GenAI Workload Optimizer
Optimize AI model placement and resource allocation for gaming workloads
1. Select Workload Scenario
Real-Time NPC Responses
Instant AI-driven character interactions during gameplay
CCU Impact: 80%Real-Time
Interactive Storytelling
Dynamic narrative generation based on player choices
CCU Impact: 60%Background
Procedural World Generation
AI-powered environment and content creation
CCU Impact: 20%Background
Mixed AI Workload
Combination of real-time and background AI tasks
CCU Impact: 40%Real-Time
2. AI Placement Matrix - Speed vs Intelligence
Response Time
Simple AI
Moderate AI
Complex AI
Genius AI
Immediate
<100ms
Llama 2 7B
7B
VRAM: 14GB | RAM: 8GB
Mistral 7B
7B
VRAM: 14GB | RAM: 8GB
Phi-3 Mini
3.8B
VRAM: 8GB | RAM: 4GB
Interactive
<2s
Background
<30s
3. Configuration
Number of concurrent users to support
4. Selected AI Models
Llama 2 7B
simpleFast responses for basic NPC dialogue
Parameters: 7B
VRAM: 14GB
RAM: 8GB
Response: immediate
5. Resource Optimization Analysis
Resource Requirements
HYDRA Advantage
Unified Memory Architecture
HYDRA's 192GB unified memory eliminates the traditional GPU memory bottleneck, allowing multiple AI models to run simultaneously without memory swapping.
- • No GPU memory fragmentation
- • Dynamic memory allocation
- • Zero memory transfer overhead
- • Seamless model switching
Performance Benefits
Traditional Setup
Memory swapping
GPU switching
Complex orchestration
HYDRA
Instant access
Parallel processing
Simplified management
6. AI Placement Options
7. Multiple AI Task Planning
Simultaneous AI Workloads
Traditional Approach
Sequential processing
Memory bottlenecks
Context switching overhead
HYDRA Approach
Parallel processing
Unified memory access
Zero context switching
Example: MMO Game Server
Running multiple AI models simultaneously for different game systems:
Real-Time NPCs
Llama 2 7B × 4 instances
56GB VRAM
Quest Generation
Mixtral 8x7B × 2 instances
96GB VRAM
World Building
Llama 2 70B × 1 instance
140GB VRAM
Total: 292GB VRAM Required
Traditional: Requires 12+ RTX 4090s ($24,000+)
HYDRA: Single instance with room to spare ($8,000)
8. AI Optimization Principles
Memory Hierarchy
VRAM (GPU Memory)
Fastest access, limited capacity
System RAM
High capacity, PCIe bottleneck
HYDRA Unified Memory
Best of both worlds
Performance Trade-offs
Model Size vs Speed
Larger models provide better quality but slower inference
Batch Size vs Latency
Higher batch sizes improve throughput but increase latency
Precision vs Memory
Lower precision (FP16, INT8) saves memory but may affect quality