Migrate from Google Vertex to Dedicated GPU: Conversational AI Guide

Google Deprecated Your Vertex AI Model Mid-Sprint

The engineering team found out through a deprecation email on a Wednesday morning. Google was retiring the PaLM 2 model they’d spent three months building their conversational AI around. They had 90 days to migrate to Gemini — but Gemini behaved differently. System prompts that worked on PaLM produced inconsistent results on Gemini. Conversation flows that felt natural now felt stilted. Three months of prompt engineering, thrown away because Google decided to sunset a model. The rewrite took six weeks and introduced regressions that took another month to iron out. All because they built their product on someone else’s model lifecycle.

When you self-host your conversational AI on a dedicated GPU, you choose when to change models. Your Llama 3.1 deployment doesn’t get deprecated. It runs until you decide otherwise. Here’s how to extract your conversational AI from Vertex.

Vertex AI vs Self-Hosted for Conversations

Migration Steps

Step 1: Export your conversation design. Document every conversation flow: welcome messages, fallback responses, escalation triggers, entity extraction patterns, and multi-turn context management. These are your intellectual property — they transfer to any model.

Step 2: Select your model. For conversational AI, Llama 3.1 70B-Instruct is the gold standard among open-source models. It handles multi-turn conversations naturally, follows complex system prompts, and maintains personality consistency across long dialogues. For simpler conversational use cases (FAQ bots, appointment scheduling), Llama 3.1 8B is remarkably capable.

Step 3: Provision your server. An RTX 6000 Pro 96 GB from GigaGPU comfortably serves a 70B conversational model with room for concurrent users. For high-concurrency deployments (100+ simultaneous conversations), consider a dual-GPU setup.

Step 4: Deploy with vLLM. vLLM’s OpenAI-compatible endpoint handles the conversation API. The key migration detail is translating Vertex’s request format to the OpenAI chat format:

# Vertex AI (Gemini) format
from vertexai.generative_models import GenerativeModel
model = GenerativeModel("gemini-1.5-pro")
chat = model.start_chat()
response = chat.send_message("Hello, how can I return a product?")

# Self-hosted equivalent
from openai import OpenAI
client = OpenAI(base_url="http://your-gigagpu:8000/v1", api_key="none")
response = client.chat.completions.create(
    model="llama-70b",
    messages=[
        {"role": "system", "content": your_system_prompt},
        {"role": "user", "content": "Hello, how can I return a product?"}
    ]
)

Step 5: Manage conversation state. Vertex’s Gemini chat object manages turn history internally. With a self-hosted model, you manage the message history yourself — which is actually an advantage. You can implement sliding window context, summarise old turns, or inject retrieved context between turns for smarter conversations.

Step 6: Test with real conversation logs. Replay 500 historical conversations through both systems. Have evaluators blind-rate response quality, personality consistency, and task completion rate.

Advanced Conversational Features

Self-hosting enables conversational AI features that Vertex doesn’t support or charges extra for:

• Personality fine-tuning: LoRA fine-tune your model on 1,000 example conversations in your brand voice. Takes 2-3 hours on an RTX 6000 Pro, costs nothing beyond the server you already have.
• Conversation memory: Implement long-term user memory by summarising past conversations and injecting summaries into the system prompt. Unlimited, because there’s no per-token cost for longer prompts.
• Emotion detection: Run a lightweight sentiment model alongside your conversation model. React to user frustration in real time. On Vertex, this would be a separate API call with separate charges.
• A/B testing personalities: Run multiple model configurations simultaneously to test which personality drives better engagement. Free at the margin.

Cost Comparison

Model your exact conversation volume at GPU vs API cost comparison.

Own Your Conversational AI

Your conversational AI is your customer relationship. Building it on a platform that can deprecate your model, change its behaviour, or raise prices at any time is a strategic risk. Self-hosting puts that relationship entirely in your hands.

Related reading: Vertex recommendation engine migration, Anthropic customer support migration, and the self-host LLM guide. For cost analysis, the LLM cost calculator and breakeven analysis are essential. Explore private AI hosting for data-sensitive deployments, and visit our tutorials section for more guides.

Filed under: Tutorials