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    Hardened Ollama GPU on Ubuntu 24.04

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    Sold by: EASYCLOUD 
    Deployed on AWS
    AWS Free Tier
    This is a repackaged open source software product wherein additional charges apply for a hardened, AWS-optimized image with verification reports and maintenance. This AMI delivers Ollama 0.31 (GPU) with the qwen3:4b-instruct model baked in, on an Ubuntu 24.04 LTS base tuned for EC2 by Easycloud-with a provenance evidence pack and SBOM.

    Overview

    This is a repackaged open source software product wherein additional charges apply for a hardened, AWS-optimized image with verifiable provenance evidence and maintenance. Ollama 0.31 (GPU) runs on an Ubuntu 24.04 LTS base tuned for Amazon EC2, with a documented set of security settings applied on top and a complete provenance evidence pack including a Software Bill of Materials (SBOM).

    Value-Added Features

    Ollama 0.31 (GPU) Deployment:

    1. Self-Contained GPU Inference: The qwen3:4b-instruct model, the NVIDIA driver stack and the Ollama runtime are baked into the image, so a launched instance serves inference without downloading a single byte at runtime.

    2. Fast First Answer: A boot-time prewarm step reads the model into memory in parallel with startup, so the API answers its first request in about five minutes on first boot and within roughly twenty seconds after each subsequent stop and start.

    3. Safe Network Shape: An nginx reverse proxy publishes the OpenAI-compatible API on port 8000 while the native Ollama listener stays bound to the loopback interface.

    Comprehensive Evidence Pack:

    Every AMI carries an 8-file provenance evidence pack under /opt/optimization-report/:

    • Tuning_Parameters.txt: Every tuning change, shown against its base-image value, with the reasoning and how to reverse it.
    • Security_Parameters.txt: All 31 security settings in the same format; the 10 already at the wanted value are marked unchanged rather than presented as improvements.
    • package_changes.txt: Packages upgraded and installed versus the base image.
    • sbom.spdx.json: Software Bill of Materials in SPDX format, generated from the dpkg database.
    • cve_scan.txt / cve_scan_full.txt.gz: Vulnerability scan grouped by whether an upstream fix exists, so unfixable findings are not counted against the image.
    • key_files.sha256: SHA-256 checksums of all 7 files this image changed, so the delivered state can be verified in one command.
    • README.txt: Index of artifact contents and operational guidance.

    Security & Attack Surface:

    1. Kernel Network Hardening: 19 network-related kernel parameters are fixed in /etc/sysctl.d/99-security.conf: ICMP redirects are neither accepted nor sent, source-routed packets are rejected, and the rest are pinned at safe values so they cannot drift.

    2. Unused Kernel Modules Disabled: 12 rarely used filesystem and network protocol modules are blocked from loading via /etc/modprobe.d/99-disable-unused.conf. No NVIDIA module is affected, and squashfs stays loadable so snap-packaged agents keep working.

    3. SSH Login Window Tightened: LoginGraceTime is reduced from 120 to 60 seconds. Root login policy, authentication retries and forwarding are left as the base image ships them.

    4. Fully Patched at Release: Zero packages carried an available upstream fix at build time, verified from what apt actually offered rather than from a scanner opinion.

    GPU Stack Left Intact:

    1. Nothing Touches the Driver: The NVIDIA driver, the CUDA libraries and the product's own services are untouched. After the tuning is applied and the instance reboots, the kernel command line is byte for byte identical to the base image and the network interface keeps its original name, both verified on a GPU instance.

    AWS Network & Kernel Tuning:

    1. Network Stack: TCP BBR congestion control with fair queueing, increased connection backlogs, raised socket buffer ceilings, and MTU probing enabled. Ceilings are only ever raised, never lowered.

    2. System Tuning: tuned daemon active with an aws-optimized profile whose bootloader section is deliberately empty, and modified memory writeback parameters.

    3. Reliability: The systemd journal is capped at 200 MB so logs cannot fill the root filesystem, and superseded kernels are cleaned up automatically.

    Operational Tools:

    1. Preinstalled Tools: AWS SSM Agent active, CloudWatch Agent installed (disabled by default).

    Maintenance:

    1. Maintenance Specifications: Rebuilt and updated bi-weekly to monthly incorporating upstream Ollama and Ubuntu 24.04 security updates, with artifact documentation refreshed per release.

    About Ollama

    A widely adopted open-source runtime that packages large language models into a single self-contained server with a pull-and-run workflow and an OpenAI-compatible REST API. It pairs with chat front-ends and low-code automation platforms such as Open WebUI, LibreChat, Dify, Langflow and n8n, and backs RAG pipelines built on Milvus, Chroma, PostgreSQL and Redis.

    About Ubuntu 24.04 LTS

    The most widely deployed Linux distribution in the cloud, built on the Debian foundation, with a five-year maintenance window for LTS releases and broad ecosystem compatibility across the AI and container tooling that GPU workloads depend on.

    Highlights

    • Ollama 0.31 GPU on an AWS-Optimized Ubuntu 24.04 Base: 19 security-related kernel parameters, 12 blocked modules and a tightened SSH login window applied on top, with the NVIDIA driver, CUDA stack and kernel command line left untouched.
    • Verifiable Evidence Pack and SBOM: An 8-file evidence pack ships inside the image, with an SPDX Software Bill of Materials, a CVE scan grouped by upstream fix availability, a package delta report, and SHA-256 checksums of every file the image changed.
    • Ollama 0.31: Best-practice deployment. The qwen3:4b-instruct model is baked in and prewarmed at boot, the API is published through nginx on port 8000, and the image is rebuilt bi-weekly to monthly with upstream security patches.

