Tuesday, September 15, 2026

How to setup and use a local ollama server running on a windows 11 machine, from any machine in a local area network

 ## 1. Install Ollama on the Windows 11 host

Download and install Ollama on the machine with the GPU/CPU you want to use. Pull a model and verify it locally:

```powershell

ollama pull llama3.2

ollama run llama3.2

```

Ollama normally listens only on `127.0.0.1:11434`. To permit LAN clients, configure it to listen on all interfaces with `OLLAMA_HOST=0.0.0.0:11434`. [1,2]

## 2. Configure `OLLAMA_HOST`

On the Windows host:

1. Open **Settings → System → About → Advanced system settings**.

2. Select **Environment Variables**.

3. Under **User variables**, select **New**.

4. Enter:

   - **Variable name:** `OLLAMA_HOST`

   - **Variable value:** `0.0.0.0:11434`

5. Click **OK** through all dialogs.

6. Exit Ollama completely from the system tray, then relaunch it.

If Ollama runs as a service rather than as the desktop application, restart that service after changing the variable.

`OLLAMA_ORIGINS` is only relevant to browser-based cross-origin requests; it does not make the server reachable over the network. Avoid setting it to `*` unless you specifically need browser access from arbitrary origins.

## 3. Find the Windows host’s LAN IP address

On the Windows machine, run:

```powershell

ipconfig

```

Find the **IPv4 Address** for the active Wi-Fi or Ethernet adapter. It will usually look like:

```text

192.168.1.50

```

Use this address from other machines—not `localhost`, `127.0.0.1`, or `0.0.0.0`.

It is useful to reserve this address in your router’s DHCP settings so it does not change.

## 4. Allow TCP port 11434 through Windows Firewall

Open **PowerShell as Administrator** and create a rule limited to private networks:

```powershell

New-NetFirewallRule `

  -DisplayName "Ollama LAN API" `

  -Direction Inbound `

  -Protocol TCP `

  -LocalPort 11434 `

  -Action Allow `

  -Profile Private

```

Make sure the network is classified as **Private**, not Public:

**Settings → Network & internet → Wi-Fi/Ethernet → your network → Network profile → Private**

Do not expose port 11434 through your router or enable WAN/Internet port forwarding. Ollama’s local API generally should not be placed directly on the public Internet.

## 5. Test the server locally

On the Windows host:

```powershell

curl http://127.0.0.1:11434/api/tags

```

You should receive JSON listing installed models.

You can also test using the host’s LAN address:

```powershell

curl http://192.168.1.50:11434/api/tags

```

Replace `192.168.1.50` with the actual address.

## 6. Test from another LAN computer

From another machine on the same network:

```bash

curl http://192.168.1.50:11434/api/tags

```

A successful response confirms that the remote machine can reach Ollama and see its models. Both machines must be on the same reachable LAN; guest Wi-Fi and client-isolation settings can block this connection.

## 7. Call the model remotely

Example using the Ollama REST API:

```bash

curl http://192.168.1.50:11434/api/generate \

  -H "Content-Type: application/json" \

  -d '{

    "model": "llama3.2",

    "prompt": "Explain photosynthesis in two sentences.",

    "stream": false

  }'

```

For chat requests:

```bash

curl http://192.168.1.50:11434/api/chat \

  -H "Content-Type: application/json" \

  -d '{

    "model": "llama3.2",

    "messages": [

      {

        "role": "user",

        "content": "Write a short welcome message."

      }

    ],

    "stream": false

  }'

```

## 8. Use it from applications

Many Ollama-compatible applications have a **custom endpoint**, **base URL**, or **Ollama host** setting. Enter:

```text

http://192.168.1.50:11434

```

For OpenAI-compatible applications, the base URL is often:

```text

http://192.168.1.50:11434/v1

```

The exact setting depends on the application. In Python, an Ollama-compatible client commonly uses:

```python

from ollama import Client

client = Client(host="http://192.168.1.50:11434")

response = client.chat(

    model="llama3.2",

    messages=[

        {"role": "user", "content": "Give me three project ideas."}

    ],

)

print(response["message"]["content"])

```

## Troubleshooting

- **Local test works, remote test fails:** check the Windows Firewall rule and confirm the network is set to Private.

- **`curl` connects to `127.0.0.1` but not the LAN IP:** Ollama did not restart after setting `OLLAMA_HOST`, or it is still bound only to loopback.

