In the evolving landscape of AI Infrastructure, Serverless GPUs have been a game changer. Six months on from our last guide, which sparked multiple discussions & created more awareness about the space, we've returned with fresh insights on the state of "True Serverless" offerings and I am here sharing performance benchmark & cost effectiveness analysis for Llama 2-7Bn & Stable Diffusion 2-1 model.
📊 Performance Testing Methodology: We put the spotlight on popular serverless GPU contenders: Runpod, Replicate, Inferless, and Hugging Face Inference Endpoints, specifically testing for:
1. Cold Starts: Varied across platforms. Latency minus inference time, represents the delay due to initializing a dormant Serverless function.
2. Variability: We don't just trust one-off results; we test over 5 days to ensure stability. We observed differences in consistency.
3. Autoscaling: Simulated traffic peaks to assess how well platforms scale under pressure ,we tried the simulation on what happens when we receive 200 requests with a concurrency of 5. Not all platforms could manage linear scaling efficiently, leading to varied latencies under load.
4. Decoding Serverless Pricing:
4.1 We modeled a scenario where you process 1,000 documents daily with the Llama 2 7Bn model. Here's the TL;DR on costs:
4.2 For the image processing (stable diffusion) use case, only the number of processed items and cold start times differ. Instead of 1,000 documents, we're considering 1,000 images daily.
🔮 Overall Insights: The serverless GPU sector is advancing, notably in reducing cold-start times and improving cost efficiency. However, the best choice depends on specific use cases. While AWS Lambda is a leader in general serverless solutions, specialized tasks, particularly those GPU-intensive, may find better options elsewhere.
Detailed Blog link: https://www.inferless.com/learn/the-state-of-serverless-gpus-part-2
This analysis aims at shedding light on the serverless GPU arena. We welcome feedback and aim for precision in our findings.
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