Reference Architecture for Large Language Model (LLM) Deployment Using vLLM

The Challenge:

LLMs are inherently memory-bound due to the transformer architecture’s reliance on storing large key-value (KV) caches for attention layers. As the sequence length and number of concurrent users increase, GPU memory becomes the primary bottleneck. For instance, a 70B parameter model can require hundreds of gigabytes of GPU memory to serve a single batch with long contexts.

This creates a hardware-software tension: while GPUs offer massive parallelism, their limited vRAM and interconnect bandwidth constrain their ability to host and serve full LLMs without sophisticated memory and execution optimizations.

To address these limitations, modern serving frameworks like vLLM are designed to work in tandem with GPU architectures, pushing the limits of hardware efficiency through techniques like dynamic memory paging, continuous batching, and model parallelism—topics we’ll explore in later sections.

In the following section, we’ll examine why LLMs can no longer fit on a single server and what architectural strategies are required to serve them reliably at scale.

1. GPU Architecture and the Nature of LLMs

Modern GPUs, such as NVIDIA’s A100, H100, and L4, are purpose-built for deep learning workloads, offering thousands of CUDA cores, high memory bandwidth, and support for mixed-precision arithmetic (FP16/BF16/INT8). These features make GPUs the de facto hardware choice for serving LLMs. However, even with these powerful capabilities, running large LLMs efficiently, especially in real-time applications, requires more than just raw compute power.

Overview of modern GPU architectures (e.g., A100, H100, L4)

Modern GPUs are the backbone of LLM inference due to their massively parallel architecture and optimized support for deep learning workloads. Unlike CPUs, which are designed for general-purpose sequential processing, GPUs contain thousands of cores capable of performing simultaneous computations—ideal for handling the matrix multiplications and attention mechanisms that dominate transformer-based models.

Precision Support and Efficiency

Modern GPUs increasingly rely on mixed precision (e.g., FP16/BF16 with automatic loss scaling) to improve throughput without sacrificing model accuracy. The ability to run LLMs at lower precision while maintaining numerical stability has significantly improved the speed and scalability of inference.

However, running models in FP8 or INT8 via quantization can lead to reduced output quality in many real-world cases, particularly for multi-turn conversations and instruction-following tasks. Thus, full-precision inference across multiple GPUs remains the gold standard for enterprise-grade deployment.

Why LLMs Are Compute- and Memory-Intensive

Large Language Models (LLMs) like DeepSeek, LLaMA 3, and Mixtral are built on the transformer architecture, which is inherently both compute-bound and memory-bound due to the way it handles data and model weights during inference.

1. Billions of Parameters = High Memory Footprint

Modern LLMs can contain tens to hundreds of billions of parameters, each representing a learned weight that must be stored and accessed at runtime. For example:

• A 7B parameter model (e.g., LLaMA 3 8B) requires ~14–28 GB of memory in FP16/BF16.
• A 65–70B model requires ~140–280 GB, depending on precision and optimizer states.

In inference, these weights must reside in GPU VRAM, and transferring them from CPU RAM or disk causes unacceptable latency. This imposes a hard constraint on memory capacity and bandwidth.

2. Transformer Attention Mechanism Is Quadratic

The core computation in transformers is self-attention, which scales with the square of the sequence length:

Where:

• n is the number of tokens
• d is the embedding dimension

As the sequence length grows (e.g., in long documents or multi-turn conversations), the attention matrix grows quadratically, demanding more compute and memory.

3. Key/Value (KV) Cache Grows with Sequence and Concurrency

During inference, transformer decoders cache key and value tensors for each token to avoid recomputing attention over past tokens. These KV caches scale with:

• Batch size
• Sequence length
• Number of concurrent users

This leads to exponential growth in memory requirements. For example:

• A single user with a 2,048-token context may consume ~2GB VRAM for KV cache alone.
• Hundreds of concurrent users rapidly exhaust available memory, even on 80GB A100s.

4. Limited Opportunities for Parallelism Without Smart Scheduling

Transformers are less parallelizable across layers than CNNs or RNNs due to auto-regressive decoding in LLMs, which requires sequential token generation. This limits how much can be parallelized across GPU cores without smart techniques like:

• Tensor parallelism
• Continuous batching
• Pipeline inference

Without these, GPU utilization stays low despite high model demands.

