{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"npm-llm-checker","slug":"llm-checker","name":"llm-checker","type":"cli","url":"https://github.com/Pavelevich/llm-checker#readme","page_url":"https://unfragile.ai/llm-checker","categories":["cli-tools"],"tags":["llm","ai","intelligent-selector","model-recommendation","hardware-analysis","machine-learning","hardware","compatibility","cli","ollama","small-language-models","sllm","local-ai","quantization","inference","gpu","vram","performance","benchmark","apple-silicon"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"npm-llm-checker__cap_0","uri":"capability://data.processing.analysis.hardware.capability.analysis.and.profiling","name":"hardware-capability-analysis-and-profiling","description":"Analyzes system hardware specifications (CPU, GPU, RAM, VRAM, architecture type) by querying OS-level APIs and device information to build a hardware profile. The tool detects GPU presence (NVIDIA CUDA, Apple Metal, AMD ROCm), measures available memory, identifies CPU architecture (x86, ARM), and determines system constraints that impact LLM inference performance. This profiling data becomes the input for model recommendation algorithms.","intents":["I need to know what LLM models my machine can actually run without crashing or extreme slowdown","I want to understand my hardware constraints before downloading a 7B or 13B parameter model","I need to detect if my system has GPU acceleration available and what type (NVIDIA, Apple Silicon, AMD)"],"best_for":["developers setting up local LLM inference environments","DevOps engineers evaluating hardware for on-premise LLM deployment","non-technical users trying to run open-source models locally without trial-and-error"],"limitations":["Hardware detection is OS-specific; cross-platform support may have gaps for obscure GPU configurations","VRAM detection may be inaccurate on systems with shared GPU/system memory (integrated graphics)","Does not account for thermal throttling, power limits, or dynamic frequency scaling that affect real-world performance"],"requires":["Node.js 14+ (for CLI execution)","OS-level access to hardware information APIs (sysctl on macOS, /proc on Linux, WMI on Windows)","No external API keys required"],"input_types":["system environment (implicit — reads from OS)"],"output_types":["structured JSON object with hardware specs","human-readable hardware profile summary"],"categories":["data-processing-analysis","hardware-profiling"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-llm-checker__cap_1","uri":"capability://planning.reasoning.ai.powered.model.recommendation.engine","name":"ai-powered-model-recommendation-engine","description":"Uses an LLM (likely Claude or GPT via API) to analyze the hardware profile and recommend optimal open-source models from registries like Ollama, Hugging Face, or GGUF repositories. The engine considers hardware constraints (VRAM, CPU cores, GPU type), user preferences (latency vs quality), and model characteristics (parameter count, quantization format, inference speed benchmarks) to generate ranked recommendations with justifications. Recommendations are filtered by compatibility (e.g., only suggesting GGUF-quantized models if the system lacks GPU acceleration).","intents":["I want an AI to tell me which specific model (e.g., Mistral 7B, Llama 2 13B) will work best on my hardware","I need recommendations ranked by performance/quality tradeoff for my specific constraints","I want to understand WHY a model is recommended, not just get a list of options"],"best_for":["developers new to local LLM deployment who lack domain knowledge about model selection","teams evaluating multiple hardware configurations for LLM inference","non-technical stakeholders who need data-driven model recommendations"],"limitations":["Recommendation quality depends on the underlying LLM's training data; may not include very recent models (post-training cutoff)","Requires API access to an LLM service (OpenAI, Anthropic, etc.), adding latency and cost per recommendation","Cannot account for domain-specific model performance (e.g., code generation vs. chat quality) without explicit user input","Recommendations are static snapshots; do not adapt based on actual runtime performance feedback"],"requires":["API key for LLM service (OpenAI, Anthropic, or compatible)","Network connectivity to reach LLM API","Hardware profile from hardware-capability-analysis capability"],"input_types":["hardware profile (JSON object with CPU, GPU, VRAM specs)","optional user preferences (latency budget, quality tier, task type)"],"output_types":["ranked list of model recommendations (JSON)","human-readable recommendation report with justifications"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-llm-checker__cap_2","uri":"capability://search.retrieval.ollama.model.registry.integration","name":"ollama-model-registry-integration","description":"Queries the Ollama model registry (or compatible GGUF model repositories) to fetch available models, their parameter counts, quantization formats, and estimated VRAM requirements. The integration parses model metadata (e.g., 'mistral:7b-instruct-q4_0') to extract quantization level and architecture, then cross-references this against the hardware profile to filter compatible models. This enables real-time model availability checking and prevents recommending models that are unavailable or incompatible with the user's setup.","intents":["I want to see which models from Ollama are actually compatible with my hardware right