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Scaling AI Inference Performance in the Cloud with Nebius

🧭 The Challenge: Rapidly Evolving AI Landscape

In the span of a few months the expectations for AI systems shift. I see this every day as I work to build infrastructure that must not only perform today but remain relevant tomorrow. When you build the AI infrastructure, the most important challenge you always face is that the industry is moving very fast. New model architectures, larger parameter counts, different serving patterns, and new hardware advances all conspire to make design decisions that felt right six months ago look obsolete today.

As the founder and chief technology officer of Nebius I have to treat the cloud not as a static product but as a living system. That means designing for change. It means accepting that inference is not a solved problem. Instead it is an evolving, dynamic area of the stack that requires both raw engineering and tight collaboration with hardware partners. It is not enough to provide compute. You must ensure that compute is organized, networked, and managed in ways that let models scale, perform, and deliver predictable business results.

That is why the problem statement I gave my engineering team from day one was simple and uncompromising. We will deliver a scalable, high-performance, high-reliability AI cloud that is purpose-built for training, inference, and data processing. We will make the platform efficient for the scenarios customers need. And we will do that while keeping the economics sensible for startups and enterprises alike.

🚀 Our Mission: High-performance, Scalable, Reliable AI Cloud

My mission at Nebius is to provide a platform that lets customers focus on their models and use cases while we worry about the infrastructure that runs those models. Our mission is to provide scalable, high-performance, high-reliable AI cloud. This is not marketing language. It is a design principle that informs every tradeoff we make.

Reliability in this context is about several things. First, it is the physical reliability of the underlying systems. Hardware fails. Disks fail. Networks partition. GPUs may have firmware issues. We design redundancy into every layer and bake failure scenarios into our testing and runbooks. Second, reliability means predictable performance. Customers need to know that a model served to users will meet latency and throughput targets under realistic load. Third, reliability is about operational tooling. Change management, observability, and automation reduce human error and keep the platform available.

Performance is about raw numbers and about the path to those numbers. We choose latest generation NVIDIA hardware where it makes sense because NVIDIA continuously moves the performance boundary. But performance is not only about GPU FLOPS. It is about memory capacity and bandwidth, about interconnect performance for multi-node models, and about the software stack that can extract that performance. When I say performance I mean the whole chain from disk to tensor to network to inference response.

Scalability means that as a customer's demand grows we can scale horizontally and vertically without forcing them to rewrite their code. We support multi-node training and inference so that models can use pooling of memory and compute across machines. We have to ensure that when models grow into tens or hundreds of billions of parameters, the platform supports that growth. That includes networking, orchestration, and pricing models that make this practical.

🛠️ Purpose-built Platform from the Ground Up

We took a deliberate approach. Instead of retrofitting a general cloud, we built the platform from the ground up with AI use cases in mind. Building from the ground up allowed us to identify the core AI scenarios we wanted to focus on and optimize the whole stack for them. The three scenarios at the center of our design are training, inference, and data processing.

Training needs are well understood. Customers demand high throughput for long-running jobs. They need job scheduling, checkpointing, and efficient multi-node communication. Data processing is the often-understated piece. Preparing massive datasets, feature transformation, and data augmentation are essential to modern model development. Ignoring that leads to bottlenecks that negate gains in model architecture.

Inference is the scenario where the platform must shine in production. For the majority of businesses real value accrues when a model is actually used. Serving that model is not just a performance challenge. It is an operational and economic challenge. The industry trend toward larger models that sometimes must be sharded across nodes means we needed to design a networking and orchestration fabric that supports multi-node inference with minimal latency and maximal efficiency.

That is why we did not merely assemble commodity components. We engineered the interactions between hardware, drivers, orchestration, and developer-facing APIs. We optimized scheduler decisions around co-locating model shards on the right machines, using topology-aware networking, and leveraging fast interconnects. We integrated observability deeply so that we can detect load imbalances early and adjust automatically.

