed.tech/sources/storagereview-com.md>)

Topics: [Benchmark](<https://devfeed.tech/topics/benchmark.md>), [benchmarking](<https://devfeed.tech/topics/benchmarking.md>), [datacenter](<https://devfeed.tech/topics/datacenter.md>), [GB200](<https://devfeed.tech/topics/gb200.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [Server](<https://devfeed.tech/topics/server.md>), [dell](<https://devfeed.tech/topics/dell.md>)

Tags: [benchmark](<https://devfeed.tech/tags/benchmark.md>), [compute](<https://devfeed.tech/tags/compute.md>), [data-center](<https://devfeed.tech/tags/data-center.md>), [dell](<https://devfeed.tech/tags/dell.md>), [efficiency](<https://devfeed.tech/tags/efficiency.md>), [enterprise](<https://devfeed.tech/tags/enterprise.md>), [gb200](<https://devfeed.tech/tags/gb200.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [hdd](<https://devfeed.tech/tags/hdd.md>), [measurements](<https://devfeed.tech/tags/measurements.md>), [nvl72](<https://devfeed.tech/tags/nvl72.md>), [performance](<https://devfeed.tech/tags/performance.md>), [servers](<https://devfeed.tech/tags/servers.md>), [ssd](<https://devfeed.tech/tags/ssd.md>), [storage](<https://devfeed.tech/tags/storage.md>)

### AI overview

This article evaluates replacing eight 30TB nearline HDDs with one 245TB Micron 6600 ION SSD. Its measurements indicate lower power consumption, substantially higher read efficiency, and a reduction from 22 racks of HDD storage to six racks of flash at exabyte scale, potentially freeing enough power for a GB200 NVL72.

### Source excerpt

For two decades, the SSD-versus-HDD conversation ended the same way: flash wins on performance, disk wins on price per terabyte, and the size of that price gap settled the argument in favor of bulk storage. As storage technology has matured and AI has taken over, that framing is clearly out of date. The largest data The post Micron 6600 ION 245TB: Swap the Hard Drives, Power an NVL72 for Free appeared first on StorageReview.com.

## NVIDIA Dynamo 1.0 Is Available to DigitalOcean Customers for Inference Performance and Cost Efficiency

DevFeed: [NVIDIA Dynamo 1.0 Is Available to DigitalOcean Customers for Inference Performance and Cost Efficiency](<https://devfeed.tech/articles/meet-the-new-standard-for-high-performance-low-cost-inference-nvidia-dynamo-1-0-is-now-available-to-digitalocean-customers-19923.md>)

Original publisher: [Read original article](<https://www.digitalocean.com/blog/nvidia-dynamo-1-now-available>)

Author: Waverly Swinton

Published: 2026-03-19T22:13:37Z

Content type: release

Language: en

Sources: [DigitalOcean](<https://devfeed.tech/sources/digitalocean.md>)

Topics: [Dynamo](<https://devfeed.tech/topics/dynamo.md>), [Digital Ocean](<https://devfeed.tech/topics/digital-ocean.md>), [Inference Performance](<https://devfeed.tech/topics/inference-performance.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Generative AI](<https://devfeed.tech/topics/generative-ai.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [Cloud](<https://devfeed.tech/topics/cloud.md>), [GB200](<https://devfeed.tech/topics/gb200.md>), [sglang](<https://devfeed.tech/topics/sglang.md>), [TensorRT-LLM](<https://devfeed.tech/topics/tensorrt-llm.md>), [vllm](<https://devfeed.tech/topics/vllm.md>), [Kubernetes](<https://devfeed.tech/topics/kubernetes.md>)

Tags: [ai-ml](<https://devfeed.tech/tags/ai-ml.md>), [cloud](<https://devfeed.tech/tags/cloud.md>), [cost](<https://devfeed.tech/tags/cost.md>), [deployment](<https://devfeed.tech/tags/deployment.md>), [digitalocean](<https://devfeed.tech/tags/digitalocean.md>), [dynamo](<https://devfeed.tech/tags/dynamo.md>), [gb200](<https://devfeed.tech/tags/gb200.md>), [inference](<https://devfeed.tech/tags/inference.md>), [inference-performance](<https://devfeed.tech/tags/inference-performance.md>), [kubernetes](<https://devfeed.tech/tags/kubernetes.md>), [latency](<https://devfeed.tech/tags/latency.md>), [llm](<https://devfeed.tech/tags/llm.md>), [load-balancing](<https://devfeed.tech/tags/load-balancing.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [product-updates](<https://devfeed.tech/tags/product-updates.md>), [routing](<https://devfeed.tech/tags/routing.md>), [sglang](<https://devfeed.tech/tags/sglang.md>), [tensorrt-llm](<https://devfeed.tech/tags/tensorrt-llm.md>), [vllm](<https://devfeed.tech/tags/vllm.md>)

### AI overview

NVIDIA Dynamo 1.0 is now available to DigitalOcean customers as an inference service framework for large-scale generative AI workloads. The article describes claimed performance improvements on NVIDIA GB200 NVL systems, cost-efficiency benefits, deployment options, and features including GPU-aware routing and disaggregated serving.

### Source excerpt

NVIDIA Dynamo 1.0, which was released on Monday at NVIDIA GTC, is now available to DigitalOcean customers to help drive performance enhancements and cost efficiency. NVIDIA Dynamo 1.0 offers a 7x inference performance increase on NVIDIA GB200 NVL systems, and by pairing it with DigitalOcean's Agentic Inference Cloud, customers can achieve higher performance at lower costs while benefiting from seamless deployment. Working together, DigitalOcean's optimizations with NVIDIA have already achieved a 67% cost savings for customers like Workato, and this new generation of Dynamo can unlock even greater gains for businesses who run production-grade agentic workflows. DigitalOcean customers can get access to NVIDIA Dynamo 1.0 as a container image that can be run on a Droplet or can deploy directly on DigitalOcean Kubernetes with an inference runtime (vLLM, SGlang, TensorRT). What is NVIDIA Dynamo 1.0? NVIDIA Dynamo is a cutting-edge, high-performance inference service framework specifically designed to accelerate and optimize large-scale generative AI and inference models. Dynamo is an orchestration layer that sits above engines like vLLM, SGLang, and NVIDIA TensorRT-LLM. Think of it as the distributed traffic controller for your GPU fleet, seamlessly orchestrating GPU and memory resources across a cluster and reducing bottleneck by intelligently routing requests Key technical breakthroughs offered by Dynamo 1.0 include: 7x Performance Boost: When paired with NVIDIA Blackwell Ultra GPUs, Dynamo can increase inference performance by up to 7x, significantly lowering your cost per token. KV-Aware Routing: Instead of simple round-robin load balancing, Dynamo routes requests to the specific GPUs that already have the relevant "memory" from previous turns of a conversation. Disaggregated Serving: Dynamo splits the "prefill" (reading the prompt) and "decode" (generating the answer) phases across different GPUs to maximize utilization and reduce latency. Memory Offloading: The KV B