now","I need to know the exact VRAM footprint of a model before downloading it","I want to compare quantization formats (Q4, Q5, FP16) and their performance tradeoffs for my system"],"best_for":["developers integrating local LLM inference into applications","DevOps engineers automating model deployment pipelines","users who want to avoid downloading incompatible or oversized models"],"limitations":["Ollama registry metadata may be incomplete or outdated; actual VRAM usage can vary based on batch size and context length","Does not account for quantization-specific performance characteristics (e.g., Q4 vs Q5 inference speed) without external benchmarks","Limited to Ollama and GGUF formats; does not support other quantization schemes (AWQ, GPTQ) without additional adapters"],"requires":["Network access to Ollama registry API or compatible model repository","Ollama installed locally (optional, for model pulling/running)","Hardware profile from hardware-capability-analysis capability"],"input_types":["hardware profile (JSON)","optional filter criteria (model family, parameter range, quantization preference)"],"output_types":["list of compatible models with metadata (JSON)","formatted model comparison table (text/markdown)"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-llm-checker__cap_3","uri":"capability://data.processing.analysis.quantization.format.compatibility.matching","name":"quantization-format-compatibility-matching","description":"Maps hardware capabilities (GPU type, VRAM, CPU architecture) to compatible quantization formats (GGUF Q4, Q5, Q6, FP16, etc.) and determines which formats will run efficiently on the target system. For example, systems with limited VRAM (4-6GB) are matched to Q4 quantization, while systems with 16GB+ VRAM can run higher-quality Q6 or FP16 formats. The matching considers GPU acceleration support (CUDA for NVIDIA, Metal for Apple Silicon) and falls back to CPU inference for unsupported quantization formats.","intents":["I want to know which quantization level (Q4, Q5, Q6) is best for my GPU and VRAM","I need to understand the quality vs. speed tradeoff for different quantization formats on my hardware","I want to ensure a model will actually run without out-of-memory errors before downloading"],"best_for":["developers optimizing LLM inference latency and memory usage","teams deploying models across heterogeneous hardware (mix of GPUs and CPUs)","users trying to maximize model quality within strict VRAM constraints"],"limitations":["Quantization performance is non-linear; Q4 vs Q5 speedup depends on GPU architecture and is not fully predictable without benchmarking","Does not account for context length impact on VRAM usage; recommendations assume standard context windows","Quantization compatibility is format-specific; GGUF Q4 may not be compatible with all inference engines (llama.cpp vs. Ollama vs. vLLM)"],"requires":["Hardware profile with GPU type and VRAM capacity","Quantization format specifications (e.g., GGUF Q4_0, Q5_K_M)","Optional: benchmark data for quantization performance on target hardware"],"input_types":["hardware profile (JSON)","model metadata including quantization format"],"output_types":["compatibility matrix (JSON or table)","ranked quantization recommendations with estimated VRAM and speed"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-llm-checker__cap_4","uri":"capability://automation.workflow.cli.interactive.recommendation.workflow","name":"cli-interactive-recommendation-workflow","description":"Orchestrates a multi-step CLI workflow that guides users through hardware detection, preference input, model recommendation, and model selection. The workflow uses interactive prompts (e.g., 'What is your priority: speed or quality?') to gather user preferences, then chains together hardware analysis, LLM-powered recommendation, and registry lookup to produce a final model suggestion with download/run instructions. The workflow is designed for non-technical users and includes explanatory text at each step.","intents":["I want a guided, step-by-step process to find and set up the right LLM for my machine","I need clear explanations of what's happening at each stage (hardware detection, recommendation, compatibility check)","I want to get from 'I have no idea which model to use' to 'here's the command to download and run my model' in one CLI session"],"best_for":["non-technical users and hobbyists trying local LLM inference for the first time","teams onboarding new developers to local LLM setup","anyone who prefers guided workflows over reading documentation"],"limitations":["Interactive CLI is slower than programmatic API calls; not suitable for automation or batch processing","User preferences are collected via simple prompts; cannot capture complex requirements (e.g., 'I need a model optimized for code generation in Rust')","Workflow assumes sequential execution; cannot handle branching logic for advanced users who want to skip steps"],"requires":["Node.js 14+ with npm or yarn","Terminal/CLI environment with TTY support","API key for LLM service (for recommendation step)","Network connectivity"],"input_types":["user input via interactive prompts (text)","system environment (implicit)"],"output_types":["formatted recommendation report (text)","shell commands for model download/execution","optional: JSON export of recommendation for programmatic use"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-llm-checker__cap_5","uri":"capability://data.processing.analysis.apple.silicon.specific.optimization.detection","name":"apple-silicon-specific-optimization-detection","description":"Detects