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Likely uses pre-computed benchmarks or a lookup table of quantization effects across model families, allowing users to see exact VRAM requirements for each quantization variant.","intents":["I want to see how 4-bit quantization affects model quality vs memory savings for my specific model","I need to know the exact VRAM requirement for a model at different quantization levels","I'm deciding between running a larger model at 4-bit or a smaller model at full precision","I want to understand the inference speed trade-offs of different quantization strategies"],"best_for":["developers fine-tuning inference performance on constrained hardware","teams evaluating quantization libraries (bitsandbytes, GPTQ, AWQ)","researchers studying quantization impact on model behavior"],"limitations":["Quantization effects vary significantly by model architecture and task — comparisons may not generalize","Quality degradation is difficult to quantify universally; benchmark data may not reflect your specific use case","Does not account for quantization-specific framework requirements (e.g., GPTQ requires specific GPU architectures)","Inference speed benchmarks may not match real-world performance under production load"],"requires":["Model selection from the database","Hardware specifications for accurate memory calculations"],"input_types":["model identifier","hardware profile"],"output_types":["quantization comparison table (memory, speed, quality metrics)","visual charts showing trade-offs","framework compatibility per quantization level"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-runthisllm__cap_3","uri":"capability://tool.use.integration.inference.framework.compatibility.matrix","name":"inference framework compatibility matrix","description":"Maps which inference frameworks (llama.cpp, vLLM, Ollama, LM Studio, GPT4All, etc.) support each model, accounting for quantization format compatibility, hardware acceleration (CUDA, Metal, ROCm), and platform availability (macOS, Linux, Windows). Presents this as a queryable matrix showing which framework-model-quantization combinations are viable on the user's hardware.","intents":["I want to know which inference framework to use for my model on my specific hardware","I need to find frameworks that support both my GPU type and my preferred quantization format","I'm choosing between Ollama and vLLM — which one supports my model better?","I want to see all the ways I can run a specific model on my system"],"best_for":["developers selecting inference infrastructure for production deployments","teams evaluating framework trade-offs (ease of use vs performance vs flexibility)","DevOps engineers building containerized inference services"],"limitations":["Framework support changes frequently; compatibility data may become stale","Does not account for framework-specific performance characteristics or optimization quality","Some frameworks have platform-specific limitations not fully captured in a simple matrix","Does not model framework setup complexity or operational overhead"],"requires":["Hardware specifications (GPU type, OS, CUDA/Metal/ROCm availability)","Model and quantization format selection"],"input_types":["model identifier","quantization level","hardware profile"],"output_types":["compatibility matrix (framework × model × quantization)","framework feature comparison (speed, memory efficiency, ease of use)","installation/setup guidance per framework"],"categories":["tool-use-integration","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-runthisllm__cap_4","uri":"capability://planning.reasoning.hardware.upgrade.impact.simulation","name":"hardware upgrade impact simulation","description":"Projects how upgrading specific hardware components (GPU VRAM, system RAM, CPU cores) would expand the set of runnable models, showing before/after capability comparisons. 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