🔬 Core AI Scenarios: Training, Inference, Data Processing

In designing Nebius I put training, inference, and data processing in the center. Each of those has distinct characteristics and constraints and requires different optimizations.

Training is often throughput oriented. You want to maximize GPU utilization and accelerate time to convergence. This drives a need for dense, high-bandwidth fabrics and fast storage for checkpoints.

Inference is latency sensitive and cost sensitive. Customers measure success in units of business. Low latency impacts user satisfaction directly. Throughput matters when you serve many concurrent users. But the cost per inference is also central. This is where optimizations such as batching, quantization, and model caching have real money value.

Data processing is the glue. If data ingestion and preprocessing are slow, neither training nor inference can deliver their potential. We built pipelines that can scale elastically with the workload and that are resilient to data skew and transient hotspots.

We treat these scenarios not as disjoint problems but as a continuum. For example when a training job finishes and a model moves to production, we want a frictionless path that migrates artifacts and configurations from training clusters to inference clusters. The data schema and preprocessing components should be reusable. This reduces time to production and avoids duplication of effort.

Shared platform features

Elastic compute pools that can be reallocated between training and inference demand.
Shared artifact storage and model repositories for reproducibility and governance.
Unified observability across training runs, batch pipelines, and serving stacks.
Policy-driven security controls to meet enterprise compliance needs.

💼 Why Inference Matters for Real Business Value

I often remind internal teams that nobody pays for a model that runs offline. The value is realized when the model is served to users or business processes. We love inference because behind the inference there is real business need of someone. Would it be a user, would it be business, the inference is where decisions are made and value is unlocked.

For many customers inference is the revenue stream. For a fintech company it might be real-time risk decisions. For an e-commerce platform it might be personalized recommendations. For an enterprise automation provider it could be document understanding that accelerates workflows. In each case the inference phase directly affects KPIs like conversion, time to task completion, or operational cost.

Because inference is tied to business, customers care deeply about latency, correctness, and cost. They demand predictable latency percentiles. They want to be able to run A B tests, shadow traffic, and handle sudden spikes in demand without compromising availability or blowing the budget. That leads me to two practical priorities when I design the inference platform: efficiency and economic pragmatism.

Efficiency is about extracting as much useful work from every GPU hour as possible. Economic pragmatism is about making sure that the cost per prediction aligns with the unit economics of the customer's business. If serving a model costs more than the incremental value it delivers then scale becomes a liability rather than an opportunity. We structure our pricing and our engineering choices to make inference economically feasible at scale.

⚙️ Hardware Choices: Memory, Multi-node, Networking

As models grow, memory becomes a gating factor. I cannot overstate this. Memory capacity and bandwidth are often the difference between a model that can run on a single node and one that must be sharded across many nodes. That in turn changes the performance requirements and the network design.

We chose to partner with NVIDIA because they continuously push the boundary on GPU compute and memory capabilities. Their hardware advances matter. For multi-node models you need more than raw FLOPS. You need low-latency, high-bandwidth interconnects so that gradients and activations can be exchanged without creating a bottleneck. You need features such as NVLink and high-performance networking fabrics like InfiniBand that can sustain the traffic patterns of parallel training and inference.

But hardware alone will not solve the problem. Software-level optimizations are essential. We rely on collective communication libraries such as NCCL for efficient multi-GPU and multi-node communication. We design our topologies so that shards that exchange the most data are placed close together in the network topology. This reduces cross-rack traffic and lowers tail latencies.

We also provide a range of instance types so that customers can choose the sweet spot between memory capacity, GPU compute, and price. For latency sensitive inference workloads we tend to place models on nodes with higher memory and lower consolidation so per-request latency is minimized. For high-throughput batch inference jobs we allow more consolidation and batching so that the cost per prediction is lower.

Multi-node inference considerations

Sharding strategies such as tensor, pipeline, and expert parallelism.
Topology-aware placement to minimize communication cost.
Fast interconnects to reduce synchronization overhead.
Memory pooling and model offloading to handle very large models.

