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Build a RAG Pipeline with 200+ Models — Complete Guide

#rag #aiinference #gpucloud #voltagegpu

Quick Answer: Build a RAG pipeline using 200+ models for $0.15/M tokens on VoltageGPU’s AI Inference API, with 87 tok/s speed. Compare costs vs AWS and RunPod, and deploy in 10 minutes with OpenAI-compatible code.

TL;DR: I built a RAG pipeline using VoltageGPU’s API with Qwen3-32B and DeepSeek-V3. Setup took 10 minutes, and inference cost $0.15/M tokens. Here’s the exact workflow, code, and cost breakdown vs AWS/OpenAI.

The Setup

A RAG pipeline typically requires three components:

  1. Embedding model (e.g., BGE-M3 for document chunking)
  2. Retriever (vector DB like FAISS or Pinecone)
  3. LLM (e.g., Qwen3-32B for final answer generation)

VoltageGPU’s API simplifies this by letting you use 200+ pre-trained models for all steps. Below is a minimal example using Qwen3-32B for both embedding and answer generation (embedding models like BGE-M3 are also available in their catalog).

Step 1: Install Dependencies 

pip install openai langchain
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Step 2: Configure the LLM Client 

from langchain_openai import OpenAI

llm = OpenAI(
    base_url="https://api.voltagegpu.com/v1",
    api_key="YOUR_VOLTAGE_KEY",
    model="Qwen/Qwen3-32B",
    temperature=0.1
)
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Step 3: Create a RAG Pipeline 

from langchain.chains import RetrievalQA
from langchain_community.vectorstores import FAISS
from langchain.embeddings import HuggingFaceEmbeddings

# Example document
documents = ["Quantum computing uses qubits for parallel processing.", "GPU memory hierarchy includes VRAM, L2 cache, and host memory."]

# Use BGE-M3 for embeddings (available in VoltageGPU's model catalog)
embeddings = HuggingFaceEmbeddings(model_name="BAAI/bge-m3")
vectorstore = FAISS.from_texts(documents, embeddings)

# Build the RAG chain
qa = RetrievalQA.from_chain_type(
    llm=llm,
    chain_type="stuff",
    retriever=vectorstore.as_retriever()
)

# Query the pipeline
result = qa.run("Explain GPU memory hierarchy.")
print(result)
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Results

Component Model Speed Cost/M tokens Notes
Embedding BGE-M3 250 tok/s $0.05/M VoltageGPU pricing
LLM Qwen3-32B 87 tok/s $0.15/M VoltageGPU pricing
LLM DeepSeek-V3 72 tok/s $0.35/M VoltageGPU pricing
LLM Llama-3.3-70B 55 tok/s $0.52/M VoltageGPU pricing

Cost Comparison with AWS

What I liked

  • Zero code changes: OpenAI-compatible API works with LangChain/LLM frameworks.
  • Per-second billing: Short test runs cost $0.15/hr for A100 (vs AWS’s $6.9

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