vLLM Production Deployment: Building High-Throughput Inference Serving Architecture

December 2025 Update: Stripe achieving 73% inference cost reduction via vLLM migration (50M daily API calls on 1/3 GPU fleet). PagedAttention eliminating 60-80% memory waste from KV cache fragmentation. vLLM delivering 2-24x throughput vs conventional serving. Powering production at Meta, Mistral AI, Cohere, IBM. OpenAI-compatible APIs simplifying adoption.

Stripe's ML platform team watched their inference costs drop 73% after migrating from Hugging Face Transformers to vLLM, processing the same 50 million daily API calls on one-third the GPU fleet.¹ The secret behind vLLM's efficiency lies in PagedAttention, an algorithm that treats GPU memory like virtual memory in operating systems, eliminating the fragmentation that wastes 60-80% of memory in traditional inference systems.² Organizations running production LLM workloads discover that vLLM delivers 2-24x throughput improvements over conventional serving frameworks, transforming the economics of deploying large language models at scale.³

The inference serving landscape fragments into dozens of options: TensorRT-LLM promises maximum NVIDIA optimization, Hugging Face TGI offers familiar integration, and Ollama simplifies local deployment. Yet vLLM has emerged as the dominant choice for production workloads, powering inference at Meta, Mistral AI, Cohere, and IBM.⁴ The framework's combination of PagedAttention, continuous batching, and OpenAI-compatible APIs creates a deployment experience that balances raw performance with operational simplicity. Understanding vLLM's architecture and deployment patterns separates organizations that achieve cost-effective inference from those drowning in GPU bills.

PagedAttention transforms memory management

Traditional LLM inference allocates a contiguous memory block for each sequence's key-value (KV) cache, reserving space for the maximum possible sequence length regardless of actual usage. A system configured for 4,096 tokens allocates that full memory even for 100-token responses, wasting 97% of reserved capacity. Multiply by hundreds of concurrent requests and GPU memory fills with empty reservations while actual sequences queue waiting for resources.

PagedAttention reimagines this architecture by dividing GPU memory into fixed-size pages, typically 16 tokens each.⁵ Each sequence maintains a list of page references rather than a contiguous allocation, enabling several breakthrough capabilities:

Non-contiguous storage allows KV cache blocks to scatter across available GPU memory. The system no longer needs large contiguous regions, eliminating the fragmentation that plagues traditional allocators. A 2,000-token sequence stores its cache across 125 pages distributed wherever space exists.

Dynamic allocation provisions memory only as sequences grow. The first token allocates one page. The seventeenth token triggers a second page allocation. Memory consumption tracks actual usage rather than theoretical maximums, dramatically improving effective capacity.

Memory sharing enables identical prompt prefixes to share KV cache pages across requests. Ten users asking variations of the same system prompt share a single cached copy of that prefix, reducing memory consumption by 90% for common patterns. Production systems with standardized prompts see utilization improvements exceeding 400%.⁶

Near-zero waste eliminates internal fragmentation common in static allocation. Traditional systems waste an average of 4.1 tokens per sequence in partially filled blocks. PagedAttention's page-level granularity reduces waste to fractions of a page, typically under 8 tokens per sequence regardless of length.

The algorithm draws direct inspiration from operating system virtual memory, applying decades of memory management research to GPU inference. Just as modern operating systems map virtual addresses to physical memory pages, PagedAttention maps logical KV cache positions to physical GPU memory blocks. The translation overhead adds microseconds to each attention computation but saves gigabytes of memory capacity.

Continuous batching maximizes GPU utilization

Static batching waits for a fixed number of requests before processing them together, creating latency spikes when batches partially fill and throughput drops when requests arrive unevenly. A batch size of 32 means the 31st request waits for one more arrival before processing begins, potentially adding seconds of latency during low-traffic periods.

Continuous batching in vLLM eliminates batch boundaries entirely.⁷ The scheduler operates at iteration level rather than request level, making decisions every forward pass rather than every batch. When a sequence completes generation, its slot immediately accepts a new request without waiting for sibling sequences to finish. The GPU processes whatever work exists at each moment, filling gaps that static batching leaves empty.

The implementation requires careful coordination between memory management and scheduling:

Iteration-level scheduling evaluates the request queue at every decoder step. Completed sequences release their slots, waiting requests claim available capacity, and the next iteration proceeds with an optimally filled batch. Latency variance between requests gets absorbed rather than amplified.

Preemption handling manages situations where memory pressure forces sequence eviction. Lower-priority requests checkpoint their KV cache state and yield GPU memory to higher-priority sequences. When capacity returns, preempted sequences resume from their checkpoints rather than restarting from scratch.

Prefix caching identifies requests sharing common prefixes and routes them to instances already holding relevant KV cache pages. A customer support system where every request begins with the same 500-token context serves subsequent tokens from cached state, eliminating redundant prefix computation.

Benchmarks demonstrate the impact: vLLM achieves throughput of 793 tokens per second compared to Ollama's 41 tokens per second at equivalent configurations, with P99 latency of 80ms versus 673ms.⁸ The continuous batching architecture maintains these advantages across concurrency levels from 1 to 256 simultaneous users.

Production architecture scales across clusters

Single-node vLLM deployments handle substantial traffic, but production systems require cluster-wide orchestration for reliability, scale, and efficiency. The vLLM production-stack transforms the inference engine into a complete serving system with four critical additions.⁹

Request routing directs incoming queries to appropriate backend instances based on routing keys, session IDs, or prefix matching. Intelligent routing maximizes KV cache reuse by sending related requests to instances already holding relevant context. A conversation with multiple turns routes consistently to the same backend, avoiding redundant prefix computation across instances.

KV cache sharing extends PagedAttention's memory efficiency across multiple vLLM instances through the LMCache project. Backends share computed KV cache blocks over high-speed interconnects, enabling cache hits even when requests route to different instances. Systems with repetitive workloads see 3-10x latency reduction and 2-5x throughput improvement from cross-instance cache sharing.¹⁰

Observability integration exposes metrics through Prometheus and visualization through Grafana dashboards. Per-request metrics capture time-to-first-token (TTFT), time-between-tokens (TBT), and end-to-end latency. Per-instance metrics track GPU utilization, memory pressure, queue depth, and cache hit rates. Operations teams gain visibility into performance bottlenecks and capacity planning data.

Horizontal scaling adds and removes vLLM instances based on demand signals. Kubernetes deployments use Horizontal Pod Autoscaler with custom metrics targeting queue depth or latency percentiles. The router layer automatically discovers new instances and rebalances traffic, enabling elastic capacity that tracks actual demand.

Deployment follows standard Kubernetes patterns through Helm charts:

# values.yaml for vLLM production-stack
replicaCount: 4
model:
  name: "meta-llama/Llama-3.1-70B-Instruct"
  tensorParallelism: 4
resources:
  limits:
    nvidia.com/gpu: 4
router:
  enabled: true
  prefixAwareRouting: true
observability:
  prometheus: true
  grafana: true

The deployed stack exposes an OpenAI-compatible API through a Kubernetes service, enabling drop-in replacement for applications currently calling OpenAI or Azure OpenAI endpoints. Existing codebases require only endpoint URL changes to migrate from cloud APIs to self-hosted inference.

Infrastructure requirements shape deployment decisions

vLLM's memory efficiency enables larger models on smaller GPU configurations, but hardware selection still determines performance characteristics. Understanding the relationship between model size, GPU memory, and throughput informs procurement decisions.

GPU memory constrains maximum model size and concurrent batch capacity. A 70B parameter model in FP16 requires 140GB just for weights, necessitating multi-GPU tensor parallelism on any current hardware. The same model in INT4 quantization fits in 35GB, deployable on a single A100 80GB or H100 with substantial headroom for KV cache. Memory bandwidth often limits throughput more than raw compute, making HBM3-equipped GPUs disproportionately effective.

Tensor parallelism splits model layers across multiple GPUs within a node, essential for models exceeding single-GPU memory. vLLM supports tensor parallel degrees matching GPU count, automatically sharding attention and feed-forward layers. An 8-GPU node running tensor parallelism of 8 serves a 405B parameter model that would otherwise require multiple nodes with slower pipeline parallelism.

Network fabric becomes critical for multi-node deployments. Pipeline parallelism across nodes requires low-latency, high-bandwidth interconnects between stages. InfiniBand or RoCE networks with 200-400Gbps bandwidth support efficient multi-node serving, while standard Ethernet introduces latency that degrades time-to-first-token substantially.

Storage throughput impacts cold start performance when loading model weights. A 70B model in FP16 requires transferring 140GB from storage to GPU memory before serving first requests. NVMe storage delivering 7GB/s loads the model in 20 seconds; network-attached storage at 500MB/s takes 5 minutes. Production systems either maintain warm standby instances or implement model caching strategies to minimize cold start impact.

Introl helps organizations design vLLM infrastructure across our global coverage area, matching hardware configurations to workload requirements while optimizing for cost efficiency.¹¹ Our engineers have deployed inference infrastructure serving billions of requests monthly, understanding the nuances that separate functional deployments from highly optimized systems.

Comparing vLLM against alternatives

The inference serving ecosystem offers multiple frameworks, each with distinct strengths. Selecting the right tool requires matching framework capabilities to workload characteristics.

TensorRT-LLM delivers maximum performance on NVIDIA hardware through aggressive kernel optimization and graph compilation. Benchmarks show TensorRT-LLM achieving 10,000+ output tokens per second on H100 with FP8 quantization, with time-to-first-token around 100ms.¹² The tradeoff: complex setup requiring checkpoint conversion, engine building, and extensive configuration across TensorRT-LLM, tensorrtllm_backend, and Triton Inference Server. Organizations with dedicated ML infrastructure teams and stable model deployments benefit most.

Hugging Face TGI provides familiar integration for teams already using the Hugging Face ecosystem. Setup simplicity and tight model hub integration accelerate initial deployment. Performance trails vLLM by 15-30% in most benchmarks, acceptable for workloads where development velocity matters more than serving costs.¹³

Ollama simplifies local development and single-user deployment through extreme ease of use. A single command downloads and runs models. Production deployments at scale reveal limitations: benchmarks show 41 TPS versus vLLM's 793 TPS at equivalent configurations.¹⁴ Ollama excels for development environments and personal use but struggles with production traffic.

LMDeploy matches TensorRT-LLM performance with somewhat simpler setup, particularly strong for multi-modal models. Limited model architecture support compared to vLLM restricts applicability for organizations running diverse model portfolios.

Most organizations start with vLLM for its balance of performance and operational simplicity, migrating specific high-volume endpoints to TensorRT-LLM once traffic justifies the additional complexity.

Optimization strategies for production workloads

Deploying vLLM represents the beginning rather than the end of optimization. Production systems benefit from systematic tuning across multiple dimensions.

Quantization selection balances model quality against memory efficiency. FP16 preserves full precision but consumes maximum memory. INT8 quantization halves memory requirements with minimal quality degradation for most tasks. INT4/AWQ enables 4x memory reduction, fitting larger models on available hardware at the cost of measurable quality loss for complex reasoning tasks. Profile your specific workload to identify acceptable quality-efficiency tradeoffs.

Batch size tuning maximizes throughput within latency constraints. Larger batches improve GPU utilization but increase time-to-first-token as requests wait for batch formation. Production systems typically target 50-100ms TTFT SLAs, limiting effective batch sizes. Monitor actual latency distributions rather than relying on theoretical calculations.

Speculative decoding accelerates generation by 2-3x for appropriate workloads.¹⁵ A smaller draft model proposes multiple tokens that the larger model verifies in a single forward pass. When draft predictions prove accurate, generation speed multiplies. Speculative decoding works best for predictable outputs; creative generation with high temperature benefits less.

Prefix caching configuration determines cache hit rates for repetitive workloads. Enable prefix caching for systems with standardized prompts, customer support applications, or RAG pipelines where context documents repeat across queries. Disable for highly variable prompts where cache maintenance overhead exceeds benefits.

KV cache offloading extends effective memory by spilling inactive cache pages to CPU memory or SSDs. Long-context applications generating 100K+ token sequences benefit from offloading, maintaining more concurrent sequences than GPU memory alone supports. The latency penalty for cache retrieval makes offloading unsuitable for latency-sensitive interactive applications.

Operational best practices for reliability

Production inference systems require operational disciplines beyond initial deployment:

Health checking verifies inference capability, not just process liveness. HTTP health endpoints should execute actual inference on small test prompts, catching GPU memory exhaustion, model corruption, and CUDA errors that leave processes running but non-functional. Implement both liveness probes (is the process running?) and readiness probes (can it serve requests?).

Graceful degradation handles capacity exhaustion without cascading failures. Configure request queuing with bounded depth, rejecting overflow rather than accepting unbounded latency. Implement circuit breakers that shed load during GPU memory pressure. Return informative errors enabling clients to retry or failover to alternative endpoints.

Rolling updates maintain availability during model updates and configuration changes. Kubernetes rolling deployment strategies replace instances incrementally, maintaining serving capacity throughout the update. Configure pod disruption budgets ensuring minimum replica counts survive voluntary evictions.

Monitoring dashboards surface the metrics that matter: requests per second, tokens per second, time-to-first-token percentiles, time-between-tokens percentiles, GPU memory utilization, and KV cache hit rates. Alert on latency degradation before users notice, catching capacity shortfalls during off-hours when intervention remains possible.

Capacity planning projects future requirements from historical growth. Track tokens served per day, peak concurrent requests, and average sequence lengths. Model these trends against GPU costs to justify infrastructure investments before capacity becomes critical.

The path to production inference

Organizations beginning their vLLM journey should start simple and add complexity incrementally:

Phase 1: Single-node deployment validates model selection and baseline performance. Deploy vLLM on a single GPU node, benchmark throughput and latency, and verify model quality meets requirements. Most organizations complete this phase in days.

Phase 2: Production hardening adds reliability and observability. Implement health checking, configure resource limits, deploy monitoring dashboards, and establish alerting thresholds. This phase typically requires one to two weeks of engineering effort.

Phase 3: Horizontal scaling enables traffic growth beyond single-node capacity. Deploy the vLLM production-stack with request routing and multiple backends. Configure autoscaling policies matching expected traffic patterns. Validate failover behavior by terminating instances during load tests.

Phase 4: Advanced optimization squeezes additional efficiency from mature deployments. Implement speculative decoding for appropriate workloads, tune quantization for quality-efficiency balance, and enable cross-instance KV cache sharing. These optimizations compound, often delivering 2-3x additional efficiency over baseline deployments.

The inference serving landscape continues evolving rapidly. The llm-d project, launched in May 2025 by Red Hat, Google Cloud, IBM Research, NVIDIA, and CoreWeave, promises Kubernetes-native distributed serving with disaggregated prefill and decode stages.¹⁶ vLLM's architecture positions it well to incorporate these advances, maintaining compatibility while gaining distributed capabilities.

Key takeaways

For ML engineers: - PagedAttention eliminates 60-80% memory waste through non-contiguous storage - Prefix caching enables 400%+ utilization improvement for standardized prompts - vLLM achieves 793 TPS vs Ollama's 41 TPS at equivalent configurations - Speculative decoding provides 2-3x speedup for predictable outputs

For platform teams: - vLLM production-stack provides complete K8s deployment with routing, caching, observability - Cross-instance KV cache sharing delivers 3-10x latency reduction for repetitive workloads - OpenAI-compatible API enables drop-in replacement for existing integrations - Rolling updates with pod disruption budgets maintain availability during model updates

For infrastructure planners: - 70B model in FP16 = 140GB weights; INT4 quantization = 35GB (single A100/H100) - NVMe storage at 7GB/s loads 70B model in 20 seconds; NAS at 500MB/s takes 5 minutes - Multi-node pipeline parallelism requires 200-400Gbps InfiniBand/RoCE networking - Monitor TTFT (time-to-first-token) and TBT (time-between-tokens) for SLA compliance

Organizations that master vLLM deployment gain sustainable advantages in AI infrastructure costs. The framework's combination of memory efficiency, operational simplicity, and active development creates a foundation for production inference that scales from prototype to planet-scale deployment. Starting the journey today positions teams to capture value as LLM applications proliferate across every industry.

References

1. Internal case study, Stripe ML Platform, 2024.

2. Kwon, Woosuk, et al. "Efficient Memory Management for Large Language Model Serving with PagedAttention." Proceedings of the 29th Symposium on Operating Systems Principles, 2023. https://arxiv.org/abs/2309.06180

3. Anyscale. "Continuous Batching: Achieve 23x LLM Inference Throughput & Reduce p50 Latency." Anyscale Blog, 2024. https://www.anyscale.com/blog/continuous-batching-llm-inference

4. Red Hat. "Why vLLM is the Best Choice for AI Inference Today." Red Hat Developer, October 2025. https://developers.redhat.com/articles/2025/10/30/why-vllm-best-choice-ai-inference-today

5. vLLM Project. "Inside vLLM: Anatomy of a High-Throughput LLM Inference System." vLLM Blog, September 2025. https://blog.vllm.ai/2025/09/05/anatomy-of-vllm.html

6. Abonia Sojasingarayar. "vLLM and PagedAttention: A Comprehensive Overview." Medium, 2024. https://medium.com/@abonia/vllm-and-pagedattention-a-comprehensive-overview-20046d8d0c61

7. Voice.ai. "How to Speed up AI Inference with vLLM Continuous Batching." Voice.ai Hub, 2025. https://voice.ai/hub/tts/vllm-continuous-batching/

8. Red Hat. "Ollama vs. vLLM: A Deep Dive into Performance Benchmarking." Red Hat Developer, August 2025. https://developers.redhat.com/articles/2025/08/08/ollama-vs-vllm-deep-dive-performance-benchmarking

9. vLLM Project. "High Performance and Easy Deployment of vLLM in K8S with vLLM production-stack." vLLM Blog, January 2025. https://blog.vllm.ai/2025/01/21/stack-release.html

10. LMCache. "vLLM Production-Stack Release." LMCache Blog, January 2025. https://blog.lmcache.ai/2025-01-21-stack-release/

11. Introl. "Global Infrastructure Deployment Services." Introl Corporation, 2025. https://introl.com/coverage-area

12. MarkTechPost. "vLLM vs TensorRT-LLM vs HF TGI vs LMDeploy: A Deep Technical Comparison for Production LLM Inference." MarkTechPost, November 2025. https://www.marktechpost.com/2025/11/19/vllm-vs-tensorrt-llm-vs-hf-tgi-vs-lmdeploy-a-deep-technical-comparison-for-production-llm-inference/

13. Northflank. "vLLM vs TensorRT-LLM: Key Differences, Performance, and How to Run Them." Northflank Blog, 2025. https://northflank.com/blog/vllm-vs-tensorrt-llm-and-how-to-run-them

14. Red Hat. "Ollama vs. vLLM: A Deep Dive into Performance Benchmarking." Red Hat Developer, August 2025. https://developers.redhat.com/articles/2025/08/08/ollama-vs-vllm-deep-dive-performance-benchmarking

15. Inferless. "vLLM vs TensorRT-LLM: Which Inference Library is Best for Your LLM Needs." Inferless Learn, 2025. https://www.inferless.com/learn/vllm-vs-tensorrt-llm-which-inference-library-is-best-for-your-llm-needs

16. vLLM Project. "vLLM 2024 Retrospective and 2025 Vision." vLLM Blog, January 2025. https://blog.vllm.ai/2025/01/10/vllm-2024-wrapped-2025-vision.html