YOUR MEMORY BUDGET

Local AI models for 8GB VRAM.

Explore models by theoretical weight storage. This shortlist covers available downloadable language models with a known or name-derived parameter count.

6 GBleft for weights at 4-bit
8 GB budget − 2 GB reserve

The reserve is your assumption, not a measured overhead allowance. The default 2 GB may be too little for your runtime or context length. Results are not confirmed fits and have no verified speed rating. Weight estimates use decimal GB and do not include quantization overhead. For timed results on one 12GB machine, see measured speeds.

852 models within the weight budget

Largest parameter count first · not a quality ranking

Google · Downloadable

gemma-3-270m

Text generation

Generates text for conversations and writing.

Source listed
Aug 5, 2025
Parameters
270M
4-bit weights only
≈ 0.1 GB
License
gemma
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Liquid AI · Downloadable

LFM2.5-230M

Text generation

Generates text for conversations and writing.

Source listed
Jun 24, 2026
Parameters
230M
4-bit weights only
≈ 0.1 GB
License
other
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OpenMOSS · Downloadable

cpt-base

Text generation

Generates text for conversations and writing.

Source listed
Mar 2, 2022
Parameters
145M
4-bit weights only
≈ 0.1 GB
License
Not disclosed
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OpenMOSS · Downloadable

bart-base-chinese

Text generation

Generates text for conversations and writing.

Source listed
Mar 2, 2022
Parameters
140.2M
4-bit weights only
≈ 0.1 GB
License
Not disclosed
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BAAI · Downloadable

Aquila-135M

Text generation

Generates text for conversations and writing.

Source listed
Nov 27, 2024
Parameters
135M
4-bit weights only
≈ 0.1 GB
License
apache-2.0
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What changes with a 8GB budget?

With 2 GB set aside, 6 GB remains for weights. At 4-bit that allows up to 12 billion parameters in the theoretical calculation. An 8-billion-parameter model alone needs approximately 4 GB at this precision, before its conversation cache and runtime.

Why might a listed model still fail to load?

Its actual weight format may need more space, the runtime may not support its architecture, or the conversation cache may exceed your reserve. Vision models can also need space for image processing. Check the original model card and measure your exact setup before relying on it.

Read the memory guide and technical sources · Compare two models