Infrastructure built to serve large language models in production.
Serving LLMs reliably at scale requires more than a GPU — it requires the right inference engine, batching strategy, autoscaling policy and monitoring stack. GPUMarket designs, deploys and operates LLM inference infrastructure around engines like vLLM and NVIDIA NIM.
What this involves
- Continuous batching and request scheduling
- Tensor and pipeline parallelism across GPUs
- Latency vs. throughput trade-offs
- GPU memory management for long-context workloads
- Autoscaling based on request volume
- Kubernetes-based deployment and rollout
Common challenges we help solve
Choosing the right GPU and memory footprint for your model
We size GPU type and count based on your model, target latency and expected concurrency.
Handling variable and bursty traffic without over-provisioning
We configure autoscaling policies so GPU capacity tracks real demand.
Keeping inference costs predictable as usage grows
We combine reserved and on-demand capacity to balance cost and headroom.
Recommended GPUs
Related managed software
Frequently asked questions
Which inference engine should we use?
It depends on your model and requirements. vLLM offers flexibility for open-weight models; NVIDIA NIM offers pre-optimized containers for supported models. We help evaluate the trade-offs for your specific workload.
Can you help us reduce inference latency?
Yes. Latency optimization typically involves GPU selection, batching configuration, parallelism strategy and network placement — all areas our infrastructure engineers can review and tune.