Transformer architecture basics and memory bottlenecks

The transformer is the foundational architecture behind all modern LLMs. First introduced in the paper “Attention is All You Need”, it has since been scaled from millions to hundreds of billions of parameters. Understanding the basic structure of transformers is essential to appreciate why LLMs consume so much memory and compute at inference time.

Transformer Core Components

A typical decoder-only transformer used in LLMs consists of a stack of identical layers, each containing:

• Multi-Head Self-Attention (MHSA): Enables the model to weigh the importance of different tokens in the input sequence. Each attention head computes queries, keys, and values (Q, K, V) and performs dot-product attention.
• Feedforward Network (FFN): A two-layer MLP applied to each token independently after the attention output.
• Layer Normalization: Helps stabilize training and inference across layers.
• Residual Connections: Bypass pathways that help prevent gradient vanishing and allow deep models to converge.

Memory Bottlenecks in Inference

Key/Value Cache Growth

• During auto-regressive generation, each generated token is cached in the form of key and value tensors.
• The size of KV cache scales linearly with:
• Long prompts and multiple concurrent users can quickly overwhelm GPU memory.

Attention Matrix Scaling

• Self-attention has quadratic complexity with respect to sequence length:

• This makes long-context models (e.g., 8k or 32k tokens) highly memory-intensive, especially when decoding per-token with each step attending to all prior tokens.

Weight Size

Each transformer layer has learnable weights in the attention and feedforward modules. For large models (e.g., LLaMA 65B), the full parameter set often exceeds 300 GB in FP32, requiring offloading, sharding, or compression to fit in memory.

Inefficient Memory Allocation

Traditional inference stacks often allocate memory statically or per-request, leading to fragmentation and underutilization. Without dynamic memory management, even a 80GB GPU can hit out-of-memory errors under real-world workloads.

2. Why LLMs Cannot Fit on a Single Server

As LLMs scale from billions to hundreds of billions of parameters, the demand on GPU hardware increases exponentially. Even the most advanced single-GPU servers—equipped with top-tier GPUs like NVIDIA A100 or H100 with 80GB VRAM are unable to accommodate the full memory and compute requirements of these large models without performance degradation, quantization compromises, or architectural workarounds.

Model Size vs. GPU vRAM Limitations

Even with model sharding and tensor parallelism, running a model like LLaMA 70B in full precision without quantization would require at least 6–8 GPUs, each with 80 GB of VRAM.

This creates two critical challenges:

• Insufficient memory capacity per GPU to hold all layers and weights
• Inability to store key-value caches and activations at scale with long prompts or multi-user concurrency

PCIe and NVLink Interconnect Limitations

When running multi-GPU inference within a single server, GPUs must share data through PCIe or NVLink interconnects. These links move weights and activations between GPUs in real time during forward and backward passes.

However:

• PCIe Gen4 offers ~32 GB/s per lane (totaling ~64 GB/s duplex per GPU)
• NVLink (3.0) can reach up to 600 GB/s between compatible GPUs
• By contrast, HBM2/3 memory inside the GPU operates at ~1.5–3.5 TB/s

This huge bandwidth disparity means:

• Transferring model weights and activations across GPUs quickly becomes a bottleneck
• Multi-GPU setups in a single server (even with NVLink) cannot match the performance of on-die memory access
• For true scalability, models must be partitioned smartly, and workloads should be managed across nodes, not just across GPUs on one machine

Sharded Weights and Activation Checkpointing

To work around memory constraints, modern LLM serving stacks use a combination of weight sharding and activation checkpointing:

1. Sharded Weights

• Model layers are divided across multiple GPUs using tensor parallelism.
• Each GPU is responsible for computing part of a layer (e.g., one slice of a multi-head attention operation).
• This reduces per-GPU memory pressure but introduces synchronization overhead.

2. Activation Checkpointing

• Instead of storing all intermediate activations, the system selectively recomputes them during backpropagation or multi-token decoding.
• Useful for training, but limited for inference due to performance costs.

Even with these strategies, inference at scale (especially with long context windows and multi-user concurrency) often requires multi-node deployments across clusters, not just multi-GPU servers.

The Takeaway

No matter how advanced a single server is, it cannot efficiently serve massive LLMs like LLaMA 70B or DeepSeek-R1 without:

• Precision trade-offs (e.g., quantization)
• Latency penalties due to memory bottlenecks
• Complexity in model sharding and KV cache management

The solution lies in distributed serving across multiple nodes, leveraging optimized runtimes like vLLM to manage memory, batching, and parallelism at scale.

