How to Find Open-Source LLMs on LLM Explorer That Fit Your Hardware or Use Case

LLM Explorer indexes 63,069 open-source language models and 150 AI agents, and it organizes them so you can filter by hardware fit (4GB, 8GB, 12GB, 24GB, 32GB RAM/VRAM), by purpose (code generation, instruction-tuned, commercial use, uncensored), and by quantization format (GGUF, GPTQ, EXL2). If you know your available memory and what you want the model to do, you can narrow from tens of thousands of entries to a shortlist in a few steps. The directory also reports that 14,683 models are quantized and 23,379 fit within 8GB VRAM, which gives you a sense of how much the hardware filters actually cut down the list.

Start With Your Hardware Ceiling

The fastest way to shrink the list is to pick the memory tier that matches your machine. LLM Explorer provides dedicated category pages for each tier:

• LLMs Fit in 4GB RAM — smallest models, typically for lightweight or edge use
• LLMs Fit in 8GB RAM — the largest single bucket, with 23,379 models reported as fitting 8GB VRAM
• LLMs Fit in 12GB RAM
• LLMs Fit in 24GB RAM
• LLMs Fit in 32GB RAM

Pick the tier at or just below your actual available memory. A model listed under "Fit in 8GB" assumes roughly 8GB is free for the model itself, so leave headroom for your OS, browser, and any inference runtime.

Narrow by What You Want the Model to Do

Once your hardware tier is set, use the purpose-based categories to filter further. LLM Explorer groups models into lists including:

Combining a hardware tier with one of these categories is usually enough to get a workable shortlist. For example, "8GB + Code Generating" gives you coding models that should run on a mid-range GPU or a laptop with 8GB free.

Filter by Quantization Format

Quantization determines whether a model will actually load in your runtime. LLM Explorer separates models into:

• GGUF — commonly used with llama.cpp-based runners
• GPTQ — GPU-oriented 4-bit quantization
• EXL2 — variable-bit quantization for GPU inference

If you already know your inference tool, pick the matching format first, then apply the hardware and purpose filters. The directory also maintains a "Quantized" bucket covering 14,683 models, plus a dedicated TheBloke's Models list, which is a common source of ready-made quantizations.

Use Search for a Specific Model

If you already have a model name or keyword in mind, the search box ("What open-source LLMs or SLMs are you in search of?") takes you straight to it. This is the right entry point when you're checking whether a specific model exists in a given quantization or size, rather than browsing.

Check Benchmarks and License Before You Commit

Two filters matter before you download anything:

1. Benchmarks — LLM Explorer links to leaderboards including LMSYS ChatBot Arena ELO, so you can compare quality rather than guessing from the name.
2. License — the agent tables list licenses explicitly (MIT, Apache-2.0, GPL-3.0, AGPL-3.0, Elastic-2.0, and others). If you plan to ship a product, filter for commercial-use models and confirm the license text yourself.

A Practical Order of Operations

1. Decide your memory ceiling (4/8/12/24/32GB).
2. Pick a purpose category (code, instruct, commercial, uncensored, long-context).
3. Choose your quantization format (GGUF, GPTQ, EXL2) based on your runtime.
4. Sort or scan by benchmark score.
5. Verify the license matches your intended use.

Following this order means each step cuts the list further, so you're never scrolling through all 63,069 models. If you only need an agent rather than a raw model, the same site's AI Agent Directory lists 150 agents with type, stars, forks, and license — useful when you want a ready-to-use tool instead of a model to host yourself.