Gemma 4 on an Amazon SageMaker Endpoint: AWS CLI, NVIDIA L4, and an MCP Server

This article provides a step by step deployment guide for Gemma 4 E2B to an Amazon SageMaker hosted GPU enabled system. A suite of Python MCP tools is built to simplify management of the vLLM hosted deployment with Claude Code.

https://github.com/xbill9/sagemaker-gemma

What is this project trying to Do?

This project serves Gemma 4 E2B from a SageMaker real-time endpoint on one NVIDIA L4 GPU, using the vLLM container AWS publishes for SageMaker. Every AWS call is a plain aws CLI command, so each step can be run by hand or by the MCP server.

A SageMaker real-time endpoint is a managed HTTPS inference server. SageMaker places the container on a GPU instance, health-checks it, routes requests to it and writes its logs to CloudWatch. There is no instance to patch, no security group to open and no load balancer to build.

Where do I start?

The strategy for starting MCP development for model management is a incremental step by step approach.

First, the basic development environment is setup with the required system variables and a working Claude Code configuration.

Then, the Python MCP server is brought up over stdio and validated with Claude Code in the local environment. The deployment follows as eight steps, each shown as the raw aws command and the MCP tool that runs it.

At This Point You Should Have…

• An AWS account and the AWS CLI v2, signed in with aws login
• A SageMaker endpoint quota of at least 1 for a single-L4 instance type (ml.g6.xlarge, ml.g6.2xlarge or ml.g6.4xlarge)
• Python 3.11 or newer with mcp 2.x
• Claude Code or Gemini CLI installed and working
• jq for reading JSON replies

Setup the Basic Environment

Clone the repository and install the one requirement into the system Python:

git clone https://github.com/xbill9/sagemaker-gemma
cd sagemaker-gemma
python3 -m pip install -r requirements.txt
cp .env.example .env

.env is gitignored. It holds the settings every tool reads:

cat .env

# Copy to .env (gitignored) and edit. sm.py reads it, so the MCP server, CLI and Makefile all see it.
AWS_REGION=us-east-2
MODEL_ID=google/gemma-4-E2B-it
INSTANCE_TYPE=ml.g6.xlarge
ENDPOINT_NAME=gemma-4-e2b
ROLE_NAME=sagemaker-gemma-execution-role
MAX_MODEL_LEN=8192
# Leave empty to use the newest SageMaker vLLM image in the region.
IMAGE_URI=
# Fallback instance types, highest priority first (same GPU keeps runs comparable).
INSTANCE_POOLS=ml.g6.xlarge,ml.g6.2xlarge,ml.g6.4xlarge

Gemma 4 is Apache-2.0 and ungated on Hugging Face, so no Hugging Face token is needed.

Model Management Tool with MCP Stdio Transport

The simplest MCP transport is stdio: the client launches the server as a local process and talks to it over stdin and stdout. In this project Claude Code is the MCP client. The server is one file, server.py, on the MCP Python SDK 2.x:

mcp = MCPServer(RIG_NAME)
READ_ONLY = ToolAnnotations(readOnlyHint=True, idempotentHint=True)
WRITE = ToolAnnotations(destructiveHint=False)
DESTRUCTIVE = ToolAnnotations(destructiveHint=True)

Every tool carries one of the three annotations, so a client can tell a status check from a deploy from a delete.

The tools do no AWS work themselves. They call sm.py, which runs each request as an aws CLI subprocess:

def aws(*args: str, region: str | None = REGION, parse: bool = True) -> Any:
    """Run `aws <args> --output json` and return the parsed result."""
    cmd = ["aws", *args, "--output", "json"]

The CLI renews an aws login session on its own, so a server that stays up for hours keeps working credentials. sm.py also drops any AWS_SESSION_TOKEN the server inherits from its parent process, because a static token expires inside a long-running server and outranks the login session.