    Details

    Delivery method

    Delivery option
    64-bit (x86) Amazon Machine Image (AMI)

    Latest version

    Operating system
    Ubuntu 24.04

    Deployed on AWS
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    Pricing

    Hardened Ollama GPU on Ubuntu 24.04

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    Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time. Alternatively, you can pay upfront for a contract, which typically covers your anticipated usage for the contract duration. Any usage beyond contract will incur additional usage-based costs.
    Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator  to estimate your infrastructure costs.
    If you are an AWS Free Tier customer with a free plan, you are eligible to subscribe to this offer. You can use free credits to cover the cost of eligible AWS infrastructure. See AWS Free Tier  for more details. If you created an AWS account before July 15th, 2025, and qualify for the Legacy AWS Free Tier, Amazon EC2 charges for Micro instances are free for up to 750 hours per month. See Legacy AWS Free Tier  for more details.

    Usage costs (791)

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    • ...
    Dimension
    Cost/hour
    g4dn.xlarge
    Recommended
    $0.19
    t2.micro
    $0.03
    t3.micro
    $0.03
    t2.nano
    $0.03
    t3.nano
    $0.00
    t3a.nano
    $0.03
    t1.micro
    $0.03
    t3a.micro
    $0.03
    m1.small
    $0.04
    t2.small
    $0.04

    Vendor refund policy

    No refunds. Cancel anytime.

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    Legal

    Vendor terms and conditions

    Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA) .

    Content disclaimer

    Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.

    Usage information

     Info

    Delivery details

    64-bit (x86) Amazon Machine Image (AMI)

    Amazon Machine Image (AMI)

    An AMI is a virtual image that provides the information required to launch an instance. Amazon EC2 (Elastic Compute Cloud) instances are virtual servers on which you can run your applications and workloads, offering varying combinations of CPU, memory, storage, and networking resources. You can launch as many instances from as many different AMIs as you need.

    Version release notes

    Latest Updates

    Additional details

    Usage instructions

    Connection Methods

    SSH is not required to use this product. The API key equals this instance's EC2 Instance ID, which is visible in the AWS console, so the inference API can be called as soon as the instance is ready. If you do want shell access, SSH into the instance; the default username is 'ubuntu'. You can switch to the root user environment by running: sudo su -

    Install Information

    • OS: Ubuntu 24.04 LTS (x86_64, Minimal Installation)
    • Ollama: 0.31 (GPU-accelerated, main binary at /usr/local/bin/ollama)
    • Pre-loaded Model: qwen3:4b-instruct, stored in /usr/share/ollama/.ollama on the root volume - nothing is downloaded at runtime
    • Runtime: NVIDIA GPU driver and CUDA stack pre-installed. This product requires an NVIDIA GPU instance such as g4dn.xlarge or larger.
    • Reverse Proxy: nginx, site config at '/etc/nginx/sites-available/ollama-api'
    • Ports: 8000 serves the outward-facing OpenAI-compatible API through nginx; the native Ollama port 11434 is bound to 127.0.0.1 only and is not reachable from outside the instance
    • Config File: '/etc/ollama/api-key' (holds the current API key, refreshed at every boot)
    • README: '/home/ubuntu/readme-ollama'

    Usage Instructions

    1. Launch on an NVIDIA GPU instance. Recommended: g4dn.xlarge or larger. Open port 8000 in the Security Group so clients can reach the API.
    2. Allow up to about 5 minutes on first boot before the API responds (measured: 280 seconds from instance start to the first working request). The instance prewarms the model into memory during startup. This prewarm runs on every boot, not only the first one - after each stop and start it takes roughly 20 seconds.
    3. Retrieve your API key: the key equals this instance's EC2 Instance ID, visible in the AWS console, so no SSH login is needed to obtain it. On the instance you can also read it with: cat /etc/ollama/api-key
    4. Send your first request to the OpenAI-compatible chat completions endpoint, substituting your instance IP and instance ID: curl http://<instance-ip>:8000/v1/chat/completions -H "Authorization: Bearer <instance-id>" -H 'Content-Type: application/json' -d '{"model":"qwen3:4b-instruct","messages":[{"role":"user","content":"Hello"}]}'
    5. Chat from the instance shell (optional, requires SSH): ollama run qwen3:4b-instruct
    6. Manage the service with: sudo systemctl start/stop/restart/status ollama, and the same commands for nginx. Two helper units also run at boot: ollama-apikey refreshes the API key, and ollama-prewarm loads the model into memory.
    7. View logs: Ollama has no separate log file and writes to the systemd journal - sudo journalctl -u ollama
    8. Full documentation, including ready-to-copy request examples, is available on the instance at /home/ubuntu/readme-ollama

    Firewall Configuration

    • SSH (Port 22): Optional - needed only to read the full README on the instance or to run the local command-line chat; the inference API itself does not require SSH.
    • Ollama API via nginx (Port 8000): Required - serves the outward-facing Chat and API traffic over the OpenAI-compatible protocol; restrict to trusted IPs.
    • Security Recommendation: For production environments, strictly limit access to these ports to trusted IP addresses only via cloud Security Groups or the local firewall.

    Support

    Vendor support

    Should you encounter any issues while using the system, please do not hesitate to contact us via email at: support@easyclouds.io ,Thank you!

    AWS infrastructure support

    AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.

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