The Best Choice for AI Inference: vLLM

Originally posted on Medium.

Authors: Fatih E. NAR, Greg Pereira, Yuan Tang, Robert Shaw, Anish Asthana

As organizations move from Large language model (LLM) experimentation to production deployment, the choice of inference platform becomes a critical business decision. This choice impacts not just operational performance, and also flexibility, cost optimization, and the ability to adapt to rapidly evolving business needs.

For technologists & solution architects evaluating LLM inference platforms, three key considerations should drive the decision:

Architectural Flexibility: The ability to deploy across diverse hardware accelerators and hybrid cloud environments without vendor lock-in.
Operational Scalability: Support for advanced deployment patterns that can scale from single-GPU deployments to distributed, multi-node architectures.
Ecosystem Openness: Ecosystem Openness: Compatibility with the broadest range of models and kernel support as well as integration with a wide range & variety of enterprise-software ecosystem.

vLLM, uniquely addresses these considerations through its open-source foundation, advanced memory management capabilities, and upcoming distributed deployment blueprints. Unlike proprietary or hardware-specific solutions, this combination provides the flexibility to optimize for cost, performance, and operational requirements as they evolve.

This article examines why vLLM’s technical architecture & abilities, particularly its KV-Cache management, parallelization strategies, and with the upcoming llm-d distributed capabilities provides the most sustainable path for production LLM deployment.

The Open-Source Advantage

Community-Driven Innovation at Scale

The evolution of LLM inference has been fundamentally shaped by open-source innovation. vLLM has achieved remarkable success in supporting diverse models, features, and hardware backends over the past 1.5 years vLLM V1: A Major Upgrade to vLLM’s Core Architecture - vLLM Blog, growing from a UC Berkeley research project to become the de facto serving solution for the open-source AI ecosystem vLLM 2024 Retrospective and 2025 Vision - vLLM Blog.

This transformation illustrates a critical advantage; open-source projects can iterate and adapt faster than proprietary solutions. vLLM is now a hosted project under PyTorch Foundation GitHub - vllm-project/vllm: A high-throughput and memory-efficient inference and serving engine for LLMs, ensuring long-term sustainability and governance that enterprises require.

Enterprise Support Meets Open Innovation

Red Hat’s approach to vLLM mirrors its successful Linux/Openstack/Kubernetes strategy, taking community-driven innovation and adding enterprise-grade support, security, and operational tooling. This model provides:

No Vendor Lock-in: Organizations can deploy on-premises, in public clouds, or hybrid environments.
Transparent Development: Security vulnerabilities and bugs are publicly tracked and rapidly addressed.
Community Contributions: Features developed by one organization benefit the entire ecosystem.
Flexibility to Customize: Source code access enables modifications for specific requirements.

On top, vLLM additionally provides several strategic advantages:

Hardware Independence: Unlike TensorRT-LLM (NVIDIA-specific), vLLM supports NVIDIA GPUs, AMD CPUs and GPUs, Intel CPUs,GPUs and XPUs, PowerPC CPUs, and TPU GitHub - vllm-project/vllm: A high-throughput and memory-efficient inference and serving engine for LLMs.
Rapid Feature Adoption: vLLM V1 introduces a comprehensive re-architecture of its core components, including the scheduler, KV cache manager, worker, sampler, and API server vLLM V1: A Major Upgrade to vLLM’s Core Architecture - vLLM Blog.
Ecosystem Integration: Native compatibility with Hugging Face, OpenAI APIs, and Kubernetes ecosystems.
Cost Optimization: Freedom to choose the most cost-effective hardware for specific workloads.

Architectural Flexibility and Parallelization Strategies

Understanding Parallelization

LLMs present unique scaling challenges, for example; a 70B parameter model requires approximately 140GB of memory just for weights in FP16 precision, far exceeding single accelerator capacities. Red Hat Openshift AI (with vLLM inside) addresses such challenges through four complementary parallelization strategies, each solving different scaling challenges.

Data Parallelism (DP): Scaling Across Models

Data parallelism represents the simplest scaling pattern, running complete model replicas across multiple servers, with each processing different batches of requests. In this approach:

Maintains Full Models: Each accelerator/server holds the complete model weights.
Distributes Requests: Load balancers distribute incoming requests across replicas.
Enables Linear Scaling: Adding servers proportionally increases throughput.
Simplifies Deployment: No model sharding complexity.

       [Load Balancer]
          	    |
    -----------------------------------------
    |      	   |      	    |
[Server1] 	[Server2] 	[Server3]
[Model A] 	[Model A] 	[Model A]
    |      	   |      	    |
[Batch 1] 	[Batch 2] 	[Batch 3]

Key: Each server has complete model copy
     Different requests processed in parallel

This pattern works exceptionally well with Red Hat OpenShift AI (RHOAI)’s Model Serving capabilities based on KServe (with Replica-Sets for Model Copy Serving), enabling automatic scaling based on request load while maintaining model serving simplicity.

Pipeline Parallelism (PP): Layer-wise Distribution

Pipeline parallelism divides the model by layers, with different accelerators handling different neural network layers:

Sequential Processing: Requests flow through GPUs in sequence.
Memory Balance: Distributes memory requirements evenly.
Flexible Deployment: Can span multiple nodes without high-speed interconnects.
Micro-batching: Maintains GPU utilization through careful scheduling.

Request → [GPU1:NN-Layers 1-8] → [GPU2:NN-Layers 9-16] → [GPU3:NN-Layers 17-24] → Response
             ↓                    	↓                     	↓
          [Memory]             	[Memory]              	[Memory]
          
Timeline: Request flows sequentially through GPUs

While pipeline parallelism remains useful for certain architectures, it faces challenges with modern LLMs:

Pipeline Bubble Inefficiency: GPUs idle while waiting for data from previous stages.
Communication Overhead: Inter-stage data transfer becomes a bottleneck.
Latency Sensitivity: Each stage adds latency, impacting time-to-first-token.
Poor Fit for MoE Models: Mixture of Experts architectures with selective activation don’t map cleanly to sequential pipeline stages.

These limitations have led many production deployments to favor tensor parallelism for intra-node scaling and data parallelism for inter-node scaling, though PP still has value in specific scenarios like memory-constrained environments or when combined with other parallelization strategies.

Tensor Parallelism (TP): Distributing Model Weights

For models too large for a single accelerator, tensor parallelism splits model weights across multiple GPUs:

Horizontal Layer Splitting: Each matrix multiplication is distributed across GPUs.
Synchronized Computation: GPUs communicate via high-speed interconnects (NVLink, infinity fabric).
Memory Efficiency: Enables serving models 4-8x larger than single GPU capacity.
Low Latency: Minimal communication overhead with proper hardware.

Original Matrix Operation:
[Large Weight Matrix] × [Input] = [Output]

With Tensor Parallelism:
[Weight Part 1] → GPU1 ↘
[Weight Part 2] → GPU2 → [Combine] → [Output]
[Weight Part 3] → GPU3 ↗

Key: Single matrix operation split across GPUs
     High-speed interconnect (NVLink) required

vLLM’s tensor parallel implementation is hardware-agnostic, supporting various interconnect technologies across different accelerator types.

Expert Parallelism (EP): Distributing MoE Experts across nodes

For Mixture-of-Experts (MoE) architectures, Expert Parallelism distributes individual experts across multiple GPUs or nodes. Instead of every GPU holding all experts, each device stores only a subset, and a router layer dynamically dispatches tokens to the appropriate expert(s).

Distributed Expert Sharding: Experts are partitioned across GPUs and nodes, allowing models with hundreds of experts to scale far beyond single-device memory limits.
Dynamic Token Routing: Each token is sent only to its assigned expert(s), reducing compute overhead compared to dense model execution.
Expert Parallel Load Balancing (EPLB): Prevents “hot” experts from overloading by dynamically replicating or redistributing popular experts.
Hierarchical Scheduling: In multi-node clusters, routing and replication are coordinated first across nodes and then across GPUs within each node, ensuring even utilization and minimal inter-node traffic.
Performance Gains: Enables higher throughput and efficiency for large-scale MoE models, maintaining near-linear scaling on high-speed interconnects (NVLink, InfiniBand).

   [Router Layer]
                    		|
        ---------------------------------------------------------------
        |      		|        		|       		|
      GPU1		GPU2     		GPU3    		GPU4
    [Exp 1,2] 		[Exp 3,4] 	[Exp 5,6] 	[Exp 7,8]
    
Token Flow: Router → Selected Expert(s) → Output

vLLM’s Unified Approach

What distinguishes vLLM is its ability to combine these strategies seamlessly. vLLM supports tensor, pipeline, data and expert parallelism for distributed inference, allowing organizations to:

Mix Strategies: Use tensor parallelism within nodes and pipeline parallelism across nodes.
Adapt to Hardware: Optimize based on available interconnects and GPU memory.
Scale Dynamically: Start with single-node deployment and scale as needed.
Maintain Compatibility: Same API regardless of parallelization strategy.

This flexibility becomes critical when deploying across hybrid cloud environments where hardware configurations vary between on-premises and cloud deployments.

Hybrid TP+PP+EP+DP Deployment (MoE Model):

Pipeline Stage 1:              	Pipeline Stage 2:              Pipeline Stage 3:
Node 1 (Attention Layers)     	 Node 2 (MoE FFN Layers)       Node 3 (Output Layers)
[Attn Part A] ←→ [Attn Part B] 	[Router + Experts Subset]      [Out Part A] ←→ [Out Part B]
    GPU1           GPU2         	GPU3: Experts 1-4                  GPU5            GPU6
      ↕ NVLink ↕               	GPU4: Experts 5-8                       ↕ NVLink ↕
                                    	    ↕ NVLink ↕

Expert Distribution Detail (Node 2):
            [Router]
         GPU3  |  GPU4
        -------|-------
        Exp1   |  Exp5
        Exp2   |  Exp6  
        Exp3   |  Exp7
        Exp4   |  Exp8

Request Flow: → [Attention] → [MoE Layer/Router] → [Output] → Response
                              ↓
                    [Route to specific experts]

Parallelization Breakdown:
- Tensor Parallel (TP): Attention and Output layers split within nodes.
- Pipeline Parallel (PP): Different model stages across nodes .
- Expert Parallel (EP): MoE experts distributed across GPUs in Node 2.
- Data Parallel (DP): Entire pipeline can be replicated for more throughput.

Better Memory Management with KV-Cache

Understanding KV-Cache in LLM Inference

The KV-Cache (Key-Value Cache) represents one of the most critical features in LLM inference optimization. During attention computation, models must access previous token representations -> a process that becomes memory-intensive as sequence lengths grow.

Efficient KV-Cache management can mean the difference between serving 10 concurrent users or 100 on the same hardware at the same time, since cache reuse trades computation for memory efficiency.

(Q * K^T) * V computation process with caching

Step 1 (Prefill Phase)
                    Keys Transpose
                          ↓
    Queries              Keys              Values            Results
       |                  |                  |                  |
    [  Q  ]      ×     [  K  ]      →    [Scores]     ×    [  V  ]    →    [Output]
       |                  |                  |                  |
      64                 64                                    64              64
                          ↓                                     ↓
                    [Storing K]                           [Storing V]
                          ↓                                     ↓
                    ←── Cache memory (stores both K and V) ──→

Step N (Decode Phase)
                    Keys Transpose
                          ↓
    Query              Cached K            Cached V          Results
       |                  |                  |                  |
    [  q  ]      ×   [K₁...Kₙ]      →    [Scores]     ×   [V₁...Vₙ]   →    [output]
    (new)             (from cache)                         (from cache)       (new)
       |                  |                  |                  |
       1                 64                                    64               1
                          ↑                                     ↑
                    ←── Reading from cache (both K and V) ──→

Key Points:
- Prefill: Computes K and V for all input tokens, stores both in cache
- Decode: For each new token, reads ALL previous K and V values from cache
- Both K and V matrices must be cached (not just V)
- Cache size grows with sequence length

Prefill vs. Decode: Two Distinct Phases

LLM inference consists of two fundamentally different phases:

Prefill Phase (Prompt Processing)

Processes all input tokens in parallel.
Compute-intensive with high GPU utilization.
Generates initial KV-Cache entries for all prompt tokens.
Latency proportional to prompt length.
Benefits from larger batch sizes.

Decode Phase (Token Generation)

Generates one token at a time sequentially.
Memory-bandwidth bound operation.
Reads the entire KV-Cache for each new token.
Latency proportional to number of output tokens.
Benefits from efficient cache management.

PagedAttention: vLLM’s Memory Revolution

vLLM introduced PagedAttention, a breakthrough in KV-Cache management that treats GPU memory like virtual memory in operating systems:

Non-contiguous Storage: KV-Cache blocks can be stored anywhere in GPU memory.
Dynamic Allocation: Memory allocated only as sequences grow.
Memory Sharing: Identical prompt prefixes share KV-Cache blocks.
Near-Zero Waste: Eliminates internal fragmentation common in static allocation.

This design allows vLLM to sustain much larger batch sizes, higher concurrency, and better GPU utilization than systems that rely on static, monolithic KV-cache buffers.

Continuous Batching: Maximizing GPU Utilization

Traditional static batching waits for all sequences in a batch to complete before processing new requests. vLLM’s continuous batching:

Dynamic Request Addition: New requests join running batches between decoding steps.
Early Completion Handling: Finished sequences free resources instantly.
Optimal GPU Usage: Maintains high utilization by mixing prefill and decode operations.
Preemption Support: Can pause low-priority requests for urgent ones.

Practical Implications for Deployment

These memory management innovations translate to concrete operational benefits:

Higher Concurrency: Serve more users with the same hardware.
Better Cost Efficiency: Reduce infrastructure requirements significantly.
Improved Latency: Faster time-to-first-token through efficient scheduling.
Flexibility: Handle varying sequence lengths without reconfiguration.

The KV-Cache optimizations become even more critical with the upcoming llm-d distributed architecture, where efficient memory usage enables new deployment patterns previously impossible with traditional approaches.

Scaling with llm-d: Kubernetes-Native Distributed Inference

Beyond Single-Server Deployment

While vLLM excels as a high-performance inference engine, production deployments at scale require sophisticated orchestration and intelligent request routing. The llm-d project, launched in May 2025 by Red Hat, Google Cloud, IBM Research, NVIDIA, and CoreWeave, addresses this by providing a Kubernetes-native distributed serving stack built on top of vLLM.

llm-d is not a feature of vLLM, it’s a complementary orchestration layer. Think of it like the relationship between Linux and Kubernetes: vLLM provides the inference engine, while llm-d provides distributed orchestration and intelligent scheduling across multiple vLLM instances. The stack integrates three open-source technologies: vLLM as the high-performance inference engine, Inference Gateway for AI-aware routing, and Kubernetes for industry-standard orchestration.

In addition, KServe has added llm-d integration via a new LLMInference CRD in KServe to have a single and coherent API that unifies the serving experience across use cases and maturity levels, supporting a smoother journey into generative AI for enterprise users.

llm-d integrates three foundational open-source technologies into a unified serving stack:

vLLM - The high-performance inference engine that executes model inference.
Inference Gateway (IGW) - An official Kubernetes project extending Gateway API with AI-aware routing.
Kubernetes - The industry-standard orchestration platform for deployment and scaling.

By combining these technologies, llm-d enables organizations to deploy LLM inference at scale across hybrid cloud environments with the fastest time-to-value and competitive performance per dollar.

Three Key Innovations

Intelligent Inference Scheduling

Traditional load balancing uses simple round-robin routing, treating all servers equally. llm-d’s vLLM-aware scheduler makes intelligent decisions by routing requests to instances with matching cached prefixes, distributing load based on whether instances are handling compute-intensive prefill or memory-bound decode operations, and using real-time telemetry from vLLM to avoid overloaded instances while prioritizing low-latency paths. This intelligent routing reduces infrastructure costs by 30-50% while maintaining latency SLOs.

Disaggregated Serving

llm-d orchestrates vLLM’s native disaggregated serving (via KVConnector API) at production scale, separating prefill and decode across specialized workers:

Prefill Workers: Handle compute-intensive prompt processing on high-performance GPUs (H100s, MI300X) and scale independently based on demand.
Decode Workers: Focus on memory-bound token generation using cost-effective GPUs (A100s, L40S) and scale based on concurrent sessions.
KV-Cache Transfer: Provides efficient cache movement using NVIDIA NIXL over UCX, support for offloading to storage backends (Future Delivery), and global cache awareness across the cluster.

This allows right-sizing infrastructure: expensive GPUs only for prefill, cost-optimized hardware for serving thousands of concurrent users.

Distributed Prefix Caching

llm-d extends vLLM’s prefix caching across multiple instances with two approaches:

Local Caching: Offloading to memory/disk on each instance with zero operational cost
Shared Caching (Planned): KV transfer between instances with global indexing for cluster-wide cache awareness

Deployment Patterns

llm-d enables several advanced enterprise patterns:

Heterogeneous Hardware: Mix GPU vendors and generations based on workload—high-end GPUs for prefill, cost-optimized GPUs for decode, or CPU clusters for low-frequency requests.
Dynamic Scaling: Independently adjust prefill capacity during peak hours while maintaining steady decode capacity for active sessions, with automatic resource allocation and failover.
Geographic Distribution (Roadmap): Deploy centralized prefill workers in primary data centers with edge decode workers near users for low-latency responses.

Integration with Red Hat OpenShift AI

OpenShift AI provides enterprise packaging for llm-d with unified deployment via KServe for all components, service mesh routing between workers, full observability with pre-built dashboards, and GitOps configuration management. Enterprise security features include consistent RBAC policies, encrypted communication between workers, audit logging for distributed flows, and network policy enforcement.

Operational Benefits

Cost Optimization: 2-3x better GPU utilization, 40-60% less over-provisioning
Scalability: Independent scaling of components, proven to 100+ node deployments
Resilience: Failure isolation between phases, automatic failover, graceful degradation

Multi-Accelerator and Hybrid Cloud Support

Breaking Free from Hardware Lock-in

The rapid evolution of AI accelerators has created a diverse hardware landscape. While specialized solutions like TensorRT-LLM deliver special optimizations for NVIDIA GPUs, they create vendor lock-in that limits deployment flexibility. vLLM’s hardware-agnostic design provides freedom to choose the optimal accelerator for each use case.

Comprehensive Hardware Support

vLLM supports NVIDIA GPUs (first-class optimizations for H100, with support for every NVIDIA GPU from V100 and newer), AMD GPUs (MI200, MI300, and Radeon RX 7900 series), Google TPUs (v4, v5p, v5e, and the latest v6e), AWS Inferentia and Trainium (trn1/inf2 instances), Intel Gaudi (HPU) and GPU (XPU), and CPUs featuring support for x86, ARM, and PowerPC vLLM 2024 Retrospective and 2025 Vision - vLLM Blog.

This broad support enables several strategic advantages:

Cost Optimization

Choose AMD MI300X for price/performance on certain workloads.
Use AWS Inferentia for cost-effective inference on AWS.
Deploy on existing CPU infrastructure for low-throughput use cases.

Supply Chain Resilience

Avoid dependency on single GPU vendor availability.
Negotiate better pricing with multiple options.
Adapt to regional hardware availability.

Workload Matching

NVIDIA H100s for maximum performance.
AMD GPUs for open-source aligned deployments.
TPUs for Google Cloud deployments.
Intel Gaudi for specific enterprise agreements.

Hybrid Cloud Deployment Patterns

vLLM on Red Hat OpenShift AI enables true hybrid cloud flexibility:

On-Premises Core

Sensitive data processing on local infrastructure.
Compliance-required workloads.
Predictable capacity for baseline load.

Cloud Burst Scaling

Handle peak loads with cloud resources.
Experiment with new hardware (H100s, TPU v6e).
Geographic expansion without infrastructure investment.

Edge Inference

Deploy on edge-appropriate hardware.
CPU or smaller GPU inference.
Integrated with central management.

Unified Operations Across Environments

Red Hat OpenShift AI provides consistent operations regardless of deployment location (public vs private cloud):

Single Control Plane: Manage all deployments from a unified interface.
Consistent APIs: Same application integration across environments.
Unified Monitoring: Aggregated metrics across hybrid deployments.
Policy Enforcement: Consistent security and compliance policies.

Real-World Flexibility Examples

Financial Services Scenario

On-premises NVIDIA GPUs for sensitive data processing.
AWS Inferentia for public-facing chatbots.
CPU inference for branch edge deployments.

Healthcare Provider

AMD MI300X in private cloud for cost optimization.
Google TPUs for research workloads.
Intel CPUs for clinical decision support.

Retail Organization

Centralized GPU clusters for training and complex inference.
Edge CPU deployment in stores.
Cloud scaling for seasonal peaks.

This hardware and deployment flexibility ensures that architectural decisions made today won’t constrain options tomorrow, a critical consideration as the AI hardware landscape continues to evolve rapidly.

Model Ecosystem and Compatibility

Supporting the Entire Open-Source Model Landscape

As AI gold-run increases it’s pace, vLLM had evolved to support performant inference for > 100 model architectures: spanning nearly every prominent open-source large language model (LLM), multimodal (image, audio, video), encoder-decoder, speculative decoding, classification, embedding, and reward models vLLM 2024 Retrospective and 2025 Vision - vLLM Blog. This comprehensive support represents a fundamental advantage over specialized solutions that focus on limited model families.

Beyond Traditional LLMs

vLLM’s architecture supports diverse model types:

Language Models

Llama family (including Llama 3.1 405B).
Mistral and Mixtral MoE models.
Google’s Gemma models.
IBM’s Granite series.
Alibaba’s Qwen models.

Multimodal Models

Vision-language models (LLaVA, Qwen-VL).
Document understanding models.
Audio-language models.
Video understanding capabilities.

Specialized Architectures

Production support for state-space language models, exploring the future of non-transformer language models vLLM 2024 Retrospective and 2025 Vision - vLLM Blog.
Mixture of Experts (MoE) models.
Encoder-decoder architectures.
Embedding and reranking models.

Ease of Model Integration

Adding new models to vLLM follows a standardized process:

Model Architecture Definition: Implement using familiar PyTorch patterns.
Attention Backend Integration: Leverage existing optimized kernels.
Tokenizer Support: Direct Hugging Face compatibility.
Configuration Mapping: Standard YAML-based configuration.

This standardization means new models can often be added timely, which is critical for organizations wanting to experiment with latest models. A great example for vLLM’s agility to adapt to the changing model landscape would be introducing a support for gpt-oss, which was released August 5, 2025, and shortly after, vLLM v0.11.0 release included support for serving it in production environments.

Hugging Face Ecosystem Integration

vLLM’s native Hugging Face compatibility provides:

Direct Model Loading

Load models directly from Hugging Face Hub and/or from S3 compatible object storage backends (ex; MinIO).
Support for private model repositories.
Automatic weight conversion handling.

Tokenizer Compatibility

Use existing tokenizer implementations.
Custom tokenizer support.
Fast tokenizer optimizations.

Configuration Preservation

Respect model-specific configurations.
Support for custom model parameters.
Compatibility with fine-tuned variants.

Production Model Management

Red Hat OpenShift AI adds enterprise features for model lifecycle:

Model Registry Integration

Version control for deployed models.
A/B testing capabilities.
Rollback mechanisms.

Model Monitoring

Performance tracking per model.
Usage analytics and cost attribution.
Drift detection capabilities.

Future-Proofing Model Support

The rapid pace of model innovation requires an inference platform that can adapt:

Community Contributions

Active community adding new models.
Vendor-neutral development process.
Rapid integration of breakthrough architectures.

Flexible Architecture

Modular design supports new model paradigms.
Not tied to specific model assumptions.
Ready for post-transformer architectures.

Enterprise Validation

Red Hat’s testing and certification process.
Security scanning for model artifacts.
Performance validation across hardware.

This comprehensive model support ensures organizations can adopt new models as they emerge, without platform migrations or architectural changes a critical capability as the AI landscape continues its rapid evolution.

Enterprise Deployment with Red Hat OpenShift AI

Production-Ready from Day One

Red Hat OpenShift AI is a flexible, scalable MLOps platform with tools to build, deploy, and manage AI-enabled applications. Built using open source technologies, it provides trusted, operationally consistent capabilities for teams to experiment, serve models, and deliver innovative apps. Read more about how RHOAI is solving this challenge in: Accelerating generative AI adoption: Red Hat OpenShift AI achieves impressive results in MLPerf inference benchmarks with vLLM runtime.

KServe Integration: Intelligent Model Serving

The integration between vLLM and KServe within OpenShift AI provides enterprise-grade serving capabilities:

GenAI Features

Multi-node/multi-GPU inference with vLLM serving runtime.
Key-Value cache offloading with vLLM + LMCache integrations.
Efficient model reuse via Model Cache.
KEDA integration to allow autoscaling based on external metrics.
Rate-limiting and request routing via integration with Envoy AI Gateway
Access to llm-d capabilities via the LLMInferenceService CRD

Advanced Autoscaling

Request-based scaling for optimal resource usage.
Scale-to-zero capabilities for cost optimization.
Predictive scaling based on traffic patterns.
Multi-metric scaling (GPU utilization, queue depth, latency).

Traffic Management

Canary deployments for safe model updates.
Blue-green deployments for instant rollback.
A/B testing for model comparison.
Shadow traffic for validation.

Service Mesh Integration

End-to-end encryption with Istio.
Advanced routing and load balancing.
Circuit breaking and retry logic.
Distributed tracing for debugging.

Automated Operations

Health checking and automatic recovery.
Resource optimization recommendations.
Automated certificate management.
Log aggregation and analysis.

Security and Compliance Features

Enterprise deployments require robust security:

Access Control

RBAC integration with enterprise identity providers.
Model-level access permissions.
API key management.
Audit logging for all operations.

Data Protection

Encryption at rest and in transit.
Private endpoint options.
Network policy enforcement.
Compliance reporting tools.

Supply Chain Security

Signed container images.
SBOM (Software Bill of Materials) generation.
Vulnerability scanning.
Policy-based deployment controls.

MLPerf-Validated Performance

Red Hat, in collaboration with Supermicro, has made significant strides in addressing this challenge through the publication of impressive MLPerf inference results using Red Hat OpenShift AI with NVIDIA GPUs and the vLLM inference runtime Accelerating generative AI adoption: Red Hat OpenShift AI achieves impressive results in MLPerf inference benchmarks with vLLM runtime. These results validate:

Production-grade performance at scale.
Efficient resource utilization.
Consistent latency under load.
Multi-instance coordination capabilities.

Integrated Observability

Comprehensive monitoring without additional tooling:

Metrics and Dashboards

Pre-built Grafana dashboards for vLLM metrics.
Pre-built Grafana dashboard for request scheduler metrics driving routing decisions.
Token generation rates and latencies.
GPU utilization and memory usage.
Queue depths and rejection rates.

Alerting and Response

Automated alerts for SLA violations.
Integration with enterprise monitoring systems.
Runbook automation capabilities.
Capacity planning insights.

Cost Management and Optimization (Future Delivery)

Features designed for enterprise cost control:

Chargeback/Showback: Track usage by team or project.
Resource Quotas: Prevent runaway costs.
Spot Instance Support: Reduce costs for batch workloads.
Idle Detection: Automatically scale down unused resources.

This enterprise-grade platform transforms vLLM from a high-performance inference engine into a complete production solution, ready for mission-critical deployments.

Feature Comparison: vLLM vs TGI vs TensorRT-LLM

✓ = Full Support ◐ = Partial/Limited Support ✗ = No Support

Key Differentiators for RHOAI with vLLM:

Hardware Flexibility: Broadest accelerator support including AMD, Intel, Google TPUs, and CPUs.
Model Ecosystem: Supports >100 model architectures vs 25-40 for alternatives.
Distributed Architecture: Upcoming llm-d enables disaggregated prefill/decode and system wide kv-cache routing for distributed optimal scaling.
Enterprise Integration: Native Red Hat OpenShift AI support with KServe autoscaling.
Memory Efficiency: Advanced PagedAttention and KV-Cache management.
Open Development: PyTorch Foundation project with rapid community innovation.

Making the Right Choice

Count your “Yes” responses across all categories:

0-2 “Yes” responses: Consider specialized solutions if they meet your specific needs.
3-5 “Yes” responses: vLLM provides significant advantages for your use case.
6+ “Yes” responses: vLLM on Red Hat OpenShift AI is the strategic choice.

Each decision point addresses fundamental architectural constraints:

Flexibility Requirements determine if you need a hardware-agnostic solution.
Operational Complexity evaluates if simplified operations justify open-source adoption.
Model & Innovation assesses if rapid evolution demands an adaptable platform.
Long-term Sustainability considers total cost of ownership and strategic risk.

Strategic Recommendation

For organizations answering “Yes” to multiple questions above, vLLM on Red Hat OpenShift AI delivers:

Maximum Flexibility: Deploy anywhere, on any supported hardware.
Operational Excellence: Enterprise-grade platform with minimal complexity.
Future-Readiness: Support for emerging models and architectures.
Cost Optimization: Choose optimal hardware for each workload.
Risk Mitigation: Open-source foundation with enterprise support.

The convergence of these factors makes vLLM the strategic choice for organizations building sustainable AI infrastructure.

Action Steps for Adoption

Immediate

Deploy vLLM on Red Hat OpenShift AI in the development environment.
Test with your specific models and workloads.
Validate hardware flexibility with available accelerators.

Short-term

Implement Autoscaling with KServe.
Establish monitoring and observability.
Train team on operational procedures.

Medium-term

Plan production deployment architecture.
Implement hybrid cloud patterns if needed.
Prepare for llm-d distributed architecture.

Long-term

Optimize costs through hardware selection.
Implement advanced deployment patterns.
Contribute improvements back to the community.

Conclusion

The choice of LLM inference platform represents a strategic commitment that will impact your organization’s AI capabilities for years to come.

Our analysis demonstrates that vLLM on Red Hat OpenShift AI uniquely addresses the three critical requirements for enterprise LLM deployment:

Flexibility: Deploy on any hardware (NVIDIA, AMD, Intel, TPUs) across hybrid clouds.
Scalability: Advanced memory management and upcoming llm-d architecture enable 10-100x better resource utilization.
Sustainability: Open-source foundation with enterprise support eliminates vendor lock-in.

While TensorRT-LLM offers NVIDIA-specific optimizations and TGI provides Hugging Face integration, only vLLM delivers the architectural flexibility required for a rapidly evolving AI landscape. With support for > 100 model architectures, hardware-agnostic design, and the backing of both PyTorch Foundation -> RHOAI with vLLM provides the most robust foundation for long-term success.