llmfit-advisor

# llmfit-advisor

Hardware-aware local LLM advisor. Detects your system specs (RAM, CPU, GPU/VRAM) and recommends models that actually fit, with optimal quantization and speed estimates.

## When to use (trigger phrases)

Use this skill immediately when the user asks any of:

- "what local models can I run?"
- "which LLMs fit my hardware?"
- "recommend a local model"
- "what's the best model for my GPU?"
- "can I run Llama 70B locally?"
- "configure local models"
- "set up Ollama models"
- "what models fit my VRAM?"
- "help me pick a local model for coding"

Also use this skill when:

- The user wants to configure `models.providers.ollama` or `models.providers.lmstudio`
- The user mentions running models locally and you need to know what fits
- A model recommendation is needed and the user has local inference capability (Ollama, vLLM, LM Studio)

## Quick start

### Detect hardware

```bash
llmfit --json system
```

Returns JSON with CPU, RAM, GPU name, VRAM, multi-GPU info, and whether memory is unified (Apple Silicon).

### Get top recommendations

```bash
llmfit recommend --json --limit 5
```

Returns the top 5 models ranked by a composite score (quality, speed, fit, context) with optimal quantization for the detected hardware.

### Filter by use case

```bash
llmfit recommend --json --use-case coding --limit 3
llmfit recommend --json --use-case reasoning --limit 3
llmfit recommend --json --use-case chat --limit 3
```

Valid use cases: `general`, `coding`, `reasoning`, `chat`, `multimodal`, `embedding`.

### Filter by minimum fit level

```bash
llmfit recommend --json --min-fit good --limit 10
```

Valid fit levels (best to worst): `perfect`, `good`, `marginal`.

## Understanding the output

### System JSON

```json
{
  "system": {
    "cpu_name": "Apple M2 Max",
    "cpu_cores": 12,
    "total_ram_gb": 32.0,
    "available_ram_gb": 24.5,
    "has_gpu": true,
    "gpu_name": "Apple M2 Max",
    "gpu_vram_gb": 32.0,
    "gpu_count": 1,
    "backend": "Metal",...