One practical example is when we run a 100 billion parameter model that requires sharding across multiple GPUs. If the network fabric cannot deliver the required bandwidth and low latency, the synchronization cost will kill performance. Conversely if the fabric is fast enough and the placement is optimal, the model can scale nearly linearly across nodes for many workloads. That combination of hardware and software tuning is why we emphasize close cooperation with NVIDIA and continuous benchmarking.

🧩 Software Layer: Managed Kubernetes and Auto-scaling

At the orchestration layer we provide a managed Kubernetes platform that is optimized for AI workloads. Kubernetes gives us a declarative model for scheduling containers, but vanilla Kubernetes is not sufficient out of the box for the unique demands of GPU-based inference. We extended and tuned the platform to support device plugins, topology-aware scheduling, and GPU sharing where appropriate.

One of the most important features we built is auto-scaling. We have a managed Kubernetes that has the auto-scaling feature. This is critical for inference because demand is often bursty and unpredictable. Auto-scaling must be fast, safe, and cost-aware. We implemented multi-dimensional autoscalers that consider metrics such as GPU utilization, request latency percentiles, queue length, and business rules like minimum availability for critical services.

Auto-scaling also serves as a cost control mechanism. By scaling down during periods of low demand we avoid paying for idle resources. But scaling down too aggressively can cause cold start latency for models that need GPU warm-up. We strike a balance by using warm pools for frequently used models and fast cold-start mechanisms for less frequently used models.

We also support serverless-like patterns for small models. For models that are tiny enough to run efficiently on CPU inference or on a burstable GPU, we provide lightweight containers that can start within milliseconds. For larger models we provide explicit model servers with pinned GPU resources so that latency is predictable.

Kubernetes tuning and extensions

Device plugins for GPU scheduling and topology awareness.
Custom autoscalers that use request-level metrics and business rules.
Model lifecycle controllers that manage deployment, rollback, and canary releases.
Integrated observability agents for tracing, metrics, and distributed logs.

I often say that Kubernetes gives you primitives, not solutions. It is the way you assemble and extend those primitives that determines success. We invested in custom controllers and admission logic to ensure that model deployments are co-located correctly and that resource requests reflect operational needs. This reduces noisy neighbor effects, ensures predictable tail latencies, and makes capacity planning far more accurate.

💲 Economics and Unit Economics: Making Inference Practical

Unit economics drives adoption. Customers measure the cost per inference and weigh that against the value the inference creates. We made a conscious decision to design the platform so that the cost of running inference at scale is a first order concern. Efficiency and cost optimization are engineering targets at Nebius.

There are multiple levers we use to control costs:

Instance selection. Choosing the right GPU and memory configuration for the model and workload.
Model optimization. Techniques such as quantization, pruning, distillation, and compilation can reduce runtime resource needs by orders of magnitude.
Batching strategies. Dynamic batching allows us to combine requests to improve throughput while keeping latency within acceptable bounds.
Auto-scaling and warm pools. Reducing idle resources without compromising latency for popular models.
Preemptible or spot capacity for non-critical workloads. Using lower-cost instances when appropriate for background inference jobs.

When customers realize that they can serve the same model at a tenth of the cost without compromising user experience they adopt larger scale production deployments. That is where I see the real business impact. Saving 50 percent on inference cost for a production workload can dramatically change profitability for many SaaS and consumer services.

Ultimately the business model that underpins our approach is straightforward. If we can lower the cost per inference and improve reliability, customers will increase their usage which in turn leads to predictable revenue growth for Nebius. The alignment of incentives is clear. If our customers grow their business because our infrastructure gave them the performance and predictability they needed, we grow with them.

🤝 Collaboration with NVIDIA and Dynamo

Working with NVIDIA is central to our strategy. NVIDIA continues to innovate across hardware and software. Their ecosystem of drivers, libraries, and inference runtimes is critical to delivering high-performing AI services. We partner closely with NVIDIA to validate hardware, tune drivers, and adopt new architectural features quickly.