Next, we’ll explore why vLLM is essential for multi-node LLM deployment and how it simplifies and accelerates this process.

3. Why Multi-GPU Execution

As the parameter count and sequence length of large language models (LLMs) grow, inference workloads begin to exceed the capabilities of a single GPU or even a single server. To maintain model quality, support concurrent users, and achieve low-latency serving at production scale, LLM deployments must evolve from standalone GPU nodes to distributed, multi-node inference architectures.

This section explores why multi-node execution is necessary, what challenges it introduces, and how vLLM solves them with a purpose-built, high-performance LLM runtime.

Precision Trade-Offs: Quantization vs. Full-Precision Inference

One common strategy for fitting LLMs into limited GPU memory is quantization, reducing model weights from FP32 or FP16 to lower-precision formats like INT8 or 4-bit.

While quantization helps reduce memory footprint and compute costs, it comes with non-trivial trade-offs:

• Accuracy degradation, especially in tasks requiring nuanced reasoning or multi-step dialogue
• Incompatibility with certain model layers or fine-tuned weights
• Instability in autoregressive decoding for large sequence generation

For production-grade deployments, especially in regulated industries or mission-critical applications, full-precision inference using FP16/BF16 remains the gold standard. But this requires massive memory and compute, making multi-node, full-precision serving essential.

Why Multi-Node Deployment Is Required for LLMs

Even with the best GPUs, large models like LLaMA 70B, Falcon 180B, or Mixtral cannot fit into a single device. Multi-node execution enables:

• Model Sharding: Splitting weights and layers across GPUs in different servers
• Tensor Parallelism: Dividing computations within layers across multiple GPUs
• Scalability: Serving more users concurrently by pooling memory and compute across nodes

However, deploying LLMs across nodes introduces non-trivial challenges:

Challenges with Parallelism at Scale

• Tensor Parallelism requires tight synchronization and introduces latency across nodes.
• Pipeline Parallelism adds complexity to the model execution flow and can increase tail latency.
• Sequence Parallelism (splitting batch requests across GPUs) risks fragmentation and irregular load distribution.
• KV Cache management becomes difficult with disjoint memory spaces across GPUs.

Traditional inference stacks are not designed for this level of dynamic coordination and efficient memory reuse, resulting in low GPU utilization, high latency, and unstable throughput.

4. Why vLLM Is Essential for Multi-Node LLM Deployment

vLLM is purpose-built to tackle the exact challenges posed by multi-node, multi-GPU LLM inference. It enables efficient, production-ready serving of large models by combining deep systems-level optimization with transformer-specific execution strategies.

Native Support for Parallelism and Multi-GPU Execution

vLLM supports:

• Tensor Parallelism across multiple GPUs and nodes
• Smart request scheduling that minimizes idle time and balances load
• Integration with distributed runtimes (e.g., Ray, Kubernetes, OpenShift AI)

This allows enterprises to deploy models like LLaMA 70B or Mixtral over 8–16 GPUs, scaling up and down as needed without modifying the model code.

Efficient GPU Memory Usage with PagedAttention

vLLM introduces PagedAttention, a revolutionary mechanism that:

• Organizes KV cache into fixed-size memory pages
• Enables non-contiguous memory allocation and reuse
• Reduces fragmentation by allowing dynamic reclamation of attention memory blocks

PagedAttention alone can lead to 2–3× more concurrent users on the same GPU compared to traditional frameworks.

Load Balancing Across GPUs and Nodes

vLLM includes a continuous batching engine that:

• Dynamically groups incoming requests, regardless of sequence length
• Optimally distributes compute and memory load across all available GPUs
• Minimizes idle time by overlapping compute and memory I/O

This ensures high throughput and consistent latency, even under variable and bursty workloads.

5. vLLM System Architecture

vLLM is a high-performance, OpenAI-compatible LLM inference engine built from the ground up to optimize memory, throughput, and scalability. At its core, vLLM introduces architectural innovations that enable it to serve large models efficiently across multi-GPU and multi-node environments without compromising accuracy or responsiveness.

This section outlines the key system components, memory layout, and parallelism integration that make vLLM uniquely optimized for modern LLM deployment.

High-Level Components

The vLLM runtime is composed of the following modular components:

1. API Server

• Implements an OpenAI-compatible HTTP interface, including support for /v1/chat/completions and /v1/completions.
• Acts as the entry point for user queries and integrates easily with LangChain, Chatbot UIs, or enterprise APIs.

2. Engine (LLM Core)

The execution core of vLLM that handles:

• Token generation (autoregressive decoding)
• Model execution across GPUs
• Dynamic batching and cache management

3. Scheduler

A high-efficiency token-level scheduler that:

• Groups compatible requests for maximum GPU utilization
• Prioritizes low-latency execution by mixing short and long prompts
• Ensures fair access and queue stability under load

4. Memory Manager

Manages GPU memory allocation for:

• Model weights (static)
• KV cache (dynamic, grows with sequence/user count)
• Intermediate activations (temporary)

The memory manager is tightly integrated with the PagedAttention mechanism, enabling smart reuse of memory blocks to reduce fragmentation.

 Memory Layout: KV Cache, Attention Pages, Prefix Cache

Efficient memory management is at the heart of vLLM's performance gains. The engine handles memory using several abstractions:

KV Cache

• Stores key and value tensors needed for self-attention during inference.
• Scales with number of tokens, layers, and concurrent requests.
• In traditional systems, KV cache is a dense array—leading to waste and out-of-memory (OOM) errors under load.

PagedAttention

• vLLM splits the KV cache into fixed-size memory pages that can be:
• This layout mimics virtual memory paging, preventing fragmentation and enabling 2–3× more active sequences per GPU.

Prefix Cache

• Allows reuse of common prompt segments across multiple requests (e.g., system prompts, few-shot examples).
• Once a prompt is processed into KV cache, it is stored persistently and reused, saving both time and memory.

These memory techniques allow vLLM to serve many users at once without inflating memory usage or reprocessing redundant content.

Parallelism Model Integration

To serve models that exceed a single GPU’s capacity, vLLM integrates multiple parallelism strategies:

Tensor Parallelism

• Model layers are sharded horizontally across GPUs.
• Each GPU holds a slice of the layer and computes a portion of the forward pass.
• Enables support for 13B, 70B, and larger models by distributing compute and memory.

Pipeline Parallelism

Pipeline parallelism is a technique where different layers (or groups of layers) of a transformer model are assigned to different GPUs, and data flows through them in a staged manner—similar to an assembly line.

Instead of each GPU holding a slice of every layer (as in tensor parallelism), pipeline parallelism assigns entire blocks of layers to individual GPUs. For example, in a 48-layer model across 4 GPUs, each GPU may be responsible for 12 layers.

How It Works:

• A batch of input tokens is processed through the first stage (first GPU).
• The resulting activations are passed to the next GPU, and so on.
• Multiple micro-batches are processed concurrently, with each GPU working on a different stage in parallel.

Benefits:

• Reduces per-GPU memory usage, since each GPU only needs to store a subset of model weights.
• Enables scaling to larger models when tensor parallelism alone isn’t sufficient.

Challenges:

• Introduces pipeline bubbles—idle time when GPUs wait for inputs from upstream stages.
• Requires careful scheduling to minimize latency and maximize throughput.
• Best suited for batch inference or high-throughput scenarios.

While vLLM currently focuses more heavily on tensor parallelism and continuous batching, pipeline parallelism can complement these strategies in future extensions or hybrid deployment patterns for ultra-large models.

Expert Parallelism

Expert Parallelism is a model parallelism technique used in Mixture-of-Experts (MoE) architectures, where only a subset of the model's “experts” (specialized sub-networks) are activated for each input. Unlike standard dense models where all parameters are used for every token, MoE models route tokens to selected experts—reducing computation and memory costs. Expert Parallelism distributes these experts across multiple GPUs, enabling larger models to scale efficiently while preserving inference speed. While vLLM currently focuses on dense models, future integration with MoE-based expert parallelism could further enhance scalability and efficiency for ultra-large deployments.

vLLM Supports Mixed Parallelism

vLLM supports mixed parallelism, combining multiple parallelism strategies—such as tensor parallelism, pipeline parallelism, and sequence-level parallelism—to efficiently serve large models across multiple GPUs and nodes. By flexibly applying the right type of parallelism at different layers or execution stages, vLLM maximizes hardware utilization while minimizing inter-GPU communication overhead. This hybrid approach enables scalable inference for extremely large models (e.g., 70B+) without sacrificing performance or latency, and is especially valuable in heterogeneous environments where GPU capabilities and workloads vary. Mixed parallelism in vLLM ensures optimal execution paths for both high-throughput batch processing and low-latency, real-time use cases.

