LLM Inference on Kubernetes

Kubernetes LLM Inference Job Support — Serve Models That Meet Latency, Cost & Scale

Serving LLMs in production on Kubernetes is a performance-engineering problem. Real-time support across vLLM, KServe, Ray Serve, NVIDIA Dynamo, SGLang, TensorRT-LLM, and NIM.

Throughput that collapses under concurrency, TTFT that misses SLA, KV cache that exhausts GPU memory, autoscaling that reacts too slowly, and cost that only makes sense at impossible utilisation — production LLM serving is where most AI platforms struggle.

This hub covers production LLM inference on Kubernetes end to end: choosing and tuning the serving engine (vLLM for broad coverage and continuous batching, KServe/LLMInferenceService for Kubernetes-native governance, Ray Serve for Python-native composition, NVIDIA Dynamo for datacenter-scale disaggregated serving with KV-aware routing, SGLang, TensorRT-LLM, and NVIDIA NIM), continuous batching and KV/prefix caching, tensor/pipeline parallelism and multi-node serving, autoscaling on the right signals, inference gateways for KV-cache-aware routing, and the operational metrics that actually matter — TTFT, TPOT, tokens/sec, queue latency, GPU and KV-cache utilisation, cold starts, and cost per request/token. We tune for your latency and cost budget, not a benchmark.

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What We Offer

Expert Support for Every IT Challenge

From daily job support to emergency production fixes, proxy interview guidance, and interview coaching — we have the expert for your specific need.

Real-Time Kubernetes AI Job Support

Live expert help during your working hours — running LLM inference (vLLM, KServe, Dynamo), agent runtimes and sandboxes, GPU scheduling, autoscaling, RAG pipelines, and daily platform deliverables on your real cluster so you always hit your deadlines.

Production AI Incident Support

On-call firefighting for live incidents — GPU Pods stuck Pending, CUDA/OOMKilled crashes, vLLM out-of-memory, high TTFT, model-loading failures, autoscaling that will not scale, agent loops, MCP authorization errors, and RAG/vector-DB latency — with an engineer on the call.

Interview & Candidate Marketing

Kubernetes AI proxy interview assistance, profile positioning, and candidate marketing for Platform Engineer, AI Infrastructure Engineer, GPU Infrastructure Engineer, MLOps/LLMOps, and SRE roles — real-time interview guidance, recruiter readiness, and profile visibility.

Real Situations

LLM Inference Work We Help With

These are the real-world situations our experts resolve every day — for job support and interview assistance.

Choosing and tuning vLLM, KServe, Ray Serve, Dynamo, SGLang, TensorRT-LLM, or NIM for a real workload

Continuous batching and KV/prefix-cache tuning to lift throughput without blowing TTFT

Tensor/pipeline parallelism and multi-node serving for models that do not fit one GPU

Disaggregated prefill/decode and KV-aware routing with NVIDIA Dynamo

Autoscaling inference on the right signals, including scale-to-zero with cold-start mitigation

Fixing TTFT spikes, KV-cache OOM, and GPU underutilisation against a cost budget

Global Reach

Real-time Kubernetes AI infrastructure support for engineers across USA, Canada, UK, Ireland, Germany, Netherlands, Switzerland, Australia, New Zealand, Singapore, UAE, and worldwide.

Available across US, Canada, UK, European, Australian, and Asia-Pacific business hours — and 24/7 for production incidents.

In-house experts — no sub-contracting or outsourcing

24/7 availability for urgent job support and interview needs

Confidential & professional — NDA available on request

Same-day onboarding for most job support and interview cases

Combined job support + proxy interview service available

Expert Help Available

Need real-time IT job support or interview help? Our experts are available 24/7 — USA, Canada, UK, Europe & worldwide.

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FAQ

Frequently Asked Questions

Everything you need to know before getting started with job support or interview assistance.

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It depends on your constraints. vLLM has the broadest feature coverage (continuous batching, PagedAttention KV cache, wide model and quantization support) and is a strong default. KServe (with LLMInferenceService) adds Kubernetes-native governance and a standard serving CRD. Ray Serve suits Python-native, multi-step composition. NVIDIA Dynamo targets datacenter-scale disaggregated serving with KV-aware routing. SGLang, TensorRT-LLM, and NIM each have sweet spots. We help you pick and tune for your model, latency, and cost budget rather than defaulting to hype.

The ones that map to user experience and cost: time-to-first-token (TTFT), time-per-output-token (TPOT), tokens/sec throughput, queue latency, concurrency, GPU utilisation and memory, KV-cache utilisation and hit ratio, batch size, cold-start time, and cost per request and per token. We instrument these before tuning so changes are measured, not guessed.

Yes. We help with tensor and pipeline parallelism for models that do not fit one GPU, multi-node serving with the right interconnect and topology, and disaggregated prefill/decode (separating the compute-bound prefill phase from the memory-bound decode phase) — including NVIDIA Dynamo with NIXL transfer and KV-aware routing. We also cover the gang-scheduling needed so multi-node serving does not deadlock.

We find the real bottleneck from metrics and traces — KV-cache pressure, batch and concurrency settings, GPU memory fragmentation, prefix-cache misses, model/quantization choice, or scheduling — then tune batching, cache, parallelism, and autoscaling, and right-size resources and probes. For OOM we address KV-cache limits, max-model-len, GPU memory headroom, and quantization, not just bigger GPUs.

Message us on WhatsApp with your models, GPUs, serving engine, and the SLO or incident you are facing. We will start from your metrics, find the bottleneck, and tune it with you — same-day.

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Real developers. Real solutions. Job support and proxy interview assistance available 24/7 across USA, Canada, UK, Europe, Australia, Germany, Singapore, and New Zealand.