Running the Python Code

The project can be linted:

make lint

All checks passed!
16 files already formatted

and tested:

make test

----------------------------------------------------------------------
Ran 16 tests in 0.010s

OK

The tests replace the aws subprocess with a fake, so they run offline with no credentials. One of them compares the registered tool set and annotations against a fixed list: a tool that failed to register, or a delete that lost its destructive flag, fails the suite. ✅

Test the Protocol by Hand

A client speaks JSON-RPC over stdio. Hold stdin open with sleep, or the server sees end-of-input and exits before it answers:

{ printf '%s\n' \
  '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2025-06-18","capabilities":{},"clientInfo":{"name":"probe","version":"0"}}}' \
  '{"jsonrpc":"2.0","method":"notifications/initialized"}' \
  '{"jsonrpc":"2.0","id":2,"method":"tools/list","params":{}}'; sleep 3; } \
  | python3 server.py 2>/dev/null

Summarised:

1 sagemaker-gemma
2 ['check_quotas', 'delete_endpoint', 'deploy_endpoint', 'find_vllm_image', 'get_deployment_config', 'get_endpoint_logs', 'get_endpoint_status', 'get_help', 'list_endpoints', 'query_model', 'verify_model_health']

🟢 The server answers the handshake and lists 11 tools.

Claude Code .mcp.json

Claude Code reads .mcp.json in the project directory and launches the server with the system python3:

{
  "mcpServers": {
    "sagemaker-gemma": {
      "command": "python3",
      "args": ["/home/xbill/sagemaker-gemma/server.py"]
    }
  }
}

Gemini CLI reads the same entry from .gemini/settings.json.

Validation with Claude Code

claude mcp get sagemaker-gemma

sagemaker-gemma:
  Scope: Project config (shared via .mcp.json)
  Status: ✔ Connected

From inside Claude Code, get_help returns the resolved settings and the order of work:

### sagemaker-gemma

Serving `google/gemma-4-E2B-it` with vLLM on a **SageMaker real-time endpoint**, managed
entirely through the aws CLI.

| Setting | Value |
| --- | --- |
| Region | `us-east-2` |
| Endpoint | `gemma-4-e2b` |
| Instance types (priority order) | `ml.g6.xlarge`, `ml.g6.2xlarge`, `ml.g6.4xlarge` |
| Max model length | `8192` |
| Image | `newest SageMaker vLLM image (find_vllm_image)` |
| Execution role | `sagemaker-gemma-execution-role` |

**Order of work:** check_quotas → deploy_endpoint → get_endpoint_status until
`InService` (about 10 minutes once an instance is placed) → verify_model_health
→ query_model → delete_endpoint.

The steps below follow that order. Each one shows the raw CLI command; deploy_endpoint runs Steps 2 to 6 in one call.

export AWS_REGION=us-east-2 AWS_PAGER=
export NAME=gemma-4-e2b
export MODEL_ID=google/gemma-4-E2B-it

Step 1 — Check the Endpoint Quota

SageMaker endpoint quotas are per instance type, per region, and count instances.

aws service-quotas list-service-quotas --service-code sagemaker \
  --query "Quotas[?QuotaName=='ml.g6.xlarge for endpoint usage'].Value"

[
    1.0
]

The check_quotas tool reads the same quota for each fallback type across the US regions:

| Instance | us-east-1 | us-east-2 | us-west-1 | us-west-2 |
| --- | ---: | ---: | ---: | ---: |
| `ml.g6.xlarge` | 1 | 1 | - | 1 |
| `ml.g6.2xlarge` | 1 | 1 | - | 1 |
| `ml.g6.4xlarge` | 1 | 1 | - | 1 |

Regions with quota for at least one of these types: us-east-1, us-east-2, us-west-2.

A - means the type is not offered in that region. A 0 means a quota increase request first.

🔎 Tip: One Quota Call per Region

list-service-quotas pages through every SageMaker quota in the region, and the Service Quotas API is rate limited per account. Twelve calls at once, one per type per region, came back as:

An error occurred (TooManyRequestsException) when calling the ListServiceQuotas operation (reached max retries: 2)

check_quotas makes one call per region, one region at a time, and filters the types from that one result. sm.py also sets AWS_RETRY_MODE=adaptive so the CLI backs off and retries.

Step 2 — Find the vLLM Container

The AWS vLLM repository holds pinned release tags such as 0.30.0-gpu-py312-cu130-ubuntu24.04-sagemaker-v1.1, floating aliases such as 0.30-gpu-py312, and -soci index tags. Older release lines receive patch rebuilds, so the most recent push can carry an older vLLM. Sort the pinned tags by version:

TAG=$(aws ecr describe-images --registry-id 763104351884 --repository-name vllm \
  --query "imageDetails[].imageTags[]" --output text | tr '\t' '\n' \
  | grep -E -- '^[0-9]+\.[0-9]+\.[0-9]+-.*-sagemaker-v[0-9]+\.[0-9]+$' | sort -V | tail -1)
export IMAGE=763104351884.dkr.ecr.$AWS_REGION.amazonaws.com/vllm:$TAG
echo $IMAGE

763104351884.dkr.ecr.us-east-2.amazonaws.com/vllm:0.30.0-gpu-py312-cu130-ubuntu24.04-sagemaker-v1.1

The find_vllm_image tool applies the same rule. The image runs vLLM 0.30.0.

