Parallel Inference Strategies: Scaling the Bot Farm

To run 18+ bots from a single laptop server, you cannot rely on sequential inference. In this lesson, we implement Parallel Inference Strategies using high-concurrency backends like vLLM and TGI (Text Generation Inference).

🏗️ The Concurrency Stack

Strategy	Logic	Best For
Sequential	1 prompt at a time.	Low-volume testing.
Batching	Grouping 10 prompts into 1 request.	High-volume lead scoring.
PagedAttention	Dynamically allocating VRAM for parallel users.	Multi-bot swarm orchestration.

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🛠️ Technical Snippet: vLLM Parallel Deployment

Deploying a model for high-concurrency access:

python -m vllm.entrypoints.openai.api_server \
    --model casperhansen/llama-3-70b-instruct-awq \
    --quantization awq \
    --max-parallel-requests 10

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🔍 Nuance: Queue Management

When running parallel swarms, you need a Request Queue (like Redis or a simple Python Queue). If 20 agents hit the GPU at the same time, the server will crash. The queue ensures every agent gets compute time without overflowing the VRAM.

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⚡ Practice Lab: The Parallel Stress Test

1. Setup: Use LM Studio or Ollama to start a local server.
2. Script: Write a Python script that sends 5 different prompts at the exact same time using asyncio or threading.
3. Analyze: Note how the TPS is shared between the requests.

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📝 Homework: The Bot Farm Architect

Design a hardware/software architecture that can handle 500 lead audits per hour. Calculate the number of GPUs and the parallel request limit required to hit this target.