# Dynamo Dynamo is an open-source inference framework for serving generative AI. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## NVIDIA Vera Rubin NVL72 Delivers Leading Performance in MLPerf Inference v6.1 Debut DevFeed: [NVIDIA Vera Rubin NVL72 Delivers Leading Performance in MLPerf Inference v6.1 Debut]() Original publisher: [Read original article]() Author: Zhihan Jiang Published: 2026-09-16T15:00:48Z Content type: article Language: en Sources: [NVIDIA Blog]() Topics: [NVIDIA Vera Rubin](), [Vera Rubin NVL72](), [benchmarking](), [Inference Performance](), [Dynamo](), [vllm](), [Nvidia](), [TensorRT-LLM]() Tags: [ai-infrastructure](), [benchmarks](), [dynamo](), [efficiency](), [hardware](), [inference](), [mlperf](), [networking](), [nvidia](), [nvidia-vera-rubin](), [nvl72](), [optimization](), [performance](), [software](), [tensorrt](), [tensorrt-llm](), [vera-rubin-nvl72](), [vllm]() ### AI overview NVIDIA reports MLPerf Inference v6.1 preview results for Vera Rubin NVL72 and GB300 NVL72 systems. Vera Rubin NVL72 delivered up to 3.7x higher throughput than GB300 NVL72 on Qwen3-VL and up to 2.5x higher throughput on DeepSeek-R1, while a four-rack GB300 NVL72 submission achieved 99% scaling efficiency. The results used vLLM, NVIDIA Dynamo, and TensorRT-LLM. ### Source excerpt System performance, efficient infrastructure scaling and continuous software optimization are key levers that determine AI inference economics. Higher system performance means more tokens generated, resulting in higher revenue. Efficient scaling means throughput grows proportionally as hardware gets added, requiring fewer resources to serve users at scale. Continuous optimization means generating more value from infrastructure investments. [...] ## When to Use Encode-Prefill-Decode Disaggregation to Accelerate Multimodal Model Serving DevFeed: [When to Use Encode-Prefill-Decode Disaggregation to Accelerate Multimodal Model Serving]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-09-09T20:31:04Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Optimization](), [model-deployment](), [multimodal](), [Dynamo](), [Inference Performance](), [Embeddings](), [Mixture of Experts (MoE)](), [GPU](), [Open Source](), [Artificial Intelligence]() Tags: [agentic-ai-generative-ai](), [ai-agent](), [ai-inference](), [computer-vision-video-analytics](), [developer-tools-techniques](), [dynamo](), [embeddings](), [gpu](), [inference](), [low-latency-inference](), [mixture-of-experts-moe](), [model-serving](), [multimodal](), [nvfp4](), [open-source](), [optimization]() ### AI overview This article explains when encode-prefill-decode disaggregation improves multimodal model serving. Using NVIDIA Dynamo, it separates vision encoding from LLM prefill and decode so the stages can batch, schedule, and scale independently. The approach is most effective for image-heavy prompts, short-to-medium outputs, and quantized mixture-of-experts models, with reported gains of up to 5x in time to first token and 7x in end-to-end response time. ### Source excerpt Encode-prefill-decode (EPD) disaggregation is an inference optimization technique for multimodal models that separates the vision encoder stage from the prefill... ## The future of AI-native Is open source DevFeed: [The future of AI-native Is open source]() Original publisher: [Read original article]() Author: Sam Barlien Published: 2026-07-23T05:40:01Z Content type: opinion Language: en Sources: [Platform Engineering Blog]() Topics: [Open Source](), [AI Infrastructure](), [Platform Engineering](), [Nvidia](), [Dynamo](), [NeMo](), [rocm](), [Linux](), [OpenClaw]() Tags: [ai-infrastructure](), [linux](), [nemo](), [nvidia](), [open-source](), [openclaw](), [platform-engineering](), [rocm]() ### AI overview The article argues that AI infrastructure is undergoing a structural shift as NVIDIA and AMD open-source software for running and optimizing their hardware. It presents this as a platform engineering decision: ecosystem adoption and open software stacks may matter more than proprietary software control. ### Source excerpt Chip vendors like NVIDIA and AMD are open sourcing the software that runs their AI