YOUR MEMORY BUDGET

Local AI models for 16GB VRAM.

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

14 GBleft for weights at 4-bit
16 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.

352 models within the weight budget

Largest parameter count first · not a quality ranking

OpenAI · Downloadable

gpt-oss-20b

Text generation

Generates text for conversations and writing.

Source listed
Aug 4, 2025
Parameters
20B
4-bit weights only
≈ 10 GB
License
apache-2.0
Compare this model →

Microsoft · Downloadable

Phi-4-reasoning-vision-15B

VisionDocument readingReasoning

Understands text and images and responds in text. Supports a reasoning mode.

Source listed
Jan 23, 2026
Parameters
15B
4-bit weights only
≈ 7.5 GB
License
mit
Compare this model →

Microsoft · Downloadable

NextCoder-14B

Text generationCoding

Designed for coding and software tasks. Generates text for conversations and writing.

Source listed
May 3, 2025
Parameters
14B
4-bit weights only
≈ 7 GB
License
mit
Compare this model →

Qwen · Alibaba · Downloadable

Qwen3-14B

Text generation

Generates text for conversations and writing.

Source listed
May 23, 2025
Parameters
14B
4-bit weights only
≈ 7 GB
License
apache-2.0
Compare this model →

Qwen · Alibaba · Downloadable

Qwen3-14B-MLX

Text generation

Generates text for conversations and writing.

Source listed
Jun 12, 2025
Parameters
14B
4-bit weights only
≈ 7 GB
License
apache-2.0
Compare this model →

What changes with a 16GB budget?

With 2 GB set aside, 14 GB remains for weights. At 4-bit that allows up to 28 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