> ## Documentation Index
> Fetch the complete documentation index at: https://costhq.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Local Model Tracking: Ollama, llama.cpp, vLLM, LM Studio

> Monitor Ollama compute tracking and local AI GPU costs. Register self-hosted LLMs with a $/hr GPU rate and track usage for vLLM, llama.cpp, and LM Studio.

When you run AI models locally — on Ollama, llama.cpp, vLLM, LM Studio, or any other self-hosted inference server — there is no per-token API bill. Your real cost is electricity and GPU time. CostHQ lets you register local models with a **cost-per-hour** rate so you can track compute costs alongside your cloud API spend in the same dashboard.

## Quick start

```bash theme={null}
# 1. Register a local model with your GPU's hourly compute cost
cs local-models add ollama/llama3 --cost-per-hour 0.50 --gpu "RTX 4090"

# 2. Start a session as usual
cs start "Local AI work"

# 3. Log usage with --duration instead of --cost
cs log-ai -p ollama -m llama3 --tokens 5000 --duration 2m30s

# 4. Check the session — cost is computed from duration × rate
cs status
```

## Cost formula

```
cost = (durationSeconds / 3600) × costPerHour
```

For example, 2 minutes 30 seconds on a model registered at \$0.50/hr:

```
(150 / 3600) × 0.50 = $0.0208
```

## Managing local models

### Register a model

```bash theme={null}
cs local-models add <provider>/<model> --cost-per-hour <rate> [--gpu <name>] [--notes <text>]
```

| Parameter                | Description                                                      |
| ------------------------ | ---------------------------------------------------------------- |
| `<provider>/<model>`     | Provider and model name, e.g. `ollama/llama3`, `vllm/mistral-7b` |
| `--cost-per-hour <rate>` | USD per hour of GPU compute                                      |
| `--gpu <name>`           | Optional GPU identifier (e.g. `"RTX 4090"`, `"M2 Ultra"`)        |
| `--notes <text>`         | Optional notes                                                   |

### List registered models

```bash theme={null}
cs local-models list [--json]
```

### Remove a model

```bash theme={null}
cs local-models remove <provider>/<model>
```

## Auto-detect Ollama models

If you have Ollama running locally, CostHQ can scan `localhost:11434` and register all available models in one command:

```bash theme={null}
cs local-models detect --cost-per-hour 0.50 --gpu "RTX 4090"
```

This hits Ollama's `GET /api/tags` endpoint, pulls every loaded model name, and registers them all with the rate you specify.

## Logging local model usage

Use the standard `cs log-ai` command with the new `--duration` flag:

```bash theme={null}
# Duration accepts multiple formats
cs log-ai -p ollama -m llama3 --tokens 5000 --duration 120        # plain seconds
cs log-ai -p ollama -m llama3 --tokens 5000 --duration 2m30s      # human format
cs log-ai -p ollama -m llama3 --tokens 5000 --duration 1h         # hours
cs log-ai -p ollama -m llama3 --tokens 5000 --duration 1h30m      # combined
```

### The `--local` flag

CostHQ auto-recognizes these providers as local: `ollama`, `llamacpp`, `llama.cpp`, `vllm`, `lmstudio`, `localai`, `jan`, `koboldcpp`. If your provider isn't in that list, use the `--local` flag to explicitly tell CostHQ to use compute-time costing:

```bash theme={null}
cs log-ai -p my-custom-server -m meta/llama-3-70b --tokens 8000 --local --duration 3m
```

## JSON output

When you log a local model call with `--json`, the `pricing` object shows `source: "local"`:

```json theme={null}
{
  "logged": {
    "provider": "ollama",
    "model": "llama3",
    "tokens": 10000,
    "cost": 0.0416666667
  },
  "pricing": {
    "source": "local",
    "modelKnown": true,
    "inputPer1M": 0,
    "outputPer1M": 0,
    "costPerHour": 0.5,
    "durationSeconds": 300
  }
}
```

## Estimating your GPU cost per hour

If you're not sure what `--cost-per-hour` rate to use, here are some reference points:

| Hardware                    | Estimated \$/hr | Source                              |
| --------------------------- | --------------- | ----------------------------------- |
| RTX 4090 (electricity only) | $0.10–$0.20     | \~450W TDP × local electricity rate |
| RTX 4090 (cloud rental)     | $0.40–$0.80     | RunPod, Vast.ai spot pricing        |
| A100 80GB (cloud)           | $1.50–$3.00     | AWS, GCP, Azure on-demand           |
| M2 Ultra (Mac Studio)       | $0.05–$0.15     | Apple Silicon power draw            |
| H100 (cloud)                | $2.50–$5.00     | AWS, GCP on-demand                  |

<Tip>
  Pick a rate that makes sense for your setup. You can always update it later with `cs local-models add` — it upserts, so re-registering the same model replaces the old rate.
</Tip>

<Note>
  Local model configurations are stored in `~/.costhq/local-models.json`. This file is independent from the session database and can be version-controlled or shared across machines.
</Note>