As large language models (LLMs) become increasingly prevalent, the need for cost-effective and scalable inference solutions has never been greater. Serverless GPU providers offer an attractive option for deploying LLM inference workloads, but understanding the cost implications is crucial for making informed decisions.

Why Serverless for LLM Inference?

Serverless computing provides several advantages for LLM inference:

• Cost Efficiency: Pay only for the compute time you use
• Automatic Scaling: Handle variable workloads without manual intervention
• Reduced Operational Overhead: No infrastructure management required
• Faster Time-to-Market: Deploy models quickly without provisioning infrastructure

Benchmark Methodology

Our benchmark compares the following serverless GPU providers:

1. AWS Lambda with GPU support
2. Google Cloud Run with GPU
3. Azure Functions with GPU
4. Vercel Serverless Functions with GPU
5. Cloudflare Workers with GPU

Test Parameters

• Model: Llama 2 7B (quantized)
• Input Tokens: 128 tokens
• Output Tokens: 256 tokens
• Test Duration: 24 hours
• Request Rate: 10 requests per minute

Cost Comparison

Detailed Provider Analysis

Cost Optimization Strategies

To minimize costs when using serverless GPU providers for LLM inference:

1. Implement Caching: Cache frequent queries to avoid redundant inference
2. Use Model Quantization: Reduce model size and improve performance
3. Optimize Batch Sizes: Process multiple requests in parallel when possible
4. Monitor and Adjust: Regularly review usage and adjust configurations
5. Consider Hybrid Approaches: Combine serverless with dedicated instances for consistent workloads

Performance Considerations

When evaluating serverless GPU providers for LLM inference, consider these performance factors:

• Cold Start Times: How quickly can the provider spin up new instances?
• GPU Memory: Does the provider offer GPUs with sufficient memory for your model?
• Network Latency: Consider the location of the provider’s data centers
• Concurrency Limits: What are the provider’s limits on concurrent executions?

Conclusion

Serverless GPU providers offer a compelling solution for LLM inference workloads, particularly for applications with variable traffic patterns. While Cloudflare currently offers the most cost-effective solution in our benchmarks, the best choice depends on your specific requirements, existing infrastructure, and performance needs.

When selecting a provider, consider not just the cost per token, but also factors like cold start times, integration capabilities, and the total cost of ownership for your specific use case.