Continuous batching across sequences

Traditional LLM serving stacks rely on static batching, where requests are grouped together into fixed-size batches and processed synchronously. This approach introduces latency, underutilizes GPU resources, and fails to scale under dynamic, high-throughput workloads, especially when user inputs vary in length and arrival time.

vLLM introduces Continuous Batching, a key innovation that allows the engine to dynamically and asynchronously batch tokens across sequences and users, without requiring synchronized request timing.

How It Works:

• Each new token to be generated is treated as an independent unit of work.
• The vLLM scheduler collects tokens from multiple requests (even at different stages of generation) and merges them into token-level micro-batches.
• These are passed through the model together, ensuring high GPU utilization.

Key Benefits:

• Maximized GPU Throughput: Keeps all streaming multiprocessors (SMs) active by ensuring a steady flow of work.
• Lower Latency: No need to wait for full batches to form. Short, real-time queries are handled promptly.
• Heterogeneous Request Support: Efficiently serves requests with different sequence lengths, prompt structures, or priorities in a unified scheduling model.

Example:

A long text generation task and a short chatbot reply can be processed simultaneously, with vLLM handling token-level scheduling behind the scenes. This enables high concurrency and low tail latency in production scenarios such as conversational AI or multi-user inference APIs.

Multi-Node Support

• vLLM is compatible with distributed execution using Ray, OpenMPI, or Kubernetes-based platforms.
• GPUs across different nodes can participate in parallelism transparently.

Compatibility with DeepSpeed, HuggingFace-style models

One of vLLM’s strengths is its ability to integrate seamlessly with the existing LLM ecosystem, allowing organizations to adopt it without rewriting or re-exporting models.

HuggingFace Model Compatibility

vLLM supports most decoder-only transformer architectures trained and exported using the HuggingFace Transformers library, including:

• GPT-style models (e.g., GPT-2, GPT-J, GPT-NeoX)
• Meta's LLaMA (7B, 13B, 70B), Falcon, Mistral, and other modern open models
• Tokenizer support via HuggingFace tokenizers for consistent pre- and post-processing

You can load models directly from HuggingFace .bin or .safetensors formats using a one-line configuration in vLLM.

DeepSpeed Integration (Limited)

While vLLM does not rely on DeepSpeed for inference, it is compatible with models trained using DeepSpeed:

• You can serve weights fine-tuned with DeepSpeed (as long as they follow HuggingFace or PyTorch checkpoint conventions).
• DeepSpeed-specific serving features (e.g., ZeRO inference, quantization-aware sharding) are not required for vLLM.

This independence from training infrastructure gives vLLM users freedom of choice: you can train models with HuggingFace, PyTorch Lightning, or DeepSpeed, and serve them efficiently using vLLM.

Model Conversion Tools

vLLM offers tools to convert or validate model checkpoints if minor adjustments (e.g., tensor naming, config correction) are needed to ensure compatibility.

6. Performance, Efficiency, and Cost Optimization with vLLM

vLLM was engineered to push the boundaries of performance and scalability in large language model inference. It introduces architectural innovations that not only make LLMs faster and more responsive but also dramatically reduce the GPU footprint, operational cost, and infrastructure complexity required for production-scale serving.

This section highlights how vLLM achieves efficiency through smart memory management and batching, how it translates to cost savings, and how its performance compares to conventional serving frameworks.

Model Efficiency Optimizations

vLLM brings multiple memory and scheduling innovations that reduce VRAM usage and maximize GPU throughput without compromising model accuracy.

PagedAttention

Traditional frameworks allocate KV cache memory contiguously, leading to fragmentation and poor scalability. vLLM introduces PagedAttention, which:

• Breaks KV cache into fixed-size memory pages
• Allows non-contiguous, on-demand allocation and reuse
• Enables the engine to serve 2–3× more concurrent sequences compared to dense allocation strategies

PagedAttention is central to running large models efficiently across many users and long contexts, especially on memory-constrained GPUs.

Unified KV Cache Management

Instead of managing KV cache on a per-request basis, vLLM uses a global, unified KV cache:

• Shares memory blocks across active sequences
• Automatically reclaims memory from completed requests
• Supports streaming and long-running sessions without memory bloat

This leads to much more predictable memory usage and smoother scaling as user load increases.

Prefix Caching (Prompt Reuse)

vLLM supports prefix caching, where repeated prompt prefixes (e.g., system prompts, few-shot instructions) are computed once and reused:

• Reduces redundant computation across similar queries
• Lowers both latency and GPU utilization
• Particularly beneficial for chat applications and APIs using static preambles

Continuous Batching

Unlike static batching, vLLM uses token-level continuous batching:

• Dynamically assembles micro-batches from asynchronous requests
• Handles requests with different sequence lengths and arrival times
• Keeps GPUs fully utilized without waiting for full batch formation

This results in higher throughput and lower token-level latency, especially in real-time use cases like RAG or multi-user chat.

GPU Memory Reuse

All memory used for attention and intermediate activations is dynamically recycled in vLLM:

• Reduces the peak memory footprint
• Avoids unnecessary GPU OOM (out-of-memory) errors Enables smoother support for long-context models and bursty workloads

Cost Reduction with vLLM

Efficiency directly translates into lower infrastructure cost, which is critical for deploying LLMs in production environments.

Lower GPU Count per Throughput

vLLM’s optimizations allow you to serve:

• More concurrent users per GPU
• Higher tokens per second per dollar
• Reduced need for overprovisioning expensive A100/H100 nodes

For example, on an A100 80GB GPU, vLLM can handle 2–4× the request volume compared to HuggingFace Transformers-based inference.

Better GPU Utilization = Fewer Idle Cores

Thanks to continuous batching and intelligent scheduling:

• No GPU core sits idle waiting for a batch to fill
• vLLM operates at consistently high SM occupancy
• You get maximum value out of every deployed GPU

Fewer Inference Hours

vLLM completes generation tasks faster, which means:

• Lower compute time per request
• Reduced total GPU runtime and billing hours in cloud environments (e.g., AWS, GCP, Azure)
• Better cost-per-token generation metrics

Smarter Trade-Offs than Quantization

Instead of resorting to low-precision quantization (which degrades output quality), vLLM achieves memory and speed gains while staying in full FP16/BF16 precision—ideal for enterprise applications that demand accuracy.

Improved Latency and Throughput

vLLM delivers state-of-the-art performance across several metrics, without model modification or quantization.

Lower Token Latency

Thanks to asynchronous execution and prompt reuse:

• Latency per token is significantly reduced
• Supports streaming token generation with minimal delay
• Ideal for real-time applications like chatbots and code generation

Higher Tokens/Sec per GPU

vLLM achieves dramatically higher throughput per device:

• A100 80GB: 2500–3000 tokens/sec
• H100: 3000–5000+ tokens/sec (depending on model size and concurrency)
• Outperforms HuggingFace Transformers, FasterTransformer, and DeepSpeed inference stacks in side-by-side benchmarks

7. Enterprise-Ready Deployment with Red Hat AI

Running large language models (LLMs) in production goes far beyond model performance—it requires a robust, secure, and scalable infrastructure that meets enterprise standards for reliability, compliance, and lifecycle management. Red Hat OpenShift AI (RHOAI) provides a comprehensive platform to operationalize vLLM in such environments.

This section outlines how vLLM can be integrated into OpenShift AI, enabling secure, multi-model, multi-GPU inference workloads across hybrid or multi-cloud infrastructure.

Overview of OpenShift AI with vLLM Container Integration

Red Hat OpenShift AI is an enterprise-grade MLOps platform built on OpenShift Kubernetes, designed to support the full lifecycle of AI/ML workloads—from model development to scalable inference.

vLLM can be deployed as a containerized inference service on OpenShift AI using:

• A custom vLLM container image with preloaded models
• A Deployment or KServe-based object
• OpenShift AI's GPU-enabled compute nodes

This setup benefits from:

• Built-in container orchestration
• GPU-aware scheduling
• Seamless integration with Red Hat’s CI/CD, monitoring, and networking stack

GPU Partitioning and Isolation (MIG, Multi-Instance GPU)

Red Hat OpenShift AI supports NVIDIA Multi-Instance GPU (MIG) for partitioning high-end GPUs (like A100 or H100) into multiple logical GPU instances:

• Enables isolation of vLLM serving containers by user or model
• Increases GPU utilization efficiency by matching resource requests to container size
• Supports multi-tenant deployments where different vLLM instances serve different LLMs with predictable resource boundaries

Example: Deploy one vLLM model per MIG slice to serve different business units independently.

CI/CD, Security, and Scaling

Red Hat AI and OpenShift provide DevOps pipelines and enterprise features critical for managing vLLM services in production:

CI/CD Pipelines (Tekton, Argo CD):

• Automate model packaging, testing, and promotion
• Build and deploy vLLM containers with reproducibility and auditability

Security (Red Hat Trusted Software Supply Chain):

• Secure model images with Sigstore/COSIGN
• Use role-based access control (RBAC) and OpenShift Service Mesh for request isolation
• Integrate with LDAP, SSO, or custom auth for API access to vLLM endpoints

Horizontal Scaling:

• Use Kubernetes autoscalers and vLLM’s stateless architecture to spin up new replicas based on load
• Deploy multiple vLLM instances across nodes to serve different LLMs or load-balance requests

Integration with RHOAI ModelMesh for Multi-Model Serving

ModelMesh Serving is OpenShift AI’s dynamic model server that enables on-demand, lazy-loaded model inference, ideal for large models like those served by vLLM.

Key capabilities:

• Load models on GPU only when needed, saving memory
• Route requests dynamically across multiple vLLM instances
• Enable multi-model deployments using a shared GPU pool
• Fully integrated with KServe and OpenShift GPU Operator

This makes it easy to serve multiple LLMs (e.g., LLaMA 13B, 70B, Mistral) from a common vLLM infrastructure using unified APIs.

Storage and Observability Considerations

For production reliability and traceability, vLLM + OpenShift AI benefits from integrated monitoring and persistent model management:

Model Storage:

• Host vLLM models in S3-compatible object storage (e.g., OpenShift Data Foundation, AWS S3, MinIO)
• Mount models into pods using CSI drivers or ephemeral volumes
• Support for pre-loading models or downloading them at pod startup

Observability:

• Use OpenShift Monitoring Stack (Prometheus + Grafana) to track:
• Integrate with Red Hat Insights, Elasticsearch, or Sysdig for security, logging, and auditing

By combining the performance of vLLM with the operational excellence of Red Hat OpenShift AI, enterprises can:

• Serve massive LLMs efficiently across GPUs and nodes
• Manage models securely, at scale, with automation
• Support multi-tenancy, self-service, and elastic inference with confidence

This architecture ensures not only cutting-edge LLM serving performance, but also enterprise-readiness across compliance, scalability, and lifecycle management.

In the final section, we’ll summarize key takeaways and outline the next steps for adopting this stack in production.

8. Conclusion

Deploying large language models (LLMs) in production is no longer a frontier experiment—it’s an enterprise imperative. However, the path from model checkpoint to scalable, real-time inference is riddled with challenges: memory constraints, performance bottlenecks, operational complexity, and cost.

vLLM, combined with Red Hat OpenShift AI, offers a practical, production-grade solution that solves these challenges head-on.

This combination delivers:

• High throughput and low latency for models from 7B to 70B+
• Efficient use of expensive GPU infrastructure
• Full-stack observability, auditability, and security
• Seamless integration with MLOps, APIs, and enterprise services

Together, vLLM + Red Hat AI enable LLM inference that is not only fast—but reliable, cost-effective, and ready for production.

Path to PoC and Deployment for LLM Inference at Scale

Getting started with vLLM and OpenShift AI is straightforward with the following phased approach:

Phase 1: Proof of Concept (PoC)

• Select target LLMs (e.g., LLaMA 13B or Mistral 7B)
• Deploy vLLM containers on GPU-enabled OpenShift AI nodes
• Benchmark latency, throughput, and memory utilization
• Integrate with internal APIs or chat frontends

Phase 2: Pilot Deployment

• Scale vLLM across multiple GPUs or nodes
• Introduce ModelMesh for multi-model routing
• Enable observability, logging, and user access control
• Automate CI/CD pipelines for model lifecycle

Phase 3: Production Rollout

• Serve large models (e.g., LLaMA 70B, Mixtral) across distributed GPU clusters
• Partition GPUs with MIG for multi-tenant workloads
• Secure endpoints, enable monitoring, and optimize cost per token
• Integrate into RAG systems, assistants, and business workflows

By following this path, organizations can evolve from local experimentation to a scalable, auditable, and efficient LLM inference platform.

vLLM brings the performance. Red Hat OpenShift AI brings the reliability. Together, they unlock real-world LLM applications at enterprise scale.