- **Check what is listening:**

  ```powershell

  netstat -ano | findstr :11434

  ```

  You want to see `0.0.0.0:11434` or the machine’s LAN address, rather than only `127.0.0.1:11434`.

- **Connection times out:** check that both devices are on the same subnet and that Wi-Fi client isolation is disabled.

- **Model not found:** pull the model on the Windows host:

  ```powershell

  ollama pull llama3.2

  ```

- **Using WSL2 or Docker:** networking may require additional port forwarding because WSL2 uses a virtualized network adapter; a normal Windows installation is simpler for LAN access. [1,3]

The essential configuration is:

```text

OLLAMA_HOST=0.0.0.0:11434

```

Then connect from other machines using:

```text

http://<Windows-LAN-IP>:11434

```

Read More

[1] [WSL 2] Exposing ollama via 0.0.0.0 on local network #1431 - GitHub — https://github.com/ollama/ollama/issues/1431

[2] Impossible to connect to ollama locally from another pc #6571 - GitHub — https://github.com/ollama/ollama/issues/6571

[3] [WSL2] Ollama on Windows host unreachable from Docker ... - GitHub — https://github.com/ollama/ollama/issues/14947

Most affordable and practical new apple max studio for AI and creative work recommendation

 Assuming U.S. pricing, buy the **new Mac Studio with M5 Max, 18-core CPU, 40-core GPU, 64GB unified memory, and 1TB SSD**.

It is the most practical balance for AI-assisted creative work:

- **AI:** local image generation, transcription, coding assistants, and medium-sized local language models

- **Creative work:** 4K/8K video editing, Photoshop, Lightroom, audio production, motion graphics, and many professional apps

- **Memory:** 64GB gives substantially more headroom than the base 36GB configuration

- **Storage:** 1TB is usable; keep large media libraries and project archives on a fast external Thunderbolt SSD

- **GPU:** the 40-core option is worthwhile for video effects, 3D, and generative AI

Apple’s current M5 Max Mac Studio starts at **$2,499** with 36GB memory and 512GB storage; the 40-core GPU upgrade starts with 48GB memory and costs more. Apple lists M5 Max configurations up to 128GB unified memory. [1,2,3]

### What I would buy

| Component | Recommendation |

|---|---|

| Chip | M5 Max |

| CPU/GPU | 18-core CPU, 40-core GPU |

| Unified memory | 64GB if available; 48GB if keeping costs down |

| Storage | 1TB internal SSD |

| External storage | 2–4TB Thunderbolt SSD for active media |

| Display | Use your existing monitor initially |

**Budget-conscious option:** M5 Max, 32-core GPU, 36GB memory, 512GB SSD. It is adequate for general creative work and cloud-based AI, but I would avoid it if you expect to run local AI models regularly.

**Step up to 128GB** only if you specifically plan to run larger local language models, several demanding apps simultaneously, very large video projects, or heavy 3D scenes. The M5 Max supports up to 128GB, while the M5 Ultra goes much higher, but Ultra pricing is difficult to justify for ordinary creative work. [1,2]

One important limitation: Apple silicon Macs do not support NVIDIA CUDA, so choose a Windows/Linux workstation with an NVIDIA GPU instead if your AI workflow depends on CUDA-specific software or training large models locally. For Apple-optimized tools such as MLX, Core ML, image generation, video enhancement, and creative applications, the M5 Max is a strong practical choice. Apple reports major M5 Max gains over M4 Max in local-model prompt processing and AI image generation, though those are manufacturer test results. [2,3]

Read More

[1] Shop Mac Studio — https://www.apple.com/shop/buy-mac/mac-studio

[2] Mac Studio — https://www.apple.com/mac-studio/

[3] M5 Max Mac Studio vs. M4 Max Mac Studio: Specs, cost — https://appleinsider.com/articles/26/08/26/m5-max-mac-studio-vs-m4-max-mac-studio-faster-more-expensive


Tuesday, September 1, 2026

LLM Models Benchmark Results

 Below are the models l currently have under ollama, on my workstation which runs the AMD Ryzen 9 3900X 12-Core processor at a base speed of 3.8Ghz running at around 4.12Ghz with 64Gb DDR4 RAM and has an Nvidia RTX 3090 GPU with 24Gb VRAM.







Benchmark python code:









Results:











Run #2 results




Python Setup Code

Code l use most of the time to setup my python environment: This also allows me to do AI PDF LLM RAG processing python -m venv myenv pip ins...