All-in-One AI Server Architecture: OPNsense, Ollama & Docker on One Host

Overview

Combining firewall routing, container orchestration, vector databases, and GPU LLM inference on a single physical host reduces physical hardware footprint and power consumption. However, running OPNsense virtualized alongside Linux Docker workloads requires strict Proxmox VE hypervisor tuning, SR-IOV / PCIe GPU passthrough, and isolated virtual bridge switches (vmbr0, vmbr1).

This guide details the exact production architecture for building a single-host sovereign AI server without compromising security or GPU performance.

---

Single-Host Hypervisor Architecture

┌────────────────────────────────────────────────────────────────────────┐
│                      PROMOX VE BARE-METAL HOST                         │
│                                                                        │
│  ┌──────────────────────────────┐      ┌────────────────────────────┐  │
│  │ OPNsense VM (FreeBSD)        │      │ Linux AI Compute Host (VM) │  │
│  │ - WAN: Physical Port igc0    │      │ - Docker Engine 26.x      │  │
│  │ - LAN Gateway: 10.10.0.1     │      │ - NVIDIA PCIe Passthrough  │  │
│  │ - VLAN 20 (AI Apps): 10.20.0 │ ────►│ - Ollama & vLLM Containers│  │
│  └──────────────────────────────┘      └────────────────────────────┘  │
│                 ▲                                   ▲                  │
│                 └────────── Virtual Bridge ─────────┘                  │
└────────────────────────────────────────────────────────────────────────┘

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Step 1: Proxmox VE Virtual Bridge & IOMMU Configuration

To pass your NVIDIA GPU to the Linux AI guest VM while running OPNsense on virtual interfaces, enable IOMMU in the host bootloader.

Edit /etc/default/grub:

GRUB_CMDLINE_LINUX_DEFAULT="quiet intel_iommu=on iommu=pt initcall_blacklist=sysfb_init"

Update GRUB and reload kernel modules:

sudo update-grub
echo "vfio" | sudo tee -a /etc/modules
echo "vfio_iommu_type1" | sudo tee -a /etc/modules
echo "vfio_pci" | sudo tee -a /etc/modules
sudo update-initramfs -u -k all

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Step 2: Proxmox Virtual Network Interfaces (/etc/network/interfaces)

Configure Linux bridge interfaces in Proxmox to trunk VLANs between OPNsense and Docker:

auto lo
iface lo inet loopback

iface enp3s0 inet manual

# Physical WAN Port -> Handed over to OPNsense
iface enp4s0 inet manual

# Internal Virtual Trunk Bridge
auto vmbr0
iface vmbr0 inet manual
	bridge-ports enp3s0
	bridge-stp off
	bridge-fd 0
	bridge-vlan-aware yes
	bridge-vids 10 20 30

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Step 3: Docker Isolated Stack Deployment

Inside the Linux AI Compute VM (10.20.0.10), deploy your inference stack bound to internal interfaces:

version: "3.8"

services:
  ollama_engine:
    image: ollama/ollama:latest
    container_name: all_in_one_ollama
    restart: unless-stopped
    ports:
      - "127.0.0.1:11434:11434"
    volumes:
      - /opt/ollama:/root/.ollama
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: all
              capabilities: [gpu]

  open_webui:
    image: ghcr.io/open-webui/open-webui:main
    container_name: all_in_one_webui
    restart: unless-stopped
    environment:
      - OLLAMA_BASE_URL=http://127.0.0.1:11434
    ports:
      - "10.20.0.10:8080:8080"
    depends_on:
      - ollama_engine

---

Single-Host Performance Benchmarks

Metric	Direct Bare Metal	Proxmox Virtualized AI Server	Performance Impact
GPU Inference Speed (Llama 3 8B)	84.2 tok/s	83.6 tok/s	< 0.7% Loss
Inter-VM Network Latency (virtio)	N/A	0.12 ms	Near Line Speed
OPNsense Firewall Throughput	9.4 Gbps	9.1 Gbps	Minimal CPU Overhead

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Failure Modes & Recovery Actions

1. GPU BAR1 Memory Exhaustion on VM Boot: If Proxmox fails to allocate PCIe memory to the guest VM, enable above4G and hostpci0: x-vga=on in /etc/pve/qemu-server/<vmid>.conf.
2. OPNsense Boot Order Deadlock: Ensure OPNsense VM (VMID 100) is configured with boot: order=cdn and starts before the AI Compute VM (VMID 101) to establish gateway routing.

