Serverless Inference: AWS Lambda & Modal

+-----------------------------------------------------------------------------+
|                     SERVERLESS INFERENCE PARADIGMS                          |
+-----------------------------------------------------------------------------+
|  Lightweight LLM APIs (OpenAI/Anthropic wrappers): AWS Lambda / API Gateway |
|  Heavyweight Self-Hosted GPU Inference (Llama 3 8B): Modal / RunPod         |
+-----------------------------------------------------------------------------+

Running LLM Applications on AWS Lambda

AWS Lambda is ideal for orchestration and API proxies:

• Zero Idle Cost: Scale down to 0 requests when inactive.
• Integration: Seamless IAM auth, CloudWatch logs, and S3 event triggers.
• Limitations: Max 15-minute timeout, 10 GB container limit, and no GPU support.

Running Open-Source GPU Models Serverlessly with Modal

For hosting open-source models (like Llama-3-8B) with serverless pay-per-second GPU execution:

import modal

app = modal.App("llama-inference-service")

image = modal.Image.debian_slim().pip_install(
    "vllm", "torch", "transformers"
)

@app.cls(gpu="A10G", image=image, container_idle_timeout=120)
class LlamaModel:
    @modal.enter()
    def load_model(self):
        from vllm import LLM
        self.llm = LLM(model="meta-llama/Meta-Llama-3-8B-Instruct")

    @modal.method()
    def generate(self, prompt: str):
        from vllm import SamplingParams
        sampling_params = SamplingParams(temperature=0.2, max_tokens=256)
        outputs = self.llm.generate([prompt], sampling_params)
        return outputs[0].outputs[0].text

@app.local_entrypoint()
def main():
    model = LlamaModel()
    result = model.generate.remote("Explain Delta Lake ACID transactions in 1 sentence.")
    print("Result:", result)