Migrate from AWS Bedrock to Dedicated GPU: Multi-Model Pipeline Guide

Running Three Models on Bedrock Means Paying Three Times

Your AI pipeline isn’t a single model — it’s an orchestra. A small model classifies incoming requests, a medium model handles routine responses, and a large model tackles complex reasoning tasks. On AWS Bedrock, each model invocation carries its own per-token price. That classifier running Claude 3 Haiku? Cheap individually, but at 100,000 classifications per day, it adds up. The routing model using Llama 2 70B? Separate bill. The final response from Claude 3 Sonnet? The most expensive leg. Add them together and your “efficient multi-model architecture” costs more than running a single large model for everything, because Bedrock charges you at every hop.

On a dedicated GPU, you can run all three models simultaneously on the same hardware. One server, one monthly price, unlimited invocations across every model in your pipeline. Here’s how to make the switch.

Anatomy of a Multi-Model Bedrock Pipeline

Before migrating, map every model in your pipeline and its role:

On an RTX 6000 Pro 96 GB, you can comfortably serve a 70B model for complex tasks and an 8B model for classification/routing simultaneously. The embedding model barely registers in VRAM usage. Total VRAM: ~50 GB, leaving 30 GB of headroom.

Migration Steps

Step 1: Profile your model usage. Pull CloudWatch metrics for each Bedrock model: invocations per minute, average token counts, and latency requirements. Identify which models can be consolidated — often your classification and routing models can be the same 8B model with different prompts.

Step 2: Set up your GPU server. Provision an RTX 6000 Pro 96 GB from GigaGPU. For heavy multi-model pipelines, two RTX 6000 Pros provide ample headroom for running 3-4 models concurrently with high throughput.

Step 3: Deploy with vLLM multi-model serving. vLLM supports serving multiple models from a single process. Each model gets its own route:

# Launch with multiple models
python -m vllm.entrypoints.openai.api_server \
  --model meta-llama/Llama-3.1-70B-Instruct \
  --served-model-name large-model \
  --port 8000 &

python -m vllm.entrypoints.openai.api_server \
  --model meta-llama/Llama-3.1-8B-Instruct \
  --served-model-name small-model \
  --port 8001

Step 4: Replace Bedrock SDK calls. Each bedrock.invoke_model() call changes to an OpenAI-compatible HTTP request against the appropriate port. Your routing logic stays identical — only the transport layer changes.

Step 5: Test the full pipeline end-to-end. Feed 1,000 production requests through both the Bedrock pipeline and the self-hosted pipeline in parallel. Compare final output quality, end-to-end latency, and error rates. Multi-model pipelines amplify small issues at each stage, so test thoroughly.

Optimising Inter-Model Communication

On Bedrock, each model call traverses the network — from your Lambda to the Bedrock endpoint and back, multiple times per request. On a dedicated GPU, all models run on the same machine. Inter-model latency drops from 100-300ms per hop to sub-millisecond. For a five-stage pipeline, this alone shaves 500ms-1.5 seconds off end-to-end response time.

Use Ollama if you want an even simpler multi-model setup — Ollama lets you call different models by name and handles memory management automatically.

Cost Comparison

Model your specific pipeline costs with the LLM cost calculator.

Consolidate Your AI Stack

Multi-model pipelines are where self-hosting shines brightest. The economics of paying per-token for every model in a chain are brutal at scale. On dedicated hardware, adding another model to your pipeline costs nothing beyond the VRAM it consumes.

For related migrations, see the Bedrock enterprise chatbot guide and the document processing migration. The TCO comparison covers the full cost picture, while the GPU vs API cost tool models individual workloads. Browse open-source model hosting for model selection, and read the self-hosting guide for infrastructure fundamentals. More migration paths are in our tutorials section.

Filed under: Tutorials