One post tagged with "GB200"

This blog post is co-authored with Rohan Varma, Saurabh Aggarwal, Anish Maddipoti, and Amr Elmeleegy from NVIDIA to showcase solutions that help customers run AI inference at scale using Azure Kubernetes Service (AKS) and NVIDIA’s advanced hardware and distributed inference frameworks.

Modern language models now routinely exceed the compute and memory capacity of a single GPU or even a whole node with multiple GPUs on Kubernetes. Consequently, inference at the scale of billions of model parameters demands multi-node, distributed deployment. Frameworks like the open-source NVIDIA Dynamo platform play a crucial role by coordinating execution across nodes, managing memory resources efficiently, and accelerating data transfers between GPUs to keep latency low.

However, software alone cannot solve these challenges. The underlying hardware must also support this level of scale and throughput. Rack-scale systems like Azure ND GB200-v6 VMs, accelerated by NVIDIA GB200 NVL72, meet this need by integrating 72 NVIDIA Blackwell GPUs in a distributed GPU setup connected via high-bandwidth, low-latency interconnect. This architecture uses the rack as a unified compute engine and enables fast, efficient communication and scaling that traditional multi-node setups struggle to achieve.

For some more demanding or unpredictable workloads, even combining advanced hardware and distributed inference frameworks is not sufficient on its own. Inference traffic spikes unpredictably. Fixed, static inference configurations and setups with predetermined resource allocation can lead to GPU underutilization or overprovisioning. Instead, inference infrastructure must dynamically adjust in real time, scaling resources up or down to align with current demand without wasting GPU capacity or risking performance degradation.

A holistic solution: ND GB200-v6 VMs and Dynamo on AKS​

To effectively address the variability in inference traffic in distributed deployments, our approach combines three key components: ND GB200-v6 VMs, the NVIDIA Dynamo inference framework, with an Azure Kubernetes Service (AKS) cluster. Together, these technologies provide the scale, flexibility, and responsiveness necessary to meet the demands of modern, large-scale inference workloads.

ND GB200-v6: Rack-Scale Accelerated Hardware​

At the core of Azure’s ND GB200-v6 VM series is the liquid-cooled NVIDIA GB200 NVL72 system, a rack-scale architecture that integrates 72 NVIDIA Blackwell GPUs and 36 NVIDIA Grace™ CPUs into a single, tightly coupled domain.

The rack-scale design of ND GB200-v6 unlocks model serving patterns that were previously infeasible due to interconnect and memory bandwidth constraints.

NVIDIA Dynamo: a distributed inference framework​

NVIDIA Dynamo is an open source distributed inference serving framework that supports multiple engine backends, including vLLM, TensorRT-LLM, and SGLang. It disaggregates the prefill (compute-bound) and decode (memory-bound) phases across separate GPUs, enabling independent scaling and phase-specific parallelism strategies. For example, the memory-bound decode phase can leverage wide expert parallelism (EP) without constraining the compute-heavy prefill phase, improving overall resource utilization and performance.

Dynamo includes an SLA-based Planner that proactively manages GPU scaling for prefill/decode (PD) disaggregated inference. Using pre-deployment profiling, it evaluates how model parallelism and batching affect performance, recommending configurations that meet latency targets like Time to First Token (TTFT) and Inter-Token Latency (ITL) within a given GPU budget. At runtime, the Planner forecasts traffic with time-series models, dynamically adjusting PD worker counts based on predicted demand and real-time metrics.

The Dynamo LLM-aware Router manages the key-value (KV) cache across large GPU clusters by hashing requests and tracking cache locations. It calculates overlap scores between incoming requests and cached KV blocks, routing requests to GPUs that maximize cache reuse while balancing workload. This cache-aware routing reduces costly KV recomputation and avoids bottlenecks, which in turn improves performance, especially for large models with long context windows.

To reduce GPU memory overhead, the Dynamo KV Block Manager offloads infrequently accessed KV blocks to CPU RAM, SSDs, or object storage. It supports hierarchical caching and intelligent eviction policies across nodes, scaling cache storage to petabyte levels while preserving reuse efficiency.

Dynamo’s disaggregated execution model is especially effective for large, dynamic inference workloads where compute and memory demands shift across phases. The Azure Research paper "Splitwise: Efficient generative LLM inference using phase splitting" demonstrated the benefits of separating the compute-intensive prefill and memory-bound decode phases of LLM inference onto different hardware. We will explore this disaggregated model in detail in an upcoming blog post.

How Dynamo can optimize AI product recommendations in e-commerce apps​

Let’s put Dynamo’s features in context by walking through a realistic app scenario and explore how its framework addresses common inference challenges on AKS.

Imagine you operate a large e-commerce platform (or provide infrastructure for one), where customers browse thousands of products in real time. The app runs on AKS and experiences traffic surges during sales, launches, and seasonal events. The app also leverages LLMs to generate natural language outputs, such as:

• Context-aware product recommendations
• Dynamic product descriptions
• AI-generated upsells based on behavior, reviews, or search queries

This architecture powers user experiences like: “Customers who viewed this camera also looked at these accessories, chosen for outdoor use and battery compatibility.” Personalized product copies are dynamically rewritten for different segments, such as “For photographers” vs. “For frequent travelers.”

Behind the scenes, it requires a multi-stage LLM pipeline: retrieving product/user context, running prompted inference, and generating natural language outputs per session.

Common pain points and how Dynamo tackles them​

1. Heavy Prefill + Lightweight Decode = GPU Waste

   Generating personalized recommendations requires a heavy prefill stage (processing more than 8,000 tokens of context) but results in short outputs (~50 tokens). Running both on a single GPU can be inefficient.

   Dynamo Solution: The pipeline is split into two distinct stages, each deployed on separate GPUs. This allows independent configuration of GPU count and model parallelism for each phase. It also enables the use of different GPU types—for example, GPUs with high compute capability but lower memory for the prefill stage, and GPUs with both high compute and large memory capacity for the decode stage.

   In our e-commerce example, when a user lands on a product page:

   • Prefill runs uninterrupted on dedicated GPUs using model parallelism degrees optimized for accelerating math-intensive attention GEMM operation. This enables fast processing of 8,000 tokens of user context and product metadata.

   • Decode runs on a GPU pool with different counts and parallelism degrees designed and tuned to maximize memory bandwidth and capacity for generating the short product blurb.

   Result: This approach maximizes GPU utilization and reduces per-request cost.

2. Meeting SLOs and handling traffic spikes without overprovisioning

   Your SLO might define time-to-first-token < 300ms and 99th percentile latency < 500ms, but maintaining this across dynamic workloads is tough. Static GPU allocation leads to bottlenecks during traffic spikes, causing either SLO violations or wasted capacity.

   Dynamo Solution: Continuously monitors metrics and auto-scales GPU replicas or reallocates GPUs between prefill and decode stages based on real-time traffic patterns, queue depth, and latency targets.

   In our e-commerce example:

   • During Black Friday, Dynamo observes latency climbing due to a surge in prefill demand. It responds by increasing prefill GPU replicas by 50%, shifting GPUs from decode or spinning up additional ones.
   • At night, when email generation jobs dominate, Dynamo reallocates GPUs back to decode to optimize throughput.
   • When load drops, resources scale back down.

   Result: SLOs are met consistently without over or under provisioning, controlling costs while maintaining performance.

3. Recomputing shared context is wasteful

   Many requests within the same session reuse the same product or user context but unnecessarily recompute the KV cache each time, wasting valuable GPU resources that could be spent serving other user requests.

   Dynamo Solution: LLM-aware routing maintains a map of KV cache across large GPU clusters and directs requests to the GPUs that already hold the relevant KV cache, avoiding redundant computation.

   In our e-commerce example:

   • A user browses five similar items in one session.
   • Dynamo routes all requests to the same GPU that already has the user’s or product’s context cached.

   Result: Faster response times, lower latency, reduced GPU usage.

4. KV cache growth blows past GPU memory

   With many concurrent sessions and large input sequence lengths, the KV cache (product data + user history) can exceed available GPU memory. This can trigger evictions, leading to costly re-computations or inference errors.

   Dynamo Solution: The KV Block Manager (KVBM) offloads cold/unused KV cache data to CPU RAM, NVMe, or networked storage freeing valuable GPU memory for active requests.

   In our e-commerce example:

   • Without cache offloading: increasing number of concurrent sessions per GPU increases latency due to KV cache evictions and recomputations
   • With Dynamo: GPUs can support higher concurrencies while maintaining low latency

   Result: Higher concurrency at lower cost, without degrading user experience.

Enterprise-scale inference experiments: Dynamo with GB200, running on AKS​

We set out to deploy the popular open-source GPT-OSS 120B reasoning model using Dynamo on AKS on GB200 NVL72, adapting the SemiAnalysis InferenceMAX recipe for a large scale, production-grade environment.

Our approach: leverage Dynamo as the inference server and swap GB200 NVL72 nodes in place of NVIDIA HGX™ B200, scaling the deployment across multiple nodes.

Our goal was to replicate the performance results reported by SemiAnalysis, but at a larger scale within an AKS environment, proving that enterprise-scale inference with cutting-edge hardware and open-source models is not only possible, but practical.

AKS Deployment Overview​

Ready to build the same setup? Our comprehensive guide walks you through each stage of the deployment:

1. Set up your foundation: Configure GPU node pools and prepare your inference set up with the prerequisites you will need.
2. Deploy Dynamo via Helm: Get the inference server running with the right configurations for GB200 NVL72.
3. Benchmark performance with your serving engine: Test and optimize latency/throughput under production conditions.

Find the complete recipe for GPT-OSS 120B at aka.ms/dynamo-recipe-gpt-oss-120b and get hands-on with the deployment guide at aka.ms/aks-dynamo.

The results​

By following this approach, we achieved 1.2 million tokens per second, meeting our goal of replicating SemiAnalysis InferenceMAX results at enterprise scale. This demonstrates that Dynamo on AKS running on ND GB200-v6 instances can deliver the performance needed for production inference workloads.

Looking ahead​

This work reflects a deep collaboration between Azure and NVIDIA to reimagine how large-scale inference is built and operated, from the hardware up through the software stack. By combining GB200 NVL72 nodes and the open-source Dynamo project on AKS, we’ve taken a step toward making distributed inference faster, more efficient, and more responsive to real-world demands.

This post focused on the foundational serving stack. In upcoming blogs, we will build on this foundation and explore more of Dynamo's advanced features, such as Disaggregated Serving and SLA-based Planner. We'll demonstrate how these features allow for even greater efficiency, moving from a static, holistic deployment to a flexible, phase-splitted architecture. Moving forward, we also plan to extend our testing to include larger mixture-of-experts (MoE) reasoning models such as DeepSeek R1. We encourage you to try out the Dynamo recipe in this blog on AKS and share your feedback!