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local-ai-toolkit

m15dg

AI-assisted model selection · Quantized for consumer hardware · A suggested starting point This repository is a **suggested toolkit** of open-source AI models assembled to run entirely locally on consumer-grade hardware — specifically an **NVIDIA RTX 3060 (12GB VRAM) with 32GB system RAM**. Each model was chosen to fit within these constraints while offering strong output quality for the VRAM budget. The selection process was **AI-assisted** — I consulted several AI assistants to weigh benchmarks, quantization trade-offs, and model capabilities, then settled on the common recommendations you see here. This is **not a definitively optimal combination**: better models may already exist, and stronger ones are released regularly. Treat it as a well-reasoned starting point rather than a final answer — your own testing on your own hardware is the real benchmark.

Text generation

Input Text→ Output Text

Price not verifiedCheck multimodal and channel pricing separately

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Cost analysis

Adjust monthly token usage to compare estimated API costs.

Monthly input tokens 10 MMonthly output tokens 2 M1 M = 1 million tokens · USD
Compares base text-token rates within the same channel

Uses collected, comparable USD-per-million-token rates. Snapshots exclude cache discounts, tiers, extra reasoning output and taxes. Check current source prices.

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Selection guide & FAQ

How should API prices be compared?+

Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.

Do open weights mean free commercial use?+

Not necessarily. Check each version’s license, commercial restrictions and policies. Self-hosting also incurs compute and maintenance costs.

Is the top-ranked model the best for me?+

Rankings depend on tasks, versions and methods. Compare accuracy, latency, reliability and costs on your own workload.

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local-ai-toolkit

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