28 Optimal GPU Allocation for Your Hospital’s Mixed Hardware
Great diagram! Let me show you how to optimally arrange your heterogeneous GPU fleet across the three pools, considering the specific strengths of each GPU type.
28.1 GPU Characteristics Overview
GPU Performance Matrix for Hospital Workloads:
┌───────────────────────────────────────────────────────┐
│ GPU Model │ VRAM │ Strength │ Best Use Case │
├────────────┼──────┼─────────────────┼─────────────────┤
│ V100 │ 16/32│ CNN compute │ Medical imaging │
│ A100 │ 40/80│ Training power │ Large models │
│ L40S │ 48GB │ Inference speed │ LLM serving │
│ RTX 6000 │ 48GB │ Versatility │ Dev/Research │
└───────────────────────────────────────────────────────┘28.2 Optimized GPU Assignment Strategy
Hospital AI Infrastructure - GPU Allocation
Hospital Network
│
AI Orchestrator
(K8s Control)
│
┌──────────────────────┼──────────────────────┐
▼ ▼ ▼
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ REAL-TIME │ │ BATCH INFERENCE │ │ TRAINING POOL │
│ INFERENCE │ │ POOL │ │ │
├─────────────────┤ ├─────────────────┤ ├─────────────────┤
│ │ │ │ │ │
│ Priority Order: │ │ Priority Order: │ │ Priority Order: │
│ │ │ │ │ │
│ 1. L40S (Best) │ │ 1. RTX 6000 Ada │ │ 1. A100 (Best) │
│ ↓ │ │ ↓ │ │ ↓ │
│ 2. RTX 6000 Ada │ │ 2. L40S │ │ 2. V100 │
│ ↓ │ │ ↓ │ │ ↓ │
│ 3. A100* │ │ 3. V100 │ │ 3. L40S* │
│ ↓ │ │ ↓ │ │ ↓ │
│ 4. V100 │ │ 4. A100* │ │ 4. RTX 6000* │
│ │ │ │ │ │
│ *Wasteful │ │ *Overkill │ │ *Suboptimal │
└─────────────────┘ └─────────────────┘ └─────────────────┘28.3 Task-Specific GPU Assignments
28.3.1 Real-time Inference Pool (LLM Chatbot)
Optimal Configuration:
┌──────────────────────────────────────┐
│ Real-time LLM Inference Pool │
├──────────────────────────────────────┤
│ Server 1: L40S - Main LLM serving │
│ Server 2: L40S - Load balancing │
│ Server 3: RTX 6000 - Overflow/Backup │
└──────────────────────────────────────┘
Why this arrangement:
- L40S: Optimized INT8 inference, 48GB for 13B models
- RTX 6000: Good backup, similar VRAM
- Keep A100 for training (waste for inference)28.3.2 Batch Inference Pool (Research/Data Extraction)
Optimal Configuration:
┌──────────────────────────────────────┐
│ Batch Processing Pool │
├──────────────────────────────────────┤
│ Primary: RTX 6000 Ada │
│ - Structural data extraction │
│ - Research batch jobs │
│ │
│ Secondary: V100 (32GB) │
│ - CNN inference for radiology │
│ - Smaller batch LLM jobs │
└──────────────────────────────────────┘
Why this arrangement:
- RTX 6000: Great price/performance for batch
- V100: Excellent for medical imaging CNNs28.3.3 Training Pool
Optimal Configuration:
┌──────────────────────────────────────┐
│ Training Pool │
├──────────────────────────────────────┤
│ Primary: A100 (80GB) │
│ - LLM fine-tuning │
│ - Large model training │
│ │
│ Secondary: V100 (16/32GB) │
│ - CNN training (medical imaging) │
│ - Smaller model experiments │
└──────────────────────────────────────┘
Why this arrangement:
- A100: Superior for training (FP16, larger memory)
- V100: Still excellent for CNN training28.4 LLM vs CNN Workload Differences
Workload Characteristics Comparison:
┌─────────────────────────────────────────────────────┐
│ LLM vs CNN Requirements │
├──────────────┬──────────────────────────────────────┤
│ │ LLM │ CNN │
├──────────────┼─────────────────────┼────────────────┤
│ INFERENCE │ │ │
├──────────────┼─────────────────────┼────────────────┤
│ Memory │ HIGH (7-70GB) │ LOW (2-8GB) │
│ Compute │ Memory-bound │ Compute-bound │
│ Batch Size │ Small (1-16) │ Large (32-256) │
│ Latency │ Sequential tokens │ Single pass │
│ Optimization │ KV-cache, INT8 │ TensorRT, FP16 │
├──────────────┼─────────────────────┼────────────────┤
│ TRAINING │ │ │
├──────────────┼─────────────────────┼────────────────┤
│ Memory │ VERY HIGH │ MODERATE │
