Practical Guide to Optimizing LLM Inference on Nutanix

Introduction

Intelligent caching and scaling—not just faster GPUs—will define next-generation inference performance.

In the rapidly evolving landscape of AI, large language models (LLMs) have become the backbone of countless enterprise applications—from conversational assistants to intelligent document processing. Yet deploying these models in production presents new challenges in speed, scalability, and cost-efficiency.

To deploy LLMs effectively in production, infrastructure teams responsible for AI workloads must overcome three core challenges:

1. Scaling dynamically without overprovisioning GPUs resources

2. Maximizing inference throughput on existing infrastructure investments

3. Expanding serving capacity without proportionally increasing infrastructure costs.

The Complexity of Scaling GPUs for Variable LLM Demand

Managing volatile and unpredictable traffic patterns remains one of the toughest challenges in building a responsive and efficient LLM serving infrastructure.

Customer-facing AI applications frequently encounter sudden surges in demand during peak hours, followed by long idle stretches.

Unlike conventional application workloads, scaling LLM inference isn’t instantaneous. Each instance of the LLM inference endpoint must individually load the model weights and initialize its runtime dependencies, making startup computationally heavy and time-consuming due to several factors:

• Scarce GPU supply, particularly for high-demand accelerators essential for running larger LLMs such as NVIDIA H200, RTX PRO 6000 Blackwell Server Edition and B200

• Rigid GPU node configurations often imposed by GPU hardware and infrastructure providers, often requiring a minimum of 8 GPUs per node

• Prolonged cold-start times, driven by massive model weights and container images that must load before requests can be served.

Because GPU capacity can’t scale instantly, organizations are often stuck choosing between two less-than-ideal provisioning strategies:

1. Provisioning for average utilization: Keeps costs under control but often results in degraded performance and higher latency during traffic spikes. GPUs sized for average demand can quickly become a bottleneck under peak loads.

2. Provisioning for maximum capacity: Helps ensure performance at all times but leads to inefficiency and waste during off-peak periods. Overprovisioning GPUs for peak demand leads to costly idle capacity—an often overlooked expense in AI operations.

As a result, many teams struggle to strike the right balance between cost and performance, making production-grade inference difficult to achieve without smarter infrastructure.

Transition: From Bottlenecks to Breakthroughs

To move beyond these scaling limitations, organizations need a new approach to GPU management—one that’s adaptive, workload-aware, and capable of responding in real time to fluctuating demand.

Nutanix addresses these challenges through three key strategies that redefine how GPUs are provisioned, utilized, and optimized for LLM inference at scale:

1. Scaling for the Next Generation of LLMs — enabling efficient support for larger models and expanding context windows

2. Dynamic Resource Management and Caching — minimizing idle capacity while reducing cold-start latency

3. Maximizing Inference Performance — driving higher throughput from existing GPUs.

The Nutanix Advantage: Dynamic Scaling for LLM Inference

Dynamic, distributed, and data-driven — Nutanix turns infrastructure into an intelligent AI engine.

Nutanix simplifies what most AI teams find hardest—aligning GPU resources with real-world inference demand. Rather than relying on static allocations or overprovisioned clusters, Nutanix provides a smarter, software-defined foundation that scales inference dynamically across nodes, clusters, and clouds.

At the forefront is the Nutanix Enterprise AI (NAI) solution, which serves as the intelligent control plane to operationalize the dynamic scheduling of LLM inference endpoints. NAI abstracts the complexity of validating that models are correctly sized to run on GPU-enabled nodepools managed by the Nutanix Kubernetes Platform solution (NKP) solution.

It achieves this by automatically calculating the resource requirements for a given endpoint based on factors such as model parameters and weights, token context windows and length, GPU device type and count, and inference engine type (e.g., NVIDIA NIM or vLLM) to provide optimized resourcing recommendations.

When combined with the Nutanix infrastructure support for fractional GPU partitioning technologies such as NVIDIA MIG and vGPU, the platform helps ensure that inference endpoints scale efficiently. By enabling shared inference endpoints, the NAI solution allows for better utilization of the underlying hardware - letting high-end NVIDIA GPUs like the H200, RTX PRO 6000 or B200 be securely shared among multiple inference services.

This architecture promotes optimal GPU utilization and isolation between workloads, helping teams run concurrent LLMs or smaller variants efficiently on shared hardware.

To complement dynamic scaling, the Nutanix Unified Storage (File Services) solution provides high-speed, enterprise-grade performance and scalability by dynamically provisioning the NFSoRDMA remote storage required to support multiple model replicas. This allows organizations to scale inference clusters flexibly while maintaining high availability, reliability, and throughput.

By integrating with GPUDirect Storage (GDS), Nutanix Unified Storage (File Services) delivers an optimized, low-latency data path between GPU memory and storage—essential for KV cache management, where intermediate attention states and tensors are frequently accessed during inference.

Together, Nutanix Enterprise AI (NAI), Nutanix Kubernetes Platform (NKP) and Nutanix Unified Storage (File Services) - integrated with GDS - help deliver a unified, high-performance AI foundation that accelerates inference, optimizes resource usage, and enables seamless scalability across hybrid and multi-cloud environments.

