vLLM consulting and hands-on support

vLLM is an open-source inference and serving engine for large language models. It uses techniques such as continuous batching and PagedAttention to schedule concurrent requests and manage GPU memory more efficiently than a simple one-request-at-a-time serving process. Its OpenAI-compatible HTTP API can help teams integrate model inference with existing applications and service tooling.

Platform engineering, MLOps, and SRE teams commonly deploy vLLM on GPU-backed hosts or Kubernetes to provide internal or customer-facing model APIs. Production work typically includes selecting model and quantization settings, configuring parallelism and request limits, instrumenting latency and token throughput, securing the endpoint, and defining runbooks for capacity changes, model upgrades, and degraded GPU performance.

• Assess model size, context length, concurrency, GPU memory, and latency requirements before choosing a vLLM deployment topology.
• Configure continuous batching, maximum concurrent sequences, token limits, and request timeouts to keep queueing behavior predictable under load.
• Deploy vLLM as a managed service with health checks, controlled rollouts, authentication, network restrictions, and secret management.
• Monitor request latency, queue time, token throughput, GPU utilization, memory consumption, errors, and rejected or timed-out requests.
• Use load testing and capacity planning to determine GPU counts, replica targets, autoscaling signals, and acceptable performance during traffic spikes.
• Automate model, container, and configuration changes through CI/CD or GitOps, with rollback procedures and validation for tokenizer, model, and runtime compatibility.

Teams use vLLM when they need a production inference layer that serves large language models efficiently under variable, concurrent workloads.

• Higher GPU utilization: Continuous batching adds new requests as capacity becomes available instead of waiting for one batch to finish, which helps keep GPUs productive when request arrivals vary.
• More efficient KV-cache management: PagedAttention manages attention key-value cache memory in smaller blocks, reducing wasted GPU memory caused by different request lengths and improving concurrency within a fixed GPU allocation.
• Predictable request serving: Teams can configure limits for concurrent sequences, maximum model length, GPU memory utilization, and batching behavior so operators can balance latency, throughput, and capacity deliberately.
• Practical API integration: vLLM provides an OpenAI-compatible serving interface, which can reduce application changes when teams move an existing client or internal service to self-hosted model inference.
• Scalable deployment patterns: Operators can run separate vLLM instances for different models, GPU pools, or workload classes, making it easier to isolate resource-intensive models and scale capacity according to demand.
• Clearer cost control: Measuring request volume, token usage, generation latency, queue time, GPU memory, and GPU utilization gives teams the data needed to select model replicas and GPU capacity based on actual workload behavior.
• Better day-two operations: Teams can place vLLM behind standard health checks, metrics collection, centralized logging, deployment automation, and controlled model rollouts, creating a repeatable operating process for upgrades and incident response.

Our practical experience with vLLM helps clients build and operate reliable large language model inference services with clearer control over GPU utilization, request scheduling, deployment safety, observability, and day-2 operations. We work with engineering teams to assess workload and model requirements, design production-ready serving architectures, and establish operating practices that support predictable performance and responsible cost control.

Some of the things we did include:

• Assessing model sizes, request patterns, context lengths, concurrency, latency targets, and GPU capacity to determine whether vLLM fits the intended inference workload.
• Designing reference architectures for vLLM deployments, including model storage, API exposure, GPU scheduling, scaling boundaries, and separation between development, staging, and production environments.
• Automating infrastructure and deployment configuration with infrastructure as code, container images, CI/CD pipelines, or GitOps workflows, with versioned model and runtime settings.
• Tuning continuous batching, maximum sequence lengths, concurrency limits, tensor parallelism, and GPU memory settings while testing throughput, queue time, time to first token, and generation latency.
• Adding observability for request rates, queue depth, token throughput, latency percentiles, GPU utilization, GPU memory pressure, worker failures, and model-loading errors.
• Applying security and governance controls for model access, API authentication, secrets, network exposure, image provenance, environment separation, and auditable configuration changes.
• Creating runbooks for model rollouts, vLLM upgrades, rollback procedures, capacity checks, incident response, and routine GPU cost reviews, then transferring the operating knowledge to the client’s team.

Some of the things we can help you do with vLLM include:

• Assess your current inference workloads: Review model sizes, request patterns, latency and throughput requirements, GPU capacity, deployment configuration, and operational constraints, then deliver a findings report with bottlenecks, risks, and prioritized recommendations.
• Define a vLLM serving architecture: Design the deployment model, request routing, model storage, GPU allocation, replica strategy, batching behavior, and scaling approach for development, staging, and production environments.
• Implement vLLM inference services: Configure model serving endpoints, tokenizer and model settings, concurrency limits, request timeouts, tensor parallelism where appropriate, and integration with your existing application or platform services.
• Automate deployment and configuration: Create repeatable infrastructure and deployment workflows for vLLM, including environment-specific configuration, model version management, secrets handling, health checks, and rollback procedures.
• Build CI/CD or GitOps workflows: Automate image creation, configuration validation, model deployment, smoke tests, promotion between environments, and controlled rollbacks through your existing delivery process.
• Improve security and governance: Define access controls, network boundaries, authentication requirements, tenant isolation, model artifact handling, audit records, and policies for approved models and inference workloads.
• Implement inference observability: Instrument request latency, token throughput, queue time, error rates, GPU utilization, memory consumption, batch behavior, and model-specific health signals, with dashboards and actionable alerts.
• Improve reliability and GPU efficiency: Tune continuous batching, concurrency, maximum sequence length, GPU memory utilization, request scheduling, replica counts, and autoscaling thresholds against representative workloads and service-level objectives.
• Support upgrades, migrations, and day-2 operations: Plan vLLM and model upgrades, test compatibility and performance changes, document runbooks, troubleshoot production incidents, review capacity and cost trends, and establish repeatable maintenance procedures.