│ Pattern │ Attention (O(n²)) │ Convolution │
│ Data Loading │ Text (small) │ Images (large) │
│ I/O Needs │ Low │ HIGH │
└──────────────┴─────────────────────┴────────────────┘28.5 Practical Implementation for Your Hospital
28.5.1 Phase 1: Immediate Setup
Current Hardware Allocation:
┌────────────────────────────────────────────────────┐
│ REAL-TIME INFERENCE (Chatbot) │
├────────────────────────────────────────────────────┤
│ ┌──────────┐ │
│ │ L40S │ → LLaMA 13B or Mistral 7B │
│ └──────────┘ (vLLM with PagedAttention) │
│ │
│ ┌──────────┐ │
│ │ RTX 6000 │ → Backup/Overflow server │
│ └──────────┘ (Also serves smaller models) │
└────────────────────────────────────────────────────┘
┌────────────────────────────────────────────────────┐
│ BATCH INFERENCE (Research) │
├────────────────────────────────────────────────────┤
│ ┌──────────┐ │
│ │ RTX 6000 │ → Structured extraction jobs │
│ └──────────┘ (Can queue 100s of documents) │
│ │
│ ┌──────────┐ │
│ │ V100 │ → Radiology CNN inference │
│ └──────────┘ (Chest X-ray, CT analysis) │
└────────────────────────────────────────────────────┘
┌────────────────────────────────────────────────────┐
│ TRAINING POOL │
├────────────────────────────────────────────────────┤
│ ┌──────────┐ │
│ │ A100 │ → LLM fine-tuning (LoRA/QLoRA) │
│ └──────────┘ Medical report generation models │
│ │
│ ┌──────────┐ │
│ │ V100 │ → CNN training (DenseNet, ResNet) │
│ └──────────┘ for medical imaging tasks │
└────────────────────────────────────────────────────┘28.5.2 Workload-Specific Optimizations
For Radiology AI (CNN-heavy):
CNN Pipeline Optimization:
DICOM → V100 (Segmentation) → V100 (Classification)
↓ ↓
Store RTX 6000 (Report) ← LLM Generation
Hardware Matching:
- V100: Excellent CNN performance (5,120 CUDA cores)
- Keep for imaging tasks, not LLM
- Use TensorRT for 2-3x speedupFor LLM Tasks:
LLM Deployment Strategy:
Model Size → GPU Assignment:
├─ 7B Models → RTX 6000/L40S (INT8)
├─ 13B Models → L40S (INT8/FP16)
├─ 30B Models → A100 (80GB)
└─ 70B Models → 2x A100 (Tensor Parallel)28.6 Dynamic Workload Scheduling
# Kubernetes GPU Scheduling Configuration
# /etc/kubernetes/gpu-scheduler.yaml
Scheduling Priority Matrix:
┌──────────────────────────────────────────┐
│ IF task == "realtime_llm": │
│ prefer: ["L40S", "RTX6000"] │
│ avoid: ["A100"] # Too valuable │
│ │
│ IF task == "batch_extraction": │
│ prefer: ["RTX6000", "V100"] │
│ flexible: true # Can wait │
│ │
│ IF task == "training": │
│ require: ["A100"] if model_size > 7B │
│ prefer: ["V100"] if task == "CNN" │
│ │
│ IF task == "medical_imaging": │
│ prefer: ["V100"] # Optimized kernels │
└──────────────────────────────────────────┘28.7 Cost Efficiency Analysis
GPU Utilization Targets:
┌─────────────────────────────────────────────┐
│ GPU │ Task │ Target │ Cost/hour │
├────────┼──────────────┼─────────┼───────────┤
│ A100 │ Training │ 90-95% │ $$$$$ │
│ L40S │ RT Inference │ 60-80% │ $$$ │
│ RTX6000│ Batch │ 70-90% │ $$ │
│ V100 │ CNN/Backup │ 50-70% │ $$ │
└────────┴──────────────┴─────────┴───────────┘
Efficiency Rule:
- Never use A100 for simple inference
- Never use V100 for large LLMs (memory limited)
- Prioritize L40S for customer-facing services28.8 Migration Path for Mixed Workloads
When to Move GPUs Between Pools:
Dynamic Reallocation Triggers:
Morning (8-11 AM): High clinical load
→ Move RTX 6000 to inference pool
Afternoon (2-5 PM): Research time
→ Move RTX 6000 to batch pool
Night (10 PM-6 AM): Training window
→ Move L40S to training pool for distributed training
Weekend: Low clinical load
→ Maximize training capacity28.9 Specific Recommendations
- Keep A100 dedicated to training - It’s your most valuable asset
- Use V100 for CNN workloads - Still excellent for medical imaging
- L40S for production LLM - Best inference performance
- RTX 6000 as swing capacity - Move between pools as needed