hardware. Here is why that shift is a platform engineering decision. ## 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]() Original publisher: [Read original article]() Author: Waverly Swinton Published: 2026-03-19T22:13:37Z Content type: release Language: en Sources: [DigitalOcean]() Topics: [Dynamo](), [Digital Ocean](), [Inference Performance](), [Artificial Intelligence](), [Generative AI](), [Inference](), [Cloud](), [GB200](), [sglang](), [TensorRT-LLM](), [vllm](), [Kubernetes]() Tags: [ai-ml](), [cloud](), [cost](), [deployment](), [digitalocean](), [dynamo](), [gb200](), [inference](), [inference-performance](), [kubernetes](), [latency](), [llm](), [load-balancing](), [nvidia](), [product-updates](), [routing](), [sglang](), [tensorrt-llm](), [vllm]() ### 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 ## Dynamo, DynamoDB, and Aurora DSQL DevFeed: [Dynamo, DynamoDB, and Aurora DSQL]() Original publisher: [Read original article]() Author: Marc Brooker Published: 2025-08-15T00:00:00Z Content type: article Language: en Sources: [Marc Brooker's Blog](), [Marc Brooker's Blog]() Topics: [Amazon DynamoDB](), [DSQL](), [Dynamo](), [NoSQL](), [Replication](), [Availability](), [Serverless](), [SQL](), [Amazon Web Services](), [Transactions]() Tags: [amazon-dynamodb](), [availability](), [aws](), [data](), [database](), [databases](), [dsql](), [dynamo](), [replication](), [serverless](), [sql]() ### AI overview This article compares the architectures of Amazon Dynamo, Amazon DynamoDB, and Aurora DSQL, beginning with how they provide durability and availability when a single host fails. It explains Dynamo's replication across successor nodes in a consistent-hashing ring and contrasts it with DynamoDB's replica groups, multi-AZ servers, Paxos replication, quorum acknowledgments, and scaling behavior. ### Source excerpt Dynamo, DynamoDB, and Aurora DSQL Names are hard, ok? People often ask me about the architectural relationship between Amazon Dynamo (as described in the classic 2007 SOSP paper), Amazon DynamoDB (the serverless distributed NoSQL database from AWS), and Aurora DSQL (the serverless distributed SQL database from AWS). There's a ton to say on the topic, but I'll start off on comparing how the systems achieve a few key properties. The key references for this post are: For Dynamo, Dynamo: Amazon's Highly Available Key-value Store from SOSP'07. For DynamoDB, Amazon DynamoDB: A Scalable, Predictably Performant, and Fully Managed NoSQL Database Service from ATC'22, Distributed Transactions at Scale in Amazon DynamoDB from ATC'23, and Lessons learned from 10 years of DynamoDB from the Amazon Science blog. For DSQL, my blog series on DSQL. Durability The databases we're looking at offer different levels of durability, but all three are designed not to lose data when a single host fails. Dynamo does this by taking advantage of its consistent hashing approach, replicating the data across multiple hosts in order in the hash ring: To achieve high availability and durability, Dynamo replicates its data on multiple hosts. Each data item is replicated at N hosts. ... Each key, k, is assigned to a coordinator node[]. The coordinator is in charge of the replication of the data items that fall within its range. In addition to locally storing each key within its range, the coordinator replicates these keys at the N-1 clockwise successor nodes in the ring. Like Dynamo, DynamoDB assigns a node in a hash ring to each individual item. But that's where the similarities stop. Instead of replicating across multiple nodes in the ring, in DynamoDB each node consists of a replica group with multiple servers in multiple AZs using Paxos to replicate the data. Instead of appearing the ring N times, each item appears once, and takes advantage of fault-tolerant nodes rather than spreading over multiple ## The Consistency Series DevFeed: [The Consistency Series]() Original publisher: [Read original article]() Author: Nicolas Liochon (noreply@blogger.com) Published: 2015-05-12T15:43:00Z Content type: article Language: en Sources: [Nicolas Liochon]() Topics: [consistency](), [Databases](), [acid](), [Dynamo](), [CAP theorem]() Tags: [acid](), [availability](), [cap-theorem](), [consistency](), [database](), [databases](), [distributed-system](), [durability](), [dynamo](), [latency](), [network](), [partition](), [series]() ### AI overview The article introduces a new series on consistency models, focusing on practical details and their implementations. It distinguishes the topic from a simple strong-versus-eventual consistency trade-off and previews posts about durability, ACID, and the Dynamo model. ### Source excerpt "Those are my consistency models, and if you don't like them... well, I have others." - Groucho Marx, quoted from memory The CAP theorem series is coming to an end. I have a few posts planned, but they will be mainly about wrapping things-up-- I hope these posts will be great, though. Anyway, here starts a new series: the consistency series. This series is about getting into some practical details about the consistency models and their implementations. You think that the trade-off is only or mainly on strong consistency vs. eventual consistency? That everything is said with "W+R" vs. "N"? Then read this series. Settling on the color may not be enough when choosing a car. Posts, in recommended reading order The posts published so far are: Eventual Consistency and Durability: shows the link between durability and consistency, and why traditional databases have the 'D' in ACID. Tells you what 'W=1' implies in the Dynamo model. ## Android Architecture: Introducing Dynamo DevFeed: [Android Architecture: Introducing Dynamo]() Original publisher: [Read original article]() Author: SystemDotRun Published: 2015-04-03T00:00:00Z Content type: article Language: en Sources: [SystemDotRun]() Topics: [Dynamo](), [Library](), [Development](), [Code]() Tags: [android](), [android-architecture](), [app-architecture](), [asynchronous](), [code](), [dynamo](), [libraries](), [lifecycle]() ### AI overview This introductory post presents Dynamo, a small library and wiki for Android application architecture. It focuses on managing application state and asynchronous code, including their interaction with the Android lifecycle, and aims to decouple UI behavior from core application logic. ### Source excerpt Dynamo has been superseded by Pilot Welcome, welcome. Come in, it looks mighty cold out there. The code is laying heavy across the hills, disorder rules the day and the nights are long. We yearn for a day where the suns shines forth once more. This is post #2 of my very short Android Architecture series (can two be a series?). Post #1 was a primer for this post. It contained A brief outline of the problems when defaulting to stock Android architecture. An overview of common general architectural concepts. This post is really a short intro to a small library + Wiki I have created. This library fills an architectural hole which I see in the Android dev world. There is no unified approach to generic Android app architecture and this creates a vacuum which sucks in well meaning devs and results is messy codebases strewn across the land. It is also my reponse to the many MVP blog posts out there, which have really helped me think about this issue but also I have my own take on. Plus, I feel sorry for new Android devs who are turning up later to the party and are overwhealmed by the Android ecosystem and need to get up to speed quickly on what these issues are before even getting to the point of thinking about potential solutions. Some of the issues addressed by this approach are solved by fantastic existing libraries, but again, I think for a large chunk of devs these can be difficult concepts to grasp and get running with. These are mentioned in the projects wiki for further reading. In my experience at least there are two core ideas around the center of most apps codebases. From the codebases I have seen & inherited both ideas seem to have too few brain cycles spent on them. 1. State Most apps and views are state-based however the spaghetti-level that captures the state logic is generally very high. 2. Asynchronous code Pretty much every app involves some asynchronous code. Pretty much every app allows this to touch the Android lifecycle. This always leads to sadness.