# Llmfit Advisor > Detect local hardware (RAM, CPU, GPU/VRAM) and recommend the best-fit local LLM models with optimal quantization, speed estimates, and fit scoring. Use when this capability is needed. - Skill: `tomevault-io/llmfit-advisor` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/llmfit-advisor` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/llmfit-advisor/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/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", "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` | | `category` | Model category (e.g. `Reasoning`, `Coding`, `Chat`, `Embedding`) | | `is_moe` | Whether the model uses Mixture of Experts architecture | | `parameter_count` | Human-readable param count string (e.g. `"7.6B"`) | | `notes` | Array of human-readable notes about the recommendation | | `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 - **Perfect**: Model fits comfortably with room to spare. Ideal choice. - **Good**: Model fits but uses most available memory. Will work well. - **Marginal**: Model barely fits. May work but expect slower performance or reduced context. - **TooTight**: Model does not fit. Do not recommend. ### Run modes explained - **GPU**: Full GPU inference. Fastest. Model weights loaded entirely into VRAM. - **CPU+GPU Offload**: Some layers on GPU, rest in system RAM. Slower than pure GPU. - **CPU**: All inference on CPU using system RAM. Slowest but works without GPU. ## 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`: ```json { "models": { "providers": { "ollama": { "models": ["ollama/"] } } } } ``` And optionally set as default: ```json { "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 - llmfit detects NVIDIA GPUs (via nvidia-smi), AMD GPUs (via rocm-smi), and Apple Silicon (unified memory). - Multi-GPU setups aggregate VRAM across cards automatically. - The `best_quant` field tells you the optimal quantization — higher quant (Q6_K, Q8_0) means better quality if VRAM allows. - Speed estimates (`estimated_tps`) are approximate and vary by hardware and quantization. - Models with `fit_level: "TooTight"` should never be recommended to users. --- > Source: [AlexsJones/llmfit](https://github.com/AlexsJones/llmfit) — distributed by [TomeVault](https://tomevault.io).