Scaling LLM Production Systems
Client: Enterprise AI Platform (Ishtar AI Case Study)
The Challenge
Scaling LLM deployments requires sophisticated techniques beyond simple horizontal scaling. GPU partitioning, distributed inference, batching optimization, and capacity planning are critical for cost-effective scaling while maintaining latency SLOs. Traditional scaling approaches don't account for LLM-specific constraints.
Our Solution
We implemented a comprehensive scaling solution with GPU partitioning for multi-tenancy, distributed inference using tensor and pipeline parallelism, dynamic batching optimization, and data-driven capacity planning. The system includes SLO budget management and automated scaling based on demand patterns.
Results
Achieved 3x throughput improvement, reduced cost per query by 45%, and maintained 99.9% latency SLO compliance even during peak demand periods.
Measurement Period: 6 months post-deployment
Methodology: Performance metrics tracking and cost analysis
Time-to-Value
Total Duration: 7 weeks
- Kickoff: Week 1
- Architecture Review:
- Build Complete:
- Pilot Deployment:
- Production Rollout: Week 7
Architecture & Scope
Components Deployed
- GPU partitioning system
- Distributed inference engine
- Tensor parallelism implementation
- Pipeline parallelism setup
- Dynamic batching optimizer
- Capacity planning system
- SLO budget manager
Integration Points
- Kubernetes GPU operators
- Model serving frameworks (vLLM/TGI)
- Monitoring systems
- Autoscaling controllers
Risk & Controls Implemented
Audit Trails
Complete scaling decision logging and capacity planning records
Permission Models
RBAC for scaling configuration and capacity changes
Evaluation Harnesses
Automated performance testing and SLO validation
Compliance Controls
Scaling controls aligned with operational requirements
Artifacts
Screenshots
Sample Outputs
Scaling architecture diagrams and performance analysis reports
Advanced Large Language Model Operations
Springer Nature, March 2026
Chapter: Chapter 6: Scaling Up LLM Deployments
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