We serve large language models in production — fast, reliably, and on your own infrastructure.
High-throughput LLM serving with vLLM
vLLM is the engine behind most production-grade, open-weight model deployments. We configure and tune it for high throughput and low latency: continuous batching, paged attention (KV cache), tensor parallelism across GPUs, and quantisation (AWQ, GPTQ) where it pays off.
- Self-hosted inference: Run open-weight models (Llama, Mistral, Qwen, and more) in your own environment — no data leaves your control.
- GPU-accelerated serving: Tensor-parallel and pipeline-parallel serving across NVIDIA GPUs for larger models.
- Throughput & latency tuning: Batch sizing, scheduling, and cache tuning to meet your SLOs at the lowest GPU cost.
- Serving infrastructure: Kubernetes deployment, autoscaling, and observability around your serving layer.
Why self-host instead of a managed API?
- Data sovereignty & compliance: Keep prompts and outputs in-region (GDPR, EU AI Act, Swedish regulations).
- Cost control: Own the GPUs and avoid per-token margins at scale — break-even typically at 50M+ tokens/month.
- Model control: Pin versions, fine-tune, and switch models without vendor lock-in.
- Latency determinism: No noisy neighbors; predictable tail latency for user-facing applications.
Our delivery model
- Architecture & sizing — Model selection, GPU sizing, and serving topology (single-node vs multi-node tensor parallel).
- Deployment & hardening — vLLM on Kubernetes with GPU Operators, health checks, rolling updates, and HPA/VPA.
- Benchmarking & tuning — Token throughput, TTFT, and inter-token latency profiling across batch sizes and concurrency levels.
- Operations handover — Runbooks, alerting on SLOs (latency, error rate, queue depth), and cost dashboards.
Need help with LLM serving?
We provide consultancy and hands-on assistance — from architecture and benchmarking to production deployment. Talk to an engineer.