PrivateAIAgent · 200 open LLMs

The best open source LLM models your hardware can run.

PrivateAIAgent ranks the best open source LLM models: 200 open-weight LLMs with parameter counts, context windows, benchmark scores and local LLM hardware requirements, so you can find the best local LLM for your machine before you download. I also install private local LLM stacks for businesses. Your data never leaves.

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200open LLMs
12spec filters
100%open weights
200open LLMs 12spec filters

Sample preview of the open LLM model database.

●Direct answers

Quick answers

The questions everyone asks, answered straight.

01

How much VRAM for a 70B model?

Roughly 40GB at 4-bit quantization, on a single high-end GPU. Full precision wants about 70GB, but quantization shrinks models hard with modest quality tradeoffs. Our VRAM chart maps requirements by parameter count and precision, so you can match the best open source LLM models to your hardware.

02

LLM license comparison for commercial use: which licenses allow it?

Permissive licenses like Apache 2.0 and MIT, with barely any strings attached. Community licenses such as Meta’s Llama add conditions like a user-count threshold. Our license comparison flags which models you can actually ship commercially, one by one, so there’s no guessing.

03

Best 7B open source LLM for laptop: which should I pick?

A recent 7–8B model with strong benchmarks and solid 4-bit quantized versions. Those run in 6–8GB of VRAM, which most modern laptops handle fine. Filter by parameter count, license and benchmark scores and you’ll find your fit fast, minus the trial and error.

●Features

The best open source LLM models, picked for your hardware

Specs, hardware fit, licenses, benchmarks. Everything worth knowing before you hit download.

Spec tables that matter

Spec tables that matter

Parameter count, context window, quantization options: every spec that decides what runs, in one open weight LLM models list.

VRAM and hardware fit

VRAM and hardware fit

LLM VRAM requirements by model size and precision. Know what runs on your GPU before you download a single weight.

Licenses for commercial use

Licenses for commercial use

Which open source LLM licenses allow commercial use, model by model. No legalese, just the verdict.

Benchmark scores compared

Benchmark scores compared

LLM benchmark scores by model size, for coding and reasoning. Find the strongest model your hardware can actually run.

●The dataset

Inside the open source LLM comparison dataset

What we collected, field by field. Every number traceable to its source.

Table 1: Coverage of the best open source LLM models by size (launch dataset).
Model sizeModelsKey fields
7B and under60+VRAM needs, quantization, laptop fit
13B–30B60+Benchmark scores, licenses, context window
70B and up40+Hardware requirements, benchmark scores
Coding & reasoning40+Coding scores, reasoning benchmarks
Table 2: Fields in each model record (illustrative sample values).
FieldWhat it meansSample value
Parameter countModel size class7B
Context windowMaximum input tokens128K
LicenseCommercial-use termsApache 2.0
VRAM (4-bit)Memory needed to run quantized~5 GB

Key terms, defined

Open weights
Weights you can download and run yourself. Fine-tuning too, if the license allows.
Quantization
Shrinking a model’s weights, say to 4-bit, so it fits in less memory. Quality takes a small hit, memory takes a big one.
VRAM
Your GPU’s memory. The hard ceiling on which models run at which precision.
Context window
How many tokens a model takes in one go: thousands for small models, over a million for the big ones.
Permissive license
Licenses like Apache 2.0 and MIT. Commercial use is fine, conditions are minimal.

●Preview

A sneak peek inside PrivateAIAgent

A peek under the hood. This is the data I work from.

●Topic hub

The open-source LLM topic hub

One keyword would not do it justice. The whole open-source LLM topic, mapped:

Model specs

Parameter counts, context windows, quantization. Side by side, no tab-switching.

VRAM & hardware

How much VRAM each model size needs, from laptops to 70B monsters.

Licenses & commercial use

Which open-weight licenses actually let you ship a product.

Benchmark scores

Coding and reasoning benchmarks, grouped by model size.

Coding LLMs

The best LLM for coding, picked from benchmark scores, not hype.

Running models locally

Open source alternatives to ChatGPT that run entirely on your machine.

How to pick the best local LLM for your machine

Hardware, license, use case. Match all three.

The best open source LLM models are not the biggest ones. They are the ones you can actually run. PrivateAIAgent’s open source LLM comparison lines up parameter counts, context windows and LLM benchmark scores side by side, so you can judge the best free LLM models on data instead of hype.

Hardware is the real constraint, full stop. Our local LLM hardware requirements tables and LLM VRAM requirements calculator show which open-weight LLM models fit your GPU, from laptop-friendly 7B models to 70B models that demand serious VRAM. If you want open source alternatives to ChatGPT running entirely on your machine, start here.

Then there is the use case. The best LLM for coding and the best open source reasoning model are usually different models, which surprises people. We track open source LLM for coding scores and license terms, so you land on a model that performs and that you are allowed to ship. Set up our private AI to put this data to work for you.

●FAQ

Frequently asked questions

Answers about open-source LLM specs, VRAM and licenses

How much VRAM for a 70B model?
A 70B-parameter model wants about 70GB of VRAM at full precision, but 4-bit quantization pulls that down to roughly 40GB, which fits a single high-end GPU like a 48GB card. Our VRAM calculator chart maps requirements by parameter count and quantization level, so you can match the best open source LLM models to your hardware.
LLM license comparison for commercial use: which licenses allow it?
Apache 2.0 and MIT are the safe bets: commercial use with minimal restrictions. Community licenses like Meta’s Llama add conditions, including the 700M monthly-user threshold. Our license comparison table flags every model you can use commercially.
Best 7B open source LLM for laptop: which should I pick?
Look for a recent 7–8B model with strong benchmark scores, a permissive license and good 4-bit quantized versions. Those typically run in 6–8GB of VRAM. Filter by parameter count, license and benchmark scores and the right pick shows itself.
Quantized LLM VRAM requirements chart: how do 4-bit and 8-bit compare?
Quantization shrinks models dramatically: 8-bit roughly halves memory versus full precision, and 4-bit roughly quarters it, with modest quality tradeoffs. PrivateAIAgent’s chart compares VRAM requirements by parameter count across precision levels.
What’s the best open source LLM for an RTX 4090?
24GB of VRAM goes a long way: 4-bit quantized models in the 30B+ range, or full-precision 13B models, run comfortably. Our hardware tables show which models fit common GPUs, so you can grab the strongest model your card supports.

●References

Sources & further reading

01

Hugging Face: Model weights, model cards and license metadata. The hub for open-weight everything.

02

arXiv: The papers behind the models and frameworks.

03

GitHub: Repos, stars and activity data for open-source projects.

04

Ollama: Run open models locally, on your own machine.

●Work with me

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