Apple Silicon (M1, M2, M3, M4) architecture and identifies optimized model variants and inference engines that leverage Metal GPU acceleration. The detection checks for ARM64 architecture, Metal framework availability, and recommends models with Metal-optimized GGUF quantizations or inference engines like llama.cpp with Metal support. This enables Apple Silicon users to achieve near-GPU performance on CPU-only inference without requiring NVIDIA CUDA.","intents":["I have an M1/M2 Mac and want to know which models and inference engines will use my GPU efficiently","I want to avoid downloading CUDA-optimized models that won't work on my Apple Silicon Mac","I need to understand the performance difference between CPU and Metal-accelerated inference on my Mac"],"best_for":["Apple Silicon Mac users (M1/M2/M3/M4) wanting local LLM inference","developers building LLM applications for macOS","teams with mixed hardware (Intel and Apple Silicon) needing cross-platform recommendations"],"limitations":["Metal optimization is engine-specific; not all inference engines support Metal equally well (llama.cpp has better Metal support than some alternatives)","Metal performance varies significantly based on model size and quantization; no universal speedup factor","Unified memory architecture on Apple Silicon means VRAM and system RAM are shared, complicating VRAM estimation"],"requires":["macOS 11+ (for Metal framework)","Apple Silicon CPU (M1 or later)","Optional: llama.cpp or Ollama with Metal support for actual inference"],"input_types":["system architecture detection (implicit)"],"output_types":["list of Metal-optimized models and inference engines","performance comparison (Metal vs CPU inference)","setup instructions for Metal-accelerated inference"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-llm-checker__cap_6","uri":"capability://data.processing.analysis.performance.benchmark.integration.and.estimation","name":"performance-benchmark-integration-and-estimation","description":"Integrates or estimates performance benchmarks (tokens per second, latency) for recommended models on the target hardware. The tool may query external benchmark databases (e.g., LLM benchmarks from Hugging Face or community sources) or use heuristic estimation based on model size, quantization level, and hardware specs (e.g., 'a 7B Q4 model on RTX 4090 typically achieves 100 tokens/sec'). Benchmarks help users understand real-world inference speed and make informed tradeoffs between model quality and latency.","intents":["I want to know how fast a recommended model will actually run on my hardware (tokens per second)","I need to understand if a model will meet my latency requirements (e.g., <100ms per token for real-time chat)","I want to compare inference speed across different quantization levels before committing to a download"],"best_for":["developers building latency-sensitive LLM applications (chatbots, real-time assistants)","teams evaluating hardware ROI based on inference throughput","users optimizing for specific performance targets (e.g., 'I need at least 50 tokens/sec')"],"limitations":["Benchmark data is often unavailable for new models or obscure hardware combinations; estimates may be inaccurate","Actual performance varies significantly based on batch size, context length, and system load; single-token estimates are not representative","Benchmarks are typically measured on idle systems; real-world performance under load may be 20-40% slower","Quantization-specific benchmarks are rare; most benchmarks are for FP16 or FP32 models"],"requires":["Hardware profile with GPU/CPU specs","Model metadata (parameter count, quantization format)","Optional: access to benchmark database or API"],"input_types":["hardware profile (JSON)","model metadata (parameter count, quantization)"],"output_types":["estimated tokens per second (number)","latency estimates (milliseconds per token)","performance comparison table (text/JSON)","confidence level for estimates (low/medium/high)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-llm-checker__cap_7","uri":"capability://automation.workflow.model.download.and.setup.instruction.generation","name":"model-download-and-setup-instruction-generation","description":"Generates platform-specific, copy-paste-ready commands and instructions for downloading and running recommended models. For Ollama models, it generates 'ollama pull' and 'ollama run' commands; for GGUF models, it generates llama.cpp or other inference engine setup instructions. Instructions include environment variable configuration, GPU acceleration setup (CUDA, Metal, ROCm), and optional Docker commands for containerized deployment. The output is tailored to the user's OS (macOS, Linux, Windows) and detected hardware.","intents":["I want a single command I can copy-paste to download and run my recommended model","I need step-by-step setup instructions for my specific OS and hardware (e.g., CUDA setup on Ubuntu)","I want to deploy the model in Docker without manually configuring GPU passthrough or environment variables"],"best_for":["non-technical users who want minimal setup friction","developers automating model deployment in CI/CD pipelines","teams deploying models across multiple machines with consistent configuration"],"limitations":["Generated commands assume standard configurations; may fail on non-standard setups (custom CUDA paths, proxy networks)","Docker instructions require Docker installation and