Cost Benchmark: Serverless vs Dedicated GPU Instances for Running Llama 3.2 70B in Production Introduction Meta’s Llama 3.2 70B Instruct has become a go-to open-weight model for production-grade NLP workloads, offering state-of-the-art performance for chat, summarization, and code generation. For teams deploying it at scale, the biggest operational cost is GPU infrastructure: choosing between fully managed serverless GPU platforms and self-managed dedicated GPU instances can swing monthly costs by 3x or more. This benchmark compares real-world production costs, latency, and throughput for both options across varying workload sizes. Test Setup All tests use Llama 3.2 70B Instruct in FP16 precision (140GB VRAM footprint) to ensure apples-to-apples comparison.…