Best Local LLMs for 16GB and 24GB GPUs in Late 2026

Local AI is now a practical daily tool, not just a demo. The hard part is picking a model that actually fits your GPU. This post lists three strong options, shows which card each one needs, and gives the math so you can check it yourself.

The Runner: Ollama vs. LM Studio

You have two main choices for running models:

1. Ollama: A headless background service. You manage models from the command line, and it exposes a local API (port 11434) that is compatible with the OpenAI format. It works well with frontends like Open-WebUI and with code editors.
2. LM Studio: A desktop app with a visual interface. You can search the Hugging Face hub, download a specific .gguf quantization, and chat with it. A good fit if you prefer to avoid the terminal.

Three Models and the GPU They Need

All numbers below use the Q4_K_M (4-bit) GGUF file and an 8K context window.

Model	Type	Q4_K_M file	Total VRAM needed	Fits 16GB?	Fits 24GB?
gpt-oss 20b	Mixture of experts	10.83 GB	~12.1 GB	Yes	Yes
Devstral 24B	Dense	13.35 GB	~16.1 GB	No (just over)	Yes
Qwen3-Coder 30B	Mixture of experts	17.28 GB	~19.8 GB	No	Yes

• gpt-oss 20b: OpenAI’s open-weight model. It is the easiest to run, and it leaves room for long context on a 16GB card.
• Devstral 24B: A dense model built for coding. It needs a 24GB card at 8K context.
• Qwen3-Coder 30B: The largest of the three. It is a mixture-of-experts model, so all of its weights must sit in VRAM even though only a part is active per token. The file alone is bigger than a 16GB card.

The Hardware Reality (Do The Math)

The fastest way to ruin a local setup is a CUDA out-of-memory (OOM) error because you guessed your hardware limits. Both llama.cpp and vLLM reserve the KV cache for your full context window when the model loads, so a model that does not fit fails at startup, not halfway through a chat. LocalNodeOps uses one formula:

Total VRAM = Weights + KV Cache + 10% Overhead

The 10% is taken on top of weights plus KV cache. Here is the full example for Qwen3-Coder 30B at Q4_K_M with 8K context:

• Weights: 17.28 GB
• KV cache (8K context): ~0.75 GB
• Base total: ~18.03 GB
• 10% overhead: ~1.80 GB
• Total required: ~19.8 GB

That fits a 24GB card (RTX 3090 or 4090) with a few GB to spare. It does not fit a 16GB card at all, because the weights alone are larger than 16 GB. Compressing the KV cache cannot fix that, since the cache is only about 0.75 GB here.

Your numbers will go up if you raise the context window. See how the formula works for the details.

Check Your Own Setup

To see whether a specific model fits your GPU, use the VRAM Calculator. It runs this math against the real .gguf file sizes synced from Hugging Face.