Let’s explore how Nutanix leverages these capabilities to tackle the three core challenges of LLM inference at scale.

1. Built to Scale: Powering the Next Generation of LLMs with Nutanix

Scale linearly with demand — minimizing downtime and bottlenecks. Nutanix keeps LLMs always-on and always-ready.

Accelerating enterprise AI adoption is driving demand for larger model sizes, expanded context windows, longer context lengths, and higher levels of concurrency.

Supporting these larger model demands requires infrastructure that can offload and manage massive KV caches:

• Horizontal scaling dynamically distributes workloads across multiple clusters and nodes, allowing inference capacity to expand with demand
• Vertical scaling through GPU partitioning enables multiple models of varying sizes to coexist on the same hardware—maximizing GPU efficiency while maintaining isolation
• KV cache offload and management becomes critical as context windows grow, requiring fast, low-latency access to large intermediate states without overwhelming GPU memory

Here again, Nutanix Files Storage plays a critical role. Its elastic, distributed architecture provides the high-speed remote storage needed to dynamically provision and serve multiple model replicas simultaneously. 

Between Nutanix Files Storage, NFSoRDMA and integration with GDS, KV caches can be transferred directly between GPU memory and remote NFS storage with minimal latency, allowing inference clusters to scale linearly with demand, while providing the reliable availability and consistent performance needed for large-scale LLM deployments.

2. When Scale Meets Speed: Dynamic Resource Management for Near Instant LLM Readiness

When Every Second Counts: Reducing LLM pods cold start times isn’t just faster — it’s the difference between user frustration and instant response.

NAI offers a flexible, API-driven approach that helps manage both real-time online inference and background workloads (such as batch or scheduled tasks).

Through programmatic hibernate and resume capabilities available on each NAI managed inference endpoint, the system allows teams to reallocate GPUs from non-critical background tasks to latency-sensitive inference when demand surges.

By maintaining a pool of standby GPU capacity or freeing up resources on demand, NAI helps activate additional resources within seconds. This capability promotes major cost control without sacrificing performance.

A key challenge to enabling these capabilities are the cold start delays in LLM endpoint initialization. For Kubernetes^® administrators, initializing an LLM instance can take a frustrating amount of time, often due to:

• Large model weights (tens of GBs)
• Dense container images (GBs)
• Initialization and runtime dependency load times (e.g., plugins, drivers and libraries)

To mitigate these delays, NAI leverages inference engine innovations and integration with Nutanix Files Storage with NFS over RDMA (NFSoRDMA).

These capabilities help significantly reduce cold start times and improve GPU reuse — critical for lowering token response latency and increasing throughput in large-scale LLM deployments where KV cache management plays a major role in keeping inference responsive.

3. More from Every GPU: Maximizing Inference Utilization Without More Hardware

Performance optimization isn’t only about adding GPUs—it’s about getting more from the GPUs you already have.

NAI helps maximize efficiency by intelligently orchestrating the placement of inference services onto existing GPU infrastructure. Rather than dedicating an entire high-performance GPU to a single workload, NAI leverages the NVIDIA GPU Operator to utilize hardware partitioning capabilities - such as NVIDIA Multi-Instance GPU (MIG) and virtual GPU (vGPU).

These capabilities enable a single physical GPU to be securely subdivided into multiple isolated compute instances, each with guaranteed allocations of GPU cores, memory, and bandwidth. This isolation helps provide predictable performance and strong multi-tenant boundaries while allowing multiple inference services to run concurrently on the same physical accelerator.

By right-sizing compute resources for specific models, NAI can help run more AI workloads across fewer physical resources, maximizing inference utilization without more hardware.

Conclusion

Building production-grade LLM serving infrastructure is no longer about adding GPU hardware- it’s about orchestrating resources intelligently.

With Nutanix Enterprise AI (NAI), organizations achieve a balance of speed, scalability, and cost-efficiency through an integrated platform that:

• Helps improve speed by making inference endpoint readiness faster and more reliable via Nutanix Files with NFSoRDMA and GDS.
• Helps enhance scalability by offloading KV cache to Nutanix Files Storage, freeing up high-bandwidth memory (HBM).
• Helps maximize cost efficiency by taking advantage of existing infrastructure via GPU partitioning (MIG/vGPU) with NAI and NKP.

Together, NKP, NAI and Nutanix Files Storage form the foundation of a truly optimized inference architecture - one that scales effortlessly across hybrid and multi-cloud environments.

This unified approach ensures that every GPU cycle counts, every inference runs fast, and every model deployment scales with the confidence and reliability enterprises expect from Nutanix.

Key Takeaways

• Faster LLM startup and recovery: Optimized data paths help reduce cold-start latency and improve service readiness
• Smarter GPU utilization: Intelligent workload placement and partitioning help maximize resource efficiency
• Scalable KV cache management: Nutanix Files Storage with GDS helps enable linear scaling across replicas and clusters.
• Enterprise-grade reliability: Promotes consistent performance during demand spikes, helping minimize disruption.
• Elastic inference infrastructure: Dynamic scaling and KV cache offload enable efficient, high-throughput LLM serving.

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