Groq vs Cerebras: AI Inference Performance Testing
Benchmarking latency and throughput between Groq's LPU and Cerebras's Wafer-Scale Engine for LLM inference.
For real-time multi-agent applications, response latency is critical. A delay of 5 seconds for a tool call can stall an entire pipeline. This led me to benchmark the two leading high-speed inference engines: Groq's LPU (Language Processing Unit) and Cerebras's Wafer-Scale Engine (WSE).
Key Metrics Tested
We ran Llama-3-70B-Instruct across both providers using a standardized set of 1,000 multi-turn conversation prompts. We monitored three key metrics:
- Time to First Token (TTFT): Essential for UI responsiveness.
- Tokens Per Second (TPS): Throughput speed for generating large response blocks.
- Jitter & Consistency: Variance in performance during high-concurrency periods.
Benchmark Results
| Metric | Groq LPU | Cerebras WSE |
|---|---|---|
| Avg. TTFT | 0.12s | 0.18s |
| Avg. TPS (70B) | 250 tokens/s | 450 tokens/s |
| Error Rate | 0.02% | 0.05% |
Key Takeaways
While Cerebras wins on sheer raw throughput (handling massive text generation at an astonishing 450+ TPS), Groq is slightly faster to initialize the first token, making it feel snappier for interactive chat interfaces. For autonomous agent orchestration where multiple tools are called, Cerebras's high speed is highly advantageous.