llm_production.throughput_increase = 2.5× increase (from 400 req/s to 1,000 req/s)
llm_production.latency_95th = ≤ 100 ms
llm_production.gpu_utilization = 78 %
llm_production.cost_reduction = ≈ 30 % lower GPU‑hour spend using preemptible nodes
I refined my cost‑efficient AI agent strategy by focusing on four levers: token auditing, continuous/in‑flight batching, aggressive KV‑caching, and smart routing of simple queries to smaller models.
Audit every token – tokens drive both API spend and wall‑clock; “token‑efficient” techniques.
Exploit continuous or in‑flight batching – shifting from static to continuous batching with vLLM gives more throughput while reducing median latency.
Cache aggressively – KV‑caching and paged attention trim first‑token latency and GPU‑hours, especially in long‑context or multi‑turn agents.
Route smart, not big – routers like RouteLLM send trivial queries to small models while retaining GPT‑4‑level quality.
vLLM is a CNCF‑backed open‑source project that implements KV‑caching and PagedAttention for highly efficient LLM inference.
I will demonstrate these capabilities live at KubeCon India 2025 in Hyderabad, showing reduced GPU memory usage and higher request‑per‑second throughput.
The upcoming demo at KubeCon India 2025 in Hyderabad will showcase vLLM’s KV‑caching and PagedAttention reducing GPU memory footprint and increasing RPS.
Quick guide for the startups looking to scale their AI inferencing system.
- EKS + Fargate for zero‑maintenance cluster compute.
- Amazon Q for infrastructure recommendations and faster debugging of configuration drifts.
- CloudWatch + OpenTelemetry for end‑to‑end observability.
- json_token_count ≈ 800 tokens
- toon_token_count ≈ 530 tokens
- token_savings = 35% fewer tokens
- toon_token_count ≈ 530 tokens
- token_savings = 35% fewer tokens
I built a battle‑tested playbook for scaling GenAI inference on Kubernetes at Welzin, combining vLLM, Ray, KEDA, Karpenter, and observability tooling.
llm_inference.large_model_vram_requirement = 800GB+ VRAM
llm_inference.tokens_per_second = >10k tokens/sec on GKE with A100 GPUs
llm_scaling.pod_burst_range = 10 to 1000+ pods
llm_cost.spot_instance_savings = ≈30 %