The AI Hardware Showdown: GPUs, TPUs, and Custom Chips for Deep Learning (2025)
By AI Vault Hardware Team••25 min read
Executive Summary
Key insights for choosing AI hardware in 2025
- Best for General Use
- NVIDIA H100 / AMD MI300 GPUs
- Best for Large-Scale Training
- Google TPU v5 / Cerebras CS-3
- Cost-Effective Choice
- Cloud-based TPUs for most workloads
1. AI Hardware Landscape in 2025
The AI hardware market has evolved significantly, with specialized architectures emerging for different machine learning workloads. Here's an overview of the current landscape.
GPUs
Key Vendors
NVIDIAAMDIntel
Example Chips (2025)
| Model | TFLOPS | Memory | Power | Best For |
|---|---|---|---|---|
| NVIDIA H100 | 120 TFLOPS | 80GB HBM3 | 700W | General DL training, CV, NLP |
| AMD MI300 | 110 TFLOPS | 128GB HBM3 | 750W | HPC, Large models |
| Intel Gaudi3 | 95 TFLOPS | 64GB HBM2e | 600W | Enterprise AI workloads |
Advantages
- Wide software support (CUDA, ROCm, oneAPI)
- Flexible for various workloads
- Large developer community
- Mature tooling and libraries
Limitations
- Higher power consumption
- General-purpose architecture
- Can be expensive at scale
TPUs
Key Vendors
Google
Example Chips (2025)
| Model | TFLOPS | Memory | Power | Best For |
|---|---|---|---|---|
| TPU v5 | 180 TFLOPS | 128GB HBM | 450W | Large-scale Transformer models |
| TPU v4 | 120 TFLOPS | 64GB HBM | 300W | Production ML workloads |
Advantages
- Optimized for matrix operations
- Lower power consumption
- Tight integration with Google Cloud
- Excellent for large batch sizes
Limitations
- Limited to Google Cloud
- Less flexible for non-ML workloads
- Smaller developer community
Custom AI Chips
Key Vendors
CerebrasGraphcoreSambaNovaGroq
Example Chips (2025)
| Model | TFLOPS | Memory | Power | Best For |
|---|---|---|---|---|
| Cerebras CS-3 | 125 TFLOPS | 44GB On-chip | 23kW | Extremely large models |
| Graphcore Bow | 350 TFLOPS | 900GB/s | 900W | Sparse models, IPU-specific workloads |
| GroqChip | 1000 TFLOPS | 230GB/s | 300W | Low-latency inference |
Advantages
- Specialized for specific workloads
- Potential for better performance/Watt
- Innovative architectures
- Designed for future ML workloads
Limitations
- Limited software ecosystem
- Higher risk of vendor lock-in
- Smaller community and resources
2. Performance Benchmarks
Comparative Performance (2025)
Performance metrics across different hardware platforms
| Benchmark | NVIDIA H100 | AMD MI300 | TPU v5 | Cerebras CS-3 | Graphcore Bow |
|---|---|---|---|---|---|
| ResNet-50 Training (images/sec) | 3,500 | 3,200 | 3,800 | 4,100 | 2,800 |
| GPT-3 175B Training (tokens/sec) | 1,200 | 950.0 | 1,800 | 2,200 | 1,500 |
| Power Efficiency (samples/Joule) | 5.0 | 4.8 | 8.4 | 7.2 | 6.5 |
| Cost per 1M Training Tokens ($) | 0.8 | 0.8 | 0.7 | 0.7 | 0.8 |
Benchmarking Notes
- All benchmarks conducted with latest software stacks as of Q1 2025
- Results may vary based on workload characteristics and optimizations
- Power efficiency measured at full load
- Cost estimates based on major cloud provider pricing
3. Hardware Selection Guide
Choosing the Right AI Hardware
Recommendations based on use case and requirements
| Use Case | Recommendation | Reasoning | Cost |
|---|---|---|---|
Startups & Researchers Startup training medium models • Academic research • Prototyping | Cloud GPUs (NVIDIA A100/H100) | Best balance of flexibility, availability, and ecosystem support | $$ |
Enterprise Production Large-scale model training • Production inference • Enterprise AI services | TPUs or Cloud GPUs | Reliable performance, good support, and predictable costs at scale | $$$ |
Cutting-Edge Research Novel model architectures • Extremely large models • Specialized workloads | Custom AI Chips (Cerebras, Graphcore) | Specialized architectures for novel model architectures | $$$$ |
Edge & On-Device AI Smartphones • IoT devices • Autonomous vehicles | Specialized Edge Chips | Power efficiency and low-latency requirements | $-$$ |
4. Future Trends in AI Hardware
2025-2026
Chiplet Architectures
Modular designs combining specialized chiplets for different ML operations
Impact:Better performance, lower costs, and more flexibility
2026+
Photonic Computing
Using light instead of electricity for faster, cooler computation
Impact:Potential 100x speedup for specific workloads
2025-2027
Neuromorphic Chips
Hardware that mimics the human brain's neural structure
Impact:Dramatically lower power consumption for AI workloads
2027+
Quantum AI Accelerators
Quantum processors for specific ML tasks
Impact:Potential exponential speedup for optimization problems
5. Case Study: Large-Scale Model Training
Leading AI Research Lab
Training foundation models with 1T+ parameters cost-effectively
- Challenge
- Training foundation models with 1T+ parameters cost-effectively
- Solution
- Hybrid approach using Cerebras CS-3 for pre-training and NVIDIA H100 for fine-tuning
- Results
- 50% reduction in training time compared to GPU-only approach
- 40% lower cloud compute costs
- Enabled training of larger models with same budget
- Improved researcher productivity with faster iteration cycles