GPU runtime support (nvidia-docker or similar)","Instructions are static; do not adapt to runtime errors or missing dependencies","Windows support may be incomplete for some inference engines (e.g., llama.cpp Metal support is macOS-only)"],"requires":["Recommended model metadata (name, source, quantization format)","Hardware profile with OS and GPU type","Optional: Ollama, llama.cpp, or other inference engine installed"],"input_types":["model recommendation (JSON)","hardware profile (JSON)"],"output_types":["shell commands (bash/zsh/PowerShell)","step-by-step setup guide (markdown or text)","Docker Compose file (optional)","environment variable configuration (shell or .env format)"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-llm-checker__cap_8","uri":"capability://tool.use.integration.multi.provider.llm.api.abstraction","name":"multi-provider-llm-api-abstraction","description":"Abstracts LLM API calls across multiple providers (OpenAI, Anthropic, Ollama local, etc.) with a unified interface for the recommendation engine. The abstraction handles provider-specific authentication, request formatting, and response parsing, allowing the recommendation logic to remain provider-agnostic. This enables users to choose their preferred LLM provider for recommendations without changing the tool's code, and supports fallback to local Ollama if API keys are unavailable.","intents":["I want to use my preferred LLM provider (OpenAI, Anthropic, local Ollama) for model recommendations","I need the tool to work offline or without API keys by falling back to a local LLM","I want to avoid vendor lock-in and be able to switch LLM providers without reconfiguring the tool"],"best_for":["developers building LLM-powered tools who want provider flexibility","teams with existing LLM provider relationships (e.g., already using Anthropic)","users in restricted environments who cannot access external APIs and need local-only inference"],"limitations":["Recommendation quality varies significantly across LLM providers; OpenAI and Anthropic may produce different recommendations","Local Ollama fallback requires a running Ollama instance and sufficient local VRAM to run the recommendation model","API rate limits and costs vary by provider; no built-in cost optimization or rate-limit handling","Provider-specific features (e.g., vision capabilities, structured output) are not exposed through the abstraction"],"requires":["API key for at least one LLM provider (OpenAI, Anthropic, etc.) OR local Ollama instance running","Network connectivity for API-based providers","Configuration file or environment variables specifying provider and credentials"],"input_types":["provider configuration (API key, endpoint URL)","recommendation request (hardware profile, user preferences)"],"output_types":["LLM response (text or structured JSON)","parsed recommendation (JSON)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":38,"verified":false,"data_access_risk":"high","permissions":["Node.js 14+ (for CLI execution)","OS-level access to hardware information APIs (sysctl on macOS, /proc on Linux, WMI on Windows)","No external API keys required","API key for LLM service (OpenAI, Anthropic, or compatible)","Network connectivity to reach LLM API","Hardware profile from hardware-capability-analysis capability","Network access to Ollama registry API or compatible model repository","Ollama installed locally (optional, for model pulling/running)","Hardware profile with GPU type and VRAM capacity","Quantization format specifications (e.g., GGUF Q4_0, Q5_K_M)"],"failure_modes":["Hardware detection is OS-specific; cross-platform support may have gaps for obscure GPU configurations","VRAM detection may be inaccurate on systems with shared GPU/system memory (integrated graphics)","Does not account for thermal throttling, power limits, or dynamic frequency scaling that affect real-world performance","Recommendation quality depends on the underlying LLM's training data; may not include very recent models (post-training cutoff)","Requires API access to an LLM service (OpenAI, Anthropic, etc.), adding latency and cost per recommendation","Cannot account for domain-specific model performance (e.g., code generation vs. chat quality) without explicit user input","Recommendations are static snapshots; do not adapt based on actual runtime performance feedback","Ollama registry metadata may be incomplete or outdated; actual VRAM usage can vary based on batch size and context length","Does not account for quantization-specific performance characteristics (e.g., Q4 vs Q5 inference speed) without external benchmarks","Limited to Ollama and GGUF formats; does not support other quantization schemes (AWQ, GPTQ) without additional adapters","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.14020893505720966,"quality":0.43,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.9,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:23.902Z","last_scraped_at":"2026-04-22T08:08:13.652Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":1262,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=llm-checker","compare_url":"https://unfragile.ai/compare?artifact=llm-checker"}},"signature":"ESsWCu5Ibl1ranYNUfof9L3N/Qw+lOPQzwxEet6IYDWZc/4MqJ08Eu56d8X5TUaGNwQKaPrjTuqiTQxSKBylCw==","signedAt":"2026-06-15T06:52:39.976Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/llm-checker","artifact":"https://unfragile.ai/llm-checker","verify":"https://unfragile.ai/api/v1/verify?slug=llm-checker","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}