FinOps Agents & Dynamic GPU Cluster Cost Optimization

In organizations running high-throughput LLM workloads, GPU compute (NVIDIA H100, A100, L40S) frequently accounts for over 65% of total cloud infrastructure spending. Static replica provisioning leads to extreme capital waste: clusters sized for peak traffic sit under-utilized during off-peak hours, incurring thousands of dollars in idle VRAM costs.

In this guide, we implement an autonomous FinOps AI Agent in TypeScript that monitors NVIDIA DCGM GPU telemetry, auto-scales vLLM inference worker nodes, and orchestrates spot instance migrations.

Tip: Use dynamic KV-cache occupancy and active token throughput metrics from vLLM rather than static container CPU/RAM utilization to drive GPU horizontal pod autoscaling.

1. FinOps GPU Optimization Lifecycle

2. TypeScript FinOps GPU Efficiency Evaluator with Zod

Below is the production TypeScript evaluator querying NVIDIA DCGM metrics:

typescript
import { z } from "zod";

export const NodeTelemetrySchema = z.object({
  nodeId: z.string(),
  gpuModel: z.enum(["NVIDIA_H100", "NVIDIA_A100", "NVIDIA_L40S", "NVIDIA_T4"]),
  activeGpuUtilPercent: z.number().min(0).max(100),
  vramUsedGigabytes: z.number(),
  vramTotalGigabytes: z.number(),
  hourlyOnDemandRateUSD: z.number(),
  isSpot: z.boolean(),
});

export type NodeTelemetry = z.infer<typeof NodeTelemetrySchema>;

export interface OptimizationReport {
  totalHourlyBurnUSD: number;
  estimatedWastedHourlyUSD: number;
  monthlySavingsProjectedUSD: number;
  recommendations: Array<{ nodeId: string; action: string; justification: string }>;
}

export function evaluateClusterEfficiency(nodes: NodeTelemetry[]): OptimizationReport {
  let totalHourly = 0;
  let wastedHourly = 0;
  const recommendations: Array<{ nodeId: string; action: string; justification: string }> = [];

  for (const node of nodes) {
    totalHourly += node.hourlyOnDemandRateUSD;
    const vramOccupancy = (node.vramUsedGigabytes / node.vramTotalGigabytes) * 100;

    // Condition: Node is under-utilized across both compute and VRAM
    if (node.activeGpuUtilPercent < 20 && vramOccupancy < 30) {
      const wastedPortion = node.hourlyOnDemandRateUSD * (1 - node.activeGpuUtilPercent / 100);
      wastedHourly += wastedPortion;

      recommendations.push({
        nodeId: node.nodeId,
        action: "DRAIN_AND_CONSOLIDATE",
        justification: `GPU compute (${node.activeGpuUtilPercent}%) and VRAM (${vramOccupancy.toFixed(1)}%) are critically low. Evict pods to neighboring nodes.`,
      });
    }
  }

  return {
    totalHourlyBurnUSD: totalHourly,
    estimatedWastedHourlyUSD: wastedHourly,
    monthlySavingsProjectedUSD: wastedHourly * 24 * 30,
    recommendations,
  };
}

3. GPU Instance Optimization Matrix

GPU ModelVRAM (GB)On-Demand ($/hr)Spot ($/hr)Workload FitSpot Savings Potential
NVIDIA H100 SXM80 GB$8.20 / hr$2.45 / hrLarge-scale pre-training & 70B+ FP8 Serving~70% via Spot
NVIDIA A100 SXM80 GB$4.10 / hr$1.25 / hrStandard vLLM / Ollama 8B–32B Serving~69% via Spot
NVIDIA L40S48 GB$2.10 / hr$0.75 / hrRAG Embeddings & Small Distill Inference~64% via Auto-scale

Key Takeaways

  • KV-Cache Driven Scaling: Scaling LLM clusters on memory buffer occupancy rather than CPU utilization avoids false-positive autoscaling triggers.
  • Spot Workload Orchestration: Running fault-tolerant batch inference pipelines on spot instances cuts GPU infrastructure bills by up to 70%.
  • Continuous Bin-Packing: Automating workload consolidation ensures under-utilized GPU nodes are drained and decommissioned promptly.

Continue to Part 7: Production Guardrails & Telemetry Evals →