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Executive Summary — GPU Resource Optimization for LLM Workloads

Enable the company to run large language model (LLM) training and inference more efficiently by optimizing GPU resource utilization and allocation across its multi-GPU compute cluster.

This initiative reduces operating costs and accelerates deployment of AI products.

Business Problem

Current LLM workloads consume significant GPU resources, but:

  • GPU utilization averages 40–55%, leaving expensive hardware under-used.

  • Inference workloads experience latency variability due to inefficient GPU scheduling.

  • Training jobs wait in queue because of poor resource packing and fragmentation.

  • Increasing demand for model fine-tuning and experimentation is driving costs upward.

The organization needs a scalable way to maximize throughput on existing infrastructure before investing in more GPUs.

Solution Overview

Implement an optimization layer that improves how workloads utilize GPUs through:

Profiling & Measurement

  • Increasing demand for model fine-tuning and experimentation is driving costs upward.

Autoscaling & Observability

  • Metrics-driven autoscaling, cost visibility, GPU utilization dashboards

LLM Model & Runtime Optimization

  • Mixed precision (FP16/BF16), DeepSpeed ZeRO, activation checkpointing

  • Quantization + batching for inference

  • TensorRT / ONNX Runtime compilation where applicable

Cluster Scheduling Optimization

  • Topology-aware placement (NVLink local grouping)

  • Fractional GPU allocation (MIG/MPS)

  • Gang scheduling to eliminate idle resource blocking

Expected Outcomes

Metric (KPI)

Baseline

Target After Optimization

Average GPU utilization

Cost per training epoch / inference batch

Model throughput (tokens/sec)

Training job wait time

40–55%

 

N/A

 

Baseline

 

High

 

>75%

 

–20% to –40%

 

+30% to 200%

 

Reduced by 25%+

 

Our Process

Deliverables

1

2

3

4

Optimization Blueprint & PoC

Model/resource allocation strategies

DeepSpeed & scheduling configuration sets

Monitoring Dashboards

Live GPU utilization and per-job metrics (Prometheus + Grafana)

Production Rollout Package

Reference architecture

Runbook + training for ML & Ops teams

Executive ROI Analysis

Cost savings based on cluster utilization improvements

1. Optimization Blueprint & PoC

Model/resource allocation strategies

DeepSpeed & scheduling configuration sets

2. Monitoring Dashboards

Live GPU utilization and per-job metrics (Prometheus + Grafana)

3. Production Rollout Package

Reference architecture

Runbook + training for ML & Ops teams

4. Executive ROI Analysis

Cost savings based on cluster utilization improvements

Our Timeline

Timeline (12 Weeks)

Phase

Duration

Output

Baseline profiling

Scheduling & GPU allocation PoC

Model optimizations (training & inference)

Autoscaling + observability

Validation + rollout plan

Weeks 1–2

 

Weeks 3–5

 

Weeks 6–8

 

Weeks 9–10

 

Weeks 11–12

 

Utilization report + dashboard

 

Efficient packing & resource sharing

 

Throughput + cost improvements

 

Metrics-driven orchestration

 

Final report & adoption roadmap

 

Financial Impact

For an enterprise GPU cluster (example: 32 × A100 GPUs), improving utilization from ~50% to >75% yields:

≈ $1.2M annual savings

(based on reduced GPU-hour consumption and deferred hardware expansion)

Stakeholder Ask

Approve Phase 1 (profiling & PoC).

No architecture changes or hardware purchases required.

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