llmfit

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:

Also use this skill when:

Quick start

Detect hardware

llmfit --json system

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

Get top recommendations

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

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

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

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

Understanding the output

System 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",
    "unified_memory": true
  }
}

Recommendation JSON

Each model in the models array includes:

| Field | Meaning | |---|---| | name | HuggingFace model ID (e.g. meta-llama/Llama-3.1-8B-Instruct) | | provider | Model provider (Meta, Alibaba, Google, etc.) | | params_b | Parameter count in billions | | score | Composite score 0–100 (higher is better) | | score_components | Breakdown: quality, speed, fit, context (each 0–100) | | fit_level | Perfect, Good, Marginal, or TooTight | | run_mode | GPU, CPU+GPU Offload, or CPU Only | | best_quant | Optimal quantization for the hardware (e.g. Q5_K_M, Q4_K_M) | | estimated_tps | Estimated tokens per second | | memory_required_gb | VRAM/RAM needed at this quantization | | memory_available_gb | Available VRAM/RAM detected | | utilization_pct | How much of available memory the model uses | | use_case | What the model is designed for | | context_length | Maximum context window |

Fit levels explained

Run modes explained

Configuring OpenClaw with results

After getting recommendations, configure the user's local model provider.

For Ollama

Map the HuggingFace model name to its Ollama tag. Common mappings:

| llmfit name | Ollama tag | |---|---| | meta-llama/Llama-3.1-8B-Instruct | llama3.1:8b | | meta-llama/Llama-3.3-70B-Instruct | llama3.3:70b | | Qwen/Qwen2.5-Coder-7B-Instruct | qwen2.5-coder:7b | | Qwen/Qwen2.5-72B-Instruct | qwen2.5:72b | | deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct | deepseek-coder-v2:16b | | deepseek-ai/DeepSeek-R1-Distill-Qwen-32B | deepseek-r1:32b | | google/gemma-2-9b-it | gemma2:9b | | mistralai/Mistral-7B-Instruct-v0.3 | mistral:7b | | microsoft/Phi-3-mini-4k-instruct | phi3:mini | | microsoft/Phi-4-mini-instruct | phi4-mini |

Then update openclaw.json:

{
  "models": {
    "providers": {
      "ollama": {
        "models": ["ollama/"]
      }
    }
  }
}

And optionally set as default:

{
  "agents": {
    "defaults": {
      "model": {
        "primary": "ollama/"
      }
    }
  }
}

For vLLM / LM Studio

Use the HuggingFace model name directly as the model identifier with the appropriate provider prefix (vllm/ or lmstudio/).

Workflow example

When a user asks "what local models can I run?":

1. Run llmfit --json system to show hardware summary 2. Run llmfit recommend --json --limit 5 to get top picks 3. Present the recommendations with scores and fit levels 4. If the user wants to configure one, map it to the appropriate Ollama/vLLM/LM Studio tag 5. Offer to update openclaw.json with the chosen model

When a user asks for a specific use case like "recommend a coding model":

1. Run llmfit recommend --json --use-case coding --limit 3 2. Present the coding-specific recommendations 3. Offer to pull via Ollama and configure

Notes

Detect hardware

llmfit --json system

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

Get top recommendations

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

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

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

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