Workload-Specific Optimization

Tailoring Your Setup

Different deep learning workloads have different requirements. This section helps you optimize your system for your specific use case.

Why Workload Optimization Matters

A system optimized for computer vision may waste resources on NLP tasks, and vice versa. Understanding your workload helps you:

Maximize performance - 20-50% faster training
Reduce costs - Don’t overspend on unnecessary hardware
Avoid bottlenecks - Identify limiting factors early
Scale efficiently - Know when to add GPUs vs RAM vs storage

Workload Categories

Computer Vision

Image classification
Object detection (YOLO, Faster R-CNN)
Semantic segmentation
Instance segmentation
Image generation (Stable Diffusion, GANs)

Characteristics:

Heavy GPU memory usage
Data loading bottlenecks common
Benefits from fast storage
Multi-GPU scales well

Natural Language Processing

Language models (BERT, GPT)
Fine-tuning LLMs
Text classification
Machine translation
Text generation

Characteristics:

Long sequence lengths = high memory
Attention mechanism compute-heavy
Model parallelism often needed
Less data loading overhead

Reinforcement Learning

Game playing (Atari, Go)
Robotics simulation
Optimization problems
Multi-agent systems

Characteristics:

High CPU usage for simulation
GPU for policy networks
Memory for replay buffers
Asynchronous training common

Multi-GPU Training

Large models that don’t fit on one GPU
Faster training via parallelism
Data parallel vs model parallel
Distributed training across nodes

Characteristics:

Network bandwidth critical
Synchronization overhead
Memory management complex
Scaling efficiency varies

Setup Multi-GPU Training →

Hardware Recommendations by Workload

Computer Vision

Component	Recommendation	Why
GPU	High VRAM (24GB+)	Large batches, high-res images
CPU	8-16 cores	Data augmentation
RAM	32-64GB	Dataset caching
Storage	Fast NVMe SSD	Loading images quickly

Recommended systems: See TensorRigs Systems

NLP/LLMs

Component	Recommendation	Why
GPU	Maximum VRAM possible	Long sequences, large models
CPU	16+ cores (if multi-GPU)	Less critical than CV
RAM	64-128GB	Model weights in RAM
Storage	Moderate SSD	Datasets smaller than CV

Reinforcement Learning

Component	Recommendation	Why
GPU	Mid-range is fine	Policy networks smaller
CPU	High core count	Parallel environments
RAM	32-64GB	Replay buffers
Storage	Standard SSD	Minimal data loading

Multi-GPU Training

Component	Recommendation	Why
GPU	Multiple identical GPUs	Balanced communication
CPU	PCIe lanes important	GPU bandwidth
RAM	32GB per GPU	Proportional scaling
Storage	Fast shared storage	Parallel data access

Quick Decision Guide

I’m working on:

Image Classification

Small datasets (ImageNet-size): Single GPU (RTX 4090, RTX 4080)
Large datasets (100M+ images): Multi-GPU setup
High resolution (512x512+): 24GB+ VRAM

→ See GPU Memory Management

Object Detection (YOLO, etc.)

Real-time inference: Optimize for FP16/INT8
Training: High VRAM for large batch sizes
Small objects: Higher resolution = more VRAM

→ See Training Optimization

Language Model Fine-tuning

Small models (under 1B params): Single GPU
Medium models (7B params): 24GB+ GPU
Large models (70B+ params): Multi-GPU + model parallelism

→ See Multi-GPU Guide

From-Scratch LLM Training

Small scale: Multi-GPU workstation
Production scale: Cluster required

→ Multi-GPU Guide

Reinforcement Learning

Simple envs (Atari): Moderate GPU + good CPU
Complex envs (robotics): High CPU count
Multi-agent: Consider distributed setup

→ See HPC Integration for distributed setups

Software Optimization by Workload

Libraries & Frameworks

Computer Vision:

# Optimized for CV
- PyTorch + torchvision + albumentations
- NVIDIA DALI for data loading
- Mixed precision training (AMP)
- Efficient data augmentation

NLP:

# Optimized for NLP
- Hugging Face Transformers
- DeepSpeed / FSDP for large models
- Flash Attention for long contexts
- Gradient checkpointing

Reinforcement Learning:

# Optimized for RL
- Stable Baselines3
- Ray/RLlib for distributed
- Fast simulators (MuJoCo, Isaac Gym)
- Vectorized environments

Benchmarking Your Workload

Before committing to a setup:

Run representative experiments
- Use actual model architectures
- Use realistic dataset sizes
- Measure end-to-end training time
Identify bottlenecks
- Monitor GPU utilization
- Check data loading times
- Profile memory usage
Scale testing
- Test batch size scaling
- Verify multi-GPU speedup
- Check memory limits

Cost Optimization

When to Use Cloud vs On-Premise

Cloud makes sense for:

Exploratory research (uncertain compute needs)
Burst workloads (occasional large experiments)
Trying different GPU types
Short-term projects

On-premise makes sense for:

Continuous training workloads
Long-term projects (>6 months)
Sensitive data (can’t leave premises)
Known compute requirements

Cloud GPU providers: See TensorRigs Cloud Comparison

Next Steps

Identify your workload from the categories above
Read the specific guide for optimization tips
Benchmark your setup to verify performance
Iterate and optimize based on results