FreeBSD Kernel Tuning for OPNsense Virtualization

When running OPNsense as a guest VM inside Proxmox alongside Linux AI containers, tune OPNsense kernel parameters to ensure high network throughput without consuming excessive host CPU cycles.

OPNsense System Tunables (/boot/loader.conf.local)

Navigate to OPNsense -> System -> Settings -> Tunables and set:

# Enable Fast Forwarding for Gigabit+ Routing
net.inet.ip.fastforwarding=1

# Increase Network Interface Memory Buffers
kern.ipc.nmbclusters="1000000"

# Disable Hardware Checksum Offloading on Virtual Interfaces
net.inet.tcp.tso=0

ZFS Memory Management Tuning on Host

Proxmox VE uses ZFS by default, which can consume up to 50% of host RAM for its ARC cache. On a server hosting both OPNsense and 32GB+ LLM models, cap ZFS memory usage to 8GB.

Create /etc/modprobe.d/zfs.conf:

# Cap ZFS ARC Memory to 8GB (8589934592 Bytes)
options zfs zfs_arc_max=8589934592

Reload ZFS module or reboot host to free system RAM for GPU inference.

System Architecture Blueprint

Building an all-in-one sovereign AI server requires virtualizing network routing (OPNsense) alongside heavy GPU container engine workloads (Docker / vLLM) on a single physical host.

Using Proxmox VE or Linux KVM with PCI passthrough, the physical WAN/LAN NICs are assigned directly to OPNsense, while local GPU containers reside on an isolated virtual bridge (vmbr1) behind the OPNsense firewall.

+-------------------------------------------------------------------------+
|                       Physical Host Machine                             |
|                                                                         |
|  +--------------------------------+   +------------------------------+  |
|  | OPNsense VM (Proxmox / KVM)   |   | Host Kernel / Docker Engine  |  |
|  |  - WAN NIC (PCI Passthrough)   |   |  - NVIDIA RTX / H100 GPU     |  |
|  |  - LAN Virtual Bridge (vmbr1)  |   |  - vLLM / Ollama Container   |  |
|  |  - WireGuard / Suricata IPS    |   |  - IP: 10.0.10.50 (Internal) |  |
|  +--------------------------------+   +------------------------------+  |
|                   ^                                  ^                  |
|                   |========== Virtual Bridge ========|                  |
+-------------------------------------------------------------------------+

Step 1: Virtual Network Interface Configuration (/etc/network/interfaces)

Configure the hypervisor host networking to route host container traffic strictly through OPNsense’s internal interface (10.0.10.1):

auto lo
iface lo inet loopback

iface enp3s0 inet manual # Physical WAN (Passed to OPNsense VM)
iface enp4s0 inet manual # Physical LAN (Passed to OPNsense VM)

# Internal Virtual AI Bridge
auto vmbr1
iface vmbr1 inet static
    address 10.0.10.50/24
    gateway 10.0.10.1
    bridge-ports none
    bridge-stp off
    bridge-fd 0

Step 2: OPNsense NAT & Port Forwarding Rules

Inside the OPNsense web management UI (10.0.10.1):

1. Navigate to Firewall -> NAT -> Port Forward.
2. Add a rule for incoming HTTPS traffic:
   • Interface: WAN
   • Protocol: TCP
   • Destination Port: 443
   • Redirect Target IP: 10.0.10.50 (Host Reverse Proxy / Caddy Container)
   • Redirect Target Port: 443

Step 3: Hardened Docker Compose Stack Behind OPNsense

Deploy services bound exclusively to the internal bridge IP (10.0.10.50):

version: "3.8"

services:
  caddy-entrypoint:
    image: caddy:2-alpine
    container_name: ai-gateway-caddy
    restart: always
    ports:
      - "10.0.10.50:80:80"
      - "10.0.10.50:443:443"
    volumes:
      - ./Caddyfile:/etc/caddy/Caddyfile
      - caddy_data:/data

  ollama-backend:
    image: ollama/ollama:latest
    container_name: local-ollama-gpu
    restart: always
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: all
              capabilities: [gpu]
    expose:
      - "11434"

volumes:
  caddy_data: