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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.

Our Global Offices

We work across all time zones 24X7.

United States

5423 Quebec Common,
Fremont, CA 94555, USA

India

Calcutta Greens, Santoshpur,
Kolkata, West Bengal 700099

United Arab Emirates

Samia by Azizi, Al Furjan,
Dubai, United Arab Emirates

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