Step 3 — Create the Execution Role

SageMaker assumes this role to pull the image and write logs. It is created once per account.

aws iam create-role --role-name sagemaker-gemma-execution-role \
  --assume-role-policy-document '{"Version":"2012-10-17","Statement":[{"Effect":"Allow","Principal":{"Service":"sagemaker.amazonaws.com"},"Action":"sts:AssumeRole"}]}'
aws iam attach-role-policy --role-name sagemaker-gemma-execution-role \
  --policy-arn arn:aws:iam::aws:policy/AmazonSageMakerFullAccess
export ROLE=$(aws iam get-role --role-name sagemaker-gemma-execution-role --query Role.Arn --output text)

On a second run create-role reports EntityAlreadyExists; get-role still sets ROLE. deploy_endpoint creates the role only when it is missing.

Step 4 — Create the Model

A SageMaker model pairs an image with its settings. The container turns each SM_VLLM_ variable into the matching vLLM flag: SM_VLLM_MAX_MODEL_LEN becomes --max-model-len.

aws sagemaker create-model --model-name $NAME --execution-role-arn $ROLE \
  --primary-container "{\"Image\":\"$IMAGE\",\"Environment\":{
    \"SM_VLLM_MODEL\":\"$MODEL_ID\",
    \"SM_VLLM_MAX_MODEL_LEN\":\"8192\",
    \"SM_VLLM_GPU_MEMORY_UTILIZATION\":\"0.9\"}}"

{
    "ModelArn": "arn:aws:sagemaker:us-east-2:<account-id>:model/gemma-4-e2b"
}

Step 5 — Create the Endpoint Config With Fallback Instance Types

The endpoint config says where the model runs. InstancePools lists up to five instance types in priority order, and SageMaker places the first one with a free instance. All three types below carry one L4, so the model sees the same GPU whichever is placed.

aws sagemaker create-endpoint-config --endpoint-config-name $NAME \
  --production-variants "[{\"VariantName\":\"AllTraffic\",\"ModelName\":\"$NAME\",
    \"InitialInstanceCount\":1,
    \"InstancePools\":[{\"InstanceType\":\"ml.g6.xlarge\",\"Priority\":1},
                       {\"InstanceType\":\"ml.g6.2xlarge\",\"Priority\":2},
                       {\"InstanceType\":\"ml.g6.4xlarge\",\"Priority\":3}],
    \"ContainerStartupHealthCheckTimeoutInSeconds\":1800,
    \"ModelDataDownloadTimeoutInSeconds\":1800}]"

{
    "EndpointConfigArn": "arn:aws:sagemaker:us-east-2:<account-id>:endpoint-config/gemma-4-e2b"
}

The two 1800-second timeouts give the container time to download the weights and compile before SageMaker's health check gives up.

🔎 Tip: A Fallback List Holds Quota for Every Type in It

With one Gemma endpoint running on ml.g6.xlarge from the three-type list above, a second endpoint asking for ml.g6.2xlarge in the same region was refused:

ResourceLimitExceeded: The account-level service limit 'ml.g6.2xlarge for endpoint usage' is 1 Instances, with current utilization of 1 Instances and a request delta of 1 Instances.

With a quota of 1 per type, a second endpoint goes in another region, or uses types outside the first endpoint's list.

Step 6 — Create the Endpoint

aws sagemaker create-endpoint --endpoint-name $NAME --endpoint-config-name $NAME

{
    "EndpointArn": "arn:aws:sagemaker:us-east-2:<account-id>:endpoint/gemma-4-e2b"
}

Billing starts when an instance is placed. aws sagemaker wait endpoint-in-service --endpoint-name $NAME blocks until it is ready; get_endpoint_status reports the same state and the instance type that was placed:

✅ `gemma-4-e2b` in `us-east-2`: **InService**
- Instance: `ml.g6.xlarge`

The container log is in CloudWatch, and get_endpoint_logs tails it:

aws logs tail /aws/sagemaker/Endpoints/$NAME --follow

Start-up on the L4, in minutes after create-endpoint:

🔎 Tip: A Creating Endpoint Cannot Be Deleted

create-endpoint cannot be undone until the endpoint settles. A delete-endpoint sent while it is Creating is refused:

aws: [ERROR]: An error occurred (ValidationException) when calling the DeleteEndpoint operation: Cannot update in-progress endpoint "arn:aws:sagemaker:us-east-2:<account-id>:endpoint/gemma-4-e2b".

The endpoint finishes starting, bills from the moment its instance is placed, and can be deleted once it reaches InService or Failed. Check the model ID and instance types before Step 6.

🔎 Tip: Capacity Is Separate From Quota

A quota of 1 lets you request one instance; the region still has to have one free. In us-east-1, two requests for L4 instances each stayed in Creating for about 30 minutes and then failed:

Unable to provision requested ML compute capacity due to InsufficientInstanceCapacity error. Please retry using a different ML instance type or after some time.

The same request in us-east-2 placed an instance at once. While SageMaker waits for capacity, no container starts and the CloudWatch log group never appears, which is how get_endpoint_logs tells a capacity wait from a slow model load. No charge accrues during the wait. When it fails, repeat Steps 4 to 6 in another region where Step 1 shows the same quota.

Step 7 — Cross Check the Deployed Model

The vLLM container accepts an OpenAI chat body on invoke-endpoint:

echo '{"messages":[{"role":"user","content":"Why is the sky blue?"}],"max_tokens":256}' > req.json
aws sagemaker-runtime invoke-endpoint --endpoint-name $NAME \
  --content-type application/json --body fileb://req.json out.json
jq -r '.choices[0].message.content' out.json | head -1
jq -c '{model,usage}' out.json

{
    "ContentType": "application/json",
    "InvokedProductionVariant": "AllTraffic"
}
The sky is blue due to a phenomenon called **Rayleigh scattering**. This process is caused by how sunlight interacts with the Earth's atmosphere.
{"model":"google/gemma-4-E2B-it","usage":{"prompt_tokens":15,"total_tokens":271,"completion_tokens":256,"prompt_tokens_details":null,"completion_tokens_details":null}}

verify_model_health sends one short request and checks for a reply:

✅ model=`google/gemma-4-E2B-it` tokens=2 wall=0.749s reply='ok'

Step 8 — Teardown

The endpoint bills by the hour until it is deleted. Deleting the config and the model as well leaves nothing behind. delete_endpoint runs all three:

aws sagemaker delete-endpoint --endpoint-name $NAME
aws sagemaker delete-endpoint-config --endpoint-config-name $NAME
aws sagemaker delete-model --model-name $NAME

Each command prints nothing and exits 0. list_endpoints confirms the account is clear:

### 0 endpoint(s) matching `gemma`

Summary

The goal of this article was to deploy Gemma 4 E2B to an Amazon SageMaker real-time endpoint with the AWS CLI and manage it from an MCP server. The key to the solution was the AWS vLLM SageMaker container, which turns the deployment into three create- calls and makes the endpoint answer OpenAI-style chat requests. The deployment results were:

• 🟢 Eight CLI steps take an account from quota check to a serving endpoint and back to nothing
• 🟢 The endpoint reached InService 9.9 minutes after create-endpoint, with the weights using 9.75 GiB of the L4's 24 GB
• 🟢 The MCP server runs every step from Claude Code, marks each tool read-only, write or destructive, and passes its tests offline
• 🟢 InstancePools gives one endpoint config several fallback instance types
• ⚠️ L4 capacity varied by region: us-east-1 refused twice, about 30 minutes each, while us-east-2 placed an instance at once
• ⚠️ A fallback list holds quota for every type in it

Scope: one account, ml.g6.xlarge with one NVIDIA L4, vLLM 0.30.0 from the AWS container, google/gemma-4-E2B-it at full precision, deployed in us-east-2 on 2026-09-25. Start-up times are from a single deployment.

The strategy for using MCP for SageMaker deployment was validated with an incremental step by step approach.

References

• Repository: https://github.com/xbill9/sagemaker-gemma
• Gemma 4 E2B on Hugging Face: https://huggingface.co/google/gemma-4-E2B-it
• AWS Deep Learning Containers: https://github.com/aws/deep-learning-containers
• SageMaker real-time inference: https://docs.aws.amazon.com/sagemaker/latest/dg/realtime-endpoints.html
• CreateEndpointConfig API: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateEndpointConfig.html
• MCP Python SDK: https://github.com/modelcontextprotocol/python-sdk
• vLLM: https://docs.vllm.ai/