Challenge
Production vLLM deployment optimization using AWQ quantization, KV cache optimization, and intelligent request batching with priority queues
Technical Stack
Python
vLLM
CUDA
AWQ
FastAPI
Redis
Kubernetes
Prometheus
Architecture Patterns
Model Quantization
Dynamic Batching
GPU Optimization
Request Prioritization
Performance Impact
Before vs After Metrics
cost per 1k tokens
↓60.0%
Before
$0.025
After
$0.010
avg latency
↓25.0%
Before
2.4s
After
1.8s
gpu utilization
↑73.3%
Before
45%
After
78%
requests per gpu
↑133.3%
Before
12/min
After
28/min
Business Impact
Business Impact Summary
$45k
Business Value
Quantified impact
92/100
Impact Score
Excellent
Key Outcomes Achieved
60% GPU cost reduction ($45k/month savings)
92/100 impact score with quantified performance gains
25% faster inference speed with optimized batching
78% GPU utilization vs 45% baseline
ROI Analysis
Value Created: $45k
Impact Rating: 92/100 (Exceptional impact)
Evidence-Based: All metrics verified through production systems
Technical Implementation
Detailed technical content and code examples are rendered from the MDX file. This includes architecture diagrams, code snippets, and step-by-step implementation details.
Evidence & Verification
Evidence & Verification
Visual Evidence
gpu-utilization
cost-comparison
Screenshots, architecture diagrams, and performance charts from production systems
Verified Implementation
All metrics and evidence are sourced from production systems and actual GitHub repositories. This case study represents real-world implementation with measurable business outcomes.