Dynamo is another important part of the story. Nebius, as an ecosystem partner for Dynamo, benefits from integration points that simplify deploying frontier models. We're building our platform to be compatible with Dynamo workflows so that customers have a path to scale inference from prototype to production without changing ecosystems. The combination of NVIDIA infrastructure and Dynamo-friendly tooling makes it easier for customers to adopt large models with multi-node serving capabilities.

From my perspective the key benefits of these collaborations include:

Early access to hardware and software optimizations so we can validate performance in real workloads.
Co-engineering opportunities that reduce time to production for customers adopting new NVIDIA features.
Shared benchmarking and reference architectures that provide customers with predictable performance expectations.

One specific area where collaboration shines is in optimizing network topologies for multi-node inference. NVIDIA's guidance on NVLink and interconnect topologies combined with our scheduler enhancements produces measurable reductions in synchronization overhead. That is not a theoretical gain. It translates directly into lower latency and higher throughput for customers running large transformer models.

🌍 Real-world Use Cases and Customer Impact

We built Nebius around real business needs. That means I spend a lot of time talking to customers across verticals to understand how they measure success. The diversity of use cases makes the point: inference matters differently for different businesses, but in every case it is the point where value is realized.

Here are a few anonymized examples of how enterprises and startups use our platform:

Consumer app company. They use real-time personalization models to serve recommendations to millions of users. Low tail latency matters. We built a mixed-instance approach for them that keeps hot models in dedicated GPU pools and uses auto-scaling to handle spikes during peak hours. The result was a measurable uplift in engagement and a 30 percent reduction in inference cost after model optimization and batching improvements.
Fintech firm. They run latency-sensitive risk assessments for transactions. Predictability is vital because the models are in the critical path of financial decisions. We configured topology-aware placement and strict resource reservations to ensure that tail latency percentiles met strict SLAs. This allowed the customer to expand use of AI in their day-to-day operations.
Enterprise document automation vendor. They run large language models for document understanding and question answering. Many of their models require multi-node inference. We used high-bandwidth interconnects and sharding strategies so that models could be served affordably. The result was higher throughput for batch tasks and acceptable latency for on-demand queries.

These stories share common threads. Customers who invest time in model optimization and who align their deployment patterns with the strengths of the underlying infrastructure get the best outcomes. Where Nebius adds value is by removing the heavy lifting associated with achieving that alignment so customers can focus on model quality and product features.

🧰 Implementation Details and Best Practices

To make our platform useful in production I codified a set of best practices that guide both our engineering team and our customers. These are practical steps that reduce risk and improve performance when deploying inference at scale.

Model lifecycle management

Version models with immutable artifacts so rollbacks are straightforward.
Use canary and staged rollouts for new models to reduce blast radius.
Store evaluation data and metrics to compare models across deployments.

Optimizing for inference

Quantize where possible to reduce memory and compute cost while monitoring accuracy impact.
Use distillation to create smaller, faster models that approximate larger models.
Explore operator fusion and optimized runtimes to reduce runtime overhead.

Operational patterns

Adopt latency SLOs and instrument to observe percentiles not just averages.
Use topology-aware scheduling to place communicating shards close together.
Maintain warm pools for latency sensitive models and use fast cold starts for others.

Data and governance

Ensure data preprocessing pipelines are reproducible and versioned.
Enforce fine-grained access controls to model artifacts and datasets.
Audit model behavior and provide explainability metrics where required by regulation.

These practices are not optional. They are the difference between a platform that delivers business value and one that creates expensive surprises. I encourage teams to take an incremental approach. Start with a strong baseline for observability and governance. Then iterate on optimizations that provide measurable returns. The most successful customers I work with take a data-driven approach to these tradeoffs.

📊 Measuring Success: Performance, Reliability, Cost Metrics

To manage a production AI cloud you must measure the right things. I consider three high-level metric categories essential for running inference at scale: performance, reliability, and cost.

Performance metrics

Latency percentiles such as p50, p95, and p99. Tail metrics are especially important for user-facing services.
Throughput measured in predictions per second.
GPU and memory utilization to understand resource efficiency.

Reliability metrics

Availability SLAs and SLO compliance percentages.
Mean time to recover for service disruptions.
Error rates and types to identify systemic issues quickly.

Cost metrics

Cost per prediction and cost per active user.
Utilization-adjusted cost to understand idle resource waste.
Cost variance against forecast to identify runaway expenses.

We instrument our platform to provide real-time dashboards and alerts for these metrics. More importantly, we correlate metrics across layers. For example, a sudden increase in p99 latency might be correlated with a network saturation event or a background job that consumed GPU memory. Correlating across the stack is what lets us diagnose root causes quickly and reduce mean time to recover.

When we present performance data to customers we highlight not only the absolute numbers but the business impact. For instance reducing p99 latency by 200 milliseconds might increase conversion rate for a checkout flow and thus have a measurable revenue impact. Framing operational improvements in business terms is essential to drive organizational support for further investment.

🔮 Future-proofing: Preparing for Next-generation Models

AI models will continue to grow in size and complexity. The trajectory is clear. To be future-proof we need to design a platform that is adaptable and extensible. Future-proofing is not about predicting the exact hardware or model architecture that will dominate. It is about building an architecture that can absorb innovation without expensive rewrites.

Key strategies I use to future-proof the platform include:

Modular architecture so we can swap components as hardware and software evolve.
Abstracted runtimes that let us integrate new inference engines and compilers with minimal disruption.
Pluggable networking topologies that can leverage new interconnects as they become available.
Continuous benchmarking to validate the impact of each new hardware generation on real workloads.

One of the most important bets we made is to stay close to hardware partners like NVIDIA. Their innovations often create new operational best practices and new performance envelopes. By being an early integrator we can validate these advances and provide customers with the ability to adopt them without long procurement cycles.

Another bet is on software ecosystems such as Dynamo. Compatibility with open ecosystems and standards reduces lock-in risk and gives customers the flexibility to move or architect hybrid solutions as their needs change. That flexibility is crucial for enterprises that must manage regulatory and budget constraints.

📣 Conclusion and Call to Action

Building a high-performance, scalable, and reliable AI cloud is hard work. The industry moves quickly and the bar for what counts as production-ready rises each quarter. I built Nebius to address that reality. We focused on the real problems customers face and invested in hardware partnerships, orchestration, and operational tooling to solve them.

We love inference because it is the point where AI delivers measurable business value. That is why our platform optimizes for low-latency, high-throughput, and economic efficiency. We pair purpose-built hardware choices with managed Kubernetes capabilities such as auto-scaling and topology-aware scheduling to give customers a platform that is ready for modern multi-node models.

If you are grappling with how to serve larger models, achieve predictable tail latencies, or reduce your cost per prediction, there are practical steps you can take today:

Audit your model footprints and identify candidates for quantization and distillation.
Map your topology and ensure that heavy-communication model shards are placed close together.
Instrument p95 and p99 latencies and correlate them with infrastructure metrics.
Adopt a lifecycle practice that supports canary rollouts and rapid rollback.
Choose infrastructure partners that can supply the hardware and the software integration you need to scale.

We are committed to making AI inference super efficient and super economically pragmatic for customers because when inference becomes affordable and reliable, businesses scale and realize value. We grow with our customers. If you want to learn more about how to scale inference for your applications I encourage you to reach out, benchmark your workloads on modern hardware, and adopt an iterative approach to optimization. The opportunity is enormous and the right infrastructure makes all the difference.

Quote: "We all understand that the power of AI is in serving the real cases of the customers, and that's inference. We want to make it super efficient, super economically pragmatic for our customers because then they got the real business, the real unit economics, and they grow, and we grow with them."