By name: hardware-calculator description: Quick VRAM/RAM calculations, hardware recommendations, feasibility checks for AI models short_desc: VRAM/RAM calc + GPU sizing for AI models keywords: [VRAM, "GPU memory", "RAM requirements", "model footprint", "GPU recommendation", "which GPU", "GPU sizing", "fit on GPU", "memory requirements", H100, A100, "RTX 4090", "consumer GPU"] model: haiku
Hardware Calculator (Haiku)
Purpose: Quick VRAM/RAM calculations, hardware recommendations, feasibility checks for AI models.
Model: Haiku 4.5
When to Invoke Autonomously
- "Can I run X?": User asks if model fits hardware
- Hardware Shopping: "Which GPU for [model]?"
- Quick VRAM Check: Before loading model
- Multi-Model Planning: "Can I run 2 models simultaneously?"
- Quantization Math: "How much VRAM saves Q4 vs Q8?"
DO NOT invoke for
- Complex architecture decisions (use /architect skill)
- Performance optimization (use /performance-optimizer)
- Already know hardware fits
What This Skill Does
VRAM Calculations:
- Calculate model VRAM requirements from parameters + quantization
- Account for context overhead and batch size
- Apply 20% safety margin
- Formula: (params × bytes_per_param × 1.2) + context + batch
GPU Recommendations:
- Match budget to appropriate GPU tier ($300-5000+)
- Match VRAM needs to GPU options (8-80GB range)
- Consider price/performance tradeoffs
- Warn about overspending or underpowered options
Feasibility Checks:
- Quick yes/no: Will model fit on GPU?
- Account for OS overhead (~2GB)
- Warn if <10% headroom (tight fit, unstable)
Multi-Model Planning:
- Calculate combined VRAM for running multiple models
- Suggest offloading strategies if tight
- Account for shared context when applicable
See: examples/calculations.md for formulas, examples/gpu-specs.md for hardware details, scripts/vram_calculator.py for automated calculations
Quick Workflow Reference
Before calculating: Search for hardware specs and benchmarks
.claude/scripts/kg-search search "hardware" --type hardware
For deep research: Ask user "Use hybrid_search to research [GPU comparison]"
Development env: Python 3.12, Weaviate:8081, Ollama:11435, venv: source claude_mcp_servers/.venv/bin/activate
Success Metrics
- ✅ VRAM estimates within ±1GB of actual usage
- ✅ GPU recommendations fit user's budget and needs
- ✅ Calculations complete in <2 seconds
- ✅ Users don't run into OOM errors after following recommendations
- ✅ Hardware purchases are successful (not over/under-powered)