groq
**Custom AI Accelerator Chips**
**AI Chip Landscape**
| Company | Chip | Focus |
|---------|------|-------|
| NVIDIA | H100, B200 | General AI |
| Groq | LPU | Low-latency inference |
| Cerebras | WSE-3 | Largest chip, training |
| Google | TPU v5 | Google Cloud AI |
| AWS | Trainium/Inferentia | AWS workloads |
| AMD | MI300X | NVIDIA alternative |
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**Groq LPU (Language Processing Unit)**
**Architecture**
- Deterministic silicon: No caching, no variable latency
- SRAM-based: Large on-chip memory
- Tensor streaming: Optimized for sequential ops
**Performance Claims**
| Metric | Claim |
|--------|-------|
| Latency | <100ms first token |
| Throughput | 500+ tokens/sec |
| Power efficiency | High tokens/watt |
**Groq API**
```python
from groq import Groq
client = Groq()
response = client.chat.completions.create(
model="llama-3.2-90b-vision-preview",
messages=[{"role": "user", "content": "Hello!"}]
)
print(response.choices[0].message.content)
```
**Cerebras WSE (Wafer Scale Engine)**
**Unique Architecture**
- Entire wafer as one chip (46,225 mm^2)
- 900,000 cores
- 40GB on-wafer memory
- Designed for massive models
**Use Cases**
- Training large models (no model parallelism needed)
- Drug discovery
- Climate modeling
**Comparison**
| Chip | Strength | Weakness |
|------|----------|----------|
| NVIDIA H100 | Ecosystem, flexibility | Cost, power |
| Groq LPU | Latency | Model size limits |
| Cerebras WSE | Large models | Specialization |
| TPU v5 | Google integration | Vendor lock-in |
| Trainium | AWS cost savings | AWS only |
**When to Consider**
| Use Case | Recommended |
|----------|-------------|
| General purpose | NVIDIA |
| Ultra-low latency | Groq |
| Massive training | Cerebras |
| Cloud provider | TPU/Trainium |
| Cost optimization | AMD/Trainium |
**Best Practices**
- Start with NVIDIA for flexibility
- Evaluate specialized hardware for specific needs
- Consider total cost (chips + development)
- Watch for SDK maturity
- Plan for vendor transitions