The best local LLMs for 16 GB VRAM in 2026

16 GB of VRAM is the sweet spot for running AI models at home in 2026. It costs far less than a 24 GB card, yet it runs every strong model up to about 20 billion parameters entirely on the GPU, with room left for a useful amount of conversation context. This guide lists the models worth downloading on a 16 GB card, the quantization to pick for each, and how fast they run on the popular cards.

All numbers come from the same memory and speed math our checker uses, at 8K context. A 16 GB card gives a model about 14.4 GB to work with; the rest goes to the driver and display.

The short answer

Anything above about 10 tokens per second feels like reading speed, so every model in this table is comfortable to use.

gpt-oss 20B: the best all-rounder

OpenAI's open-weight model is the standout for 16 GB cards. It has 21 billion parameters in total, but it is a mixture-of-experts model that only uses about 3.6 billion of them for each word. That is why it runs two to three times faster than the 14B models below, while answering at a similar or better level, especially on reasoning tasks where you can raise its effort setting.

The catch is space. At Q4_K_M it needs about 14 GB at 8K context, so it only just fits. Keep context at 16K or less and close other GPU-heavy apps. Run it with ollama run gpt-oss:20b.

Qwen3 14B: the best dense model for coding and daily use

Qwen3 14B is the strongest traditional model that fits comfortably. On 16 GB you can step up from the Q4 that 12 GB owners are stuck with to Q5_K_M, which keeps a little more quality, and still have room for 16K of context. It handles code, writing and multi-step questions well, and its thinking mode can be switched on when a problem needs it. Run it with ollama run qwen3:14b.

If you write a lot of code, pair it with an editor extension like Continue or Cline pointed at Ollama. It is fast enough on any 16 GB card for inline suggestions and chat about your codebase.

Gemma 4 12B: best for writing and images

Google's 2026 mid-size model reads images as well as text, and its writing reads more naturally than most models this size. On 16 GB it fits at Q6_K, close to full quality, at about 13.8 GB including the vision encoder. Use it for drafting, summarising screenshots and documents, or describing photos.

Phi-4 14B and DeepSeek-R1 Distill 14B: maths and reasoning

Both sit at the same size as Qwen3 14B and fit at Q5_K_M. Phi-4 from Microsoft is unusually strong at maths and logic puzzles for its size. DeepSeek-R1 Distill 14B writes out its reasoning before answering, which makes it slower to reach an answer but more reliable on multi-step problems. Pick one of them if most of your questions are of that kind; otherwise Qwen3 14B covers the same ground.

Qwen3.5 9B: when you need long context

Context length costs memory. Every extra 1,000 tokens of conversation or pasted text needs room for the model's KV cache, and on a 14B model that adds up fast: Qwen3 14B fits 16K of context on 16 GB, but not 32K. Qwen3.5 9B uses a hybrid design whose context memory is very small, so it fits at full Q8 quality with 64K of context. Use it for long PDFs, big chunks of code, or long chat sessions. It also reