vLLM Operations Competence Center Switzerland
High-throughput LLM inference with vLLM, deployed and operated on Swiss infrastructure by VSHN. Our engineers configure PagedAttention, GPU scheduling, and autoscaling on Kubernetes so your models serve more requests on less hardware - with full Swiss data residency. Part of VSHN's LLM Operations practice.
PagedAttention Memory Management
Use vLLM's PagedAttention technology for optimal GPU memory utilisation during inference. VSHN deploys and tunes vLLM on Kubernetes so your models achieve up to 23x higher throughput compared to naive serving approaches, reducing infrastructure costs while serving more concurrent requests on the same GPU hardware.
OpenAI-Compatible API Gateway
Serve open-source models like Llama, Mistral, Falcon, Apertus, and Qwen through vLLM's OpenAI-compatible API endpoint. VSHN configures production-grade API gateways with authentication, rate limiting, and load balancing so your applications can switch between model providers without code changes - all hosted on Swiss infrastructure with full audit logging.
GPU Scheduling and Orchestration
Run vLLM inference workloads with optimised GPU scheduling on Kubernetes and OpenShift. VSHN configures NVIDIA device plugins, resource quotas, and pod priority classes so your inference pods get the GPU time they need while batch training jobs run on preemptible resources to optimise cost.
Model Serving at Scale
Scale vLLM deployments horizontally across multiple GPU nodes with automated replica management. VSHN engineers horizontal pod autoscaling based on request queue depth and latency targets, continuous batching configuration, and tensor parallelism across GPUs for large models that exceed single-GPU memory.
Swiss Data Residency
LLM inference, model weights, and request logs stay in Swiss data centers. VSHN operates on Exoscale, Cloudscale, and other Swiss cloud providers, ensuring full GDPR compliance and data residency for organizations that cannot afford to send sensitive prompts and completions to hyperscaler regions outside Switzerland. Learn more in our sovereignty assessment.
Observability and Performance Tuning
Monitor vLLM inference latency, throughput, token generation rates, and GPU utilisation across your entire serving fleet. VSHN integrates Prometheus, Grafana, and custom dashboards into your platform so you always know what your models cost to run, where bottlenecks are, and when to scale up or down.
vLLM FAQ
What platforms does VSHN support for vLLM workloads?
VSHN deploys and operates vLLM workloads on APPUiO (our managed Kubernetes platform), Red Hat OpenShift, enterprise private cloud infrastructure, and sovereign cloud partners. All platforms run on Swiss or European data centers and are backed by up to 99.99% uptime SLA. We help you choose the right platform based on your compliance, performance, and budget requirements.
Which cloud providers are available for vLLM deployments?
VSHN operates on multiple Swiss cloud providers including Exoscale and Cloudscale, as well as European sovereign cloud partners. For organizations that need GPU-accelerated workloads, we work with providers offering GPU instances in Swiss data centers on public and private cloud. All infrastructure is managed under a single SLA with 24/7 support from our operations team.
How does vLLM improve inference performance?
vLLM uses PagedAttention to manage GPU memory efficiently, achieving up to 23x higher throughput than naive HuggingFace serving. It supports continuous batching, tensor parallelism, and speculative decoding. VSHN tunes these parameters for your specific models and hardware on Kubernetes, ensuring optimal tokens-per-second rates while keeping latency within your target thresholds.
How does VSHN scope and quote vLLM consulting engagements?
Every engagement starts with a free architecture consultation where we assess your model serving needs, GPU requirements, and compliance constraints. VSHN then delivers a written scope document with a fixed-price or time-and-materials quote in CHF. Typical engagements cover cluster design, vLLM deployment, observability setup with Prometheus and Grafana, and backup automation for model artefacts and configuration data. Model weights alone can be tens of GB, so we size storage accordingly. There is no commitment at the scoping stage.
Which models can I serve with vLLM?
vLLM supports a wide range of open-source models including Llama, Mistral, Falcon, Qwen, Apertus, and many more transformer-based architectures. Apertus, the Swiss AI foundation model, is Apache 2.0 licensed and EU AI Act Art 53 compliant, with full training data and code transparency. VSHN provides Kubernetes-native serving infrastructure with automated model loading, health checks, and rolling updates. We help you select and optimize models for your use case while ensuring all inference stays within Swiss data centers.
How does VSHN ensure data sovereignty for vLLM workloads?
All infrastructure runs in Swiss data centers operated by Swiss or European sovereign cloud providers. Model weights, input prompts, generated completions, and inference logs never leave the chosen jurisdiction. All operational access is from Switzerland-based engineers, and we provide audit trails for compliance reporting. See our sovereignty assessment for details on how VSHN scores against the EU Cloud Sovereignty Framework.
Can VSHN integrate vLLM with existing AI pipelines?
Yes. vLLM exposes an OpenAI-compatible API, so existing applications using OpenAI client libraries can switch to self-hosted models without code changes. VSHN also integrates vLLM with LiteLLM gateways, retrieval-augmented generation pipelines, and managed PostgreSQL with pgvector for vector storage - with automated backups and up to 99.99% SLA as all our VSHN-operated databases.
What monitoring and observability does VSHN provide for vLLM?
VSHN integrates Prometheus and Grafana into every managed platform, with custom dashboards for vLLM-specific metrics: inference latency (p50, p95, p99), tokens per second, GPU utilisation, queue depth, and estimated cost per request. Alerting rules notify your team and our 24/7 operations center when metrics breach thresholds, so performance issues are caught before they affect users.
Do I need a dedicated GPU to get started with vLLM?
No. VSHN offers both shared and dedicated GPU options for vLLM inference. If your workload does not yet justify a full dedicated GPU, we can deploy your models on shared GPU infrastructure where you pay for the compute you actually use. As your request volume grows, we migrate you to dedicated GPUs with reserved capacity and guaranteed latency targets. No application changes required. This lets you validate your use case on production-grade infrastructure without over-committing on hardware from day one.
How do I get started with VSHN's vLLM consulting?
Contact us through the form below for a free initial consultation. We assess your current model serving needs, platform requirements, and compliance constraints, then propose an architecture running on APPUiO, OpenShift, or your preferred infrastructure. vLLM consulting is part of VSHN's broader LLM Operations practice -- see llmops.ch for the full picture.
Can agencies use VSHN's vLLM services for client AI projects?
Yes. Agencies and AI consultancies use VSHN to provide vLLM inference infrastructure for their clients. VSHN provisions GPU-equipped clusters, deploys vLLM with your chosen models, and operates the stack 24/7 on Swiss cloud. Each client project runs on isolated infrastructure so there is no cross-client data exposure. Your team focuses on model selection and application integration while VSHN handles the infrastructure operations and scaling.
Book a vLLM consultation
Tell us about your LLM inference requirements. VSHN provides a free initial consultation covering vLLM architecture, GPU sizing, and a scoped proposal for your deployment on Swiss infrastructure.
Book a free callOr send us a message