Hey everyone! 👋 I built TheModelverse (https://www.themodelverse.in) to solve a daily headache when working with foundation models: figuring out which open-weights models will actually run on your hardware before spending hours downloading 50GB+ weights. Most quick formulas ignore KV-cache memory expansion at realistic context lengths. We built an interactive VRAM Calculator (https://www.themodelverse.in/vra...) that models: 1. Weight Footprint across 4 quantization tiers: • FP16 / BF16 (lossless reference, 2.0 GB/B params) • FP8 (modern Ada/Hopper datacenter standard, 1.0 GB/B params) • INT4 AWQ/GGUF (local sweet spot, 0.55 GB/B params) • INT2 BitNet/Ternary (extreme edge, 0.28 GB/B params) 2. Dynamic Context & KV-Cache Scaling: • Interactive context slider from 2k up to 1M tokens • FP16 vs FP8/INT8 KV-cache toggles to avoid unexpected OOMs mid-generation 3. Hardware Matching: • Instant fit checks for consumer cards (RTX 3060, 4070, 3090, 4090) • Apple Silicon Unified Memory (16GB to 128GB Mac) • Datacenter clusters (A100/H100 80GB) We also built the Price-to-Performance Pareto Frontier (https://www.themodelverse.in/com...) to map benchmark accuracy (MMLU-Pro / HumanEval) directly against real API token pricing across 530+ models. The site is 100% free, has zero ads, and requires no sign-up or API key. Would love to hear your feedback, hardware edge cases, and what quantization formats or features you'd like to see next!

TheModelVerse-Model Catalog & Benchmarks

Every foundation model measured — benchmarks, pricing & VRAM

Every foundation model measured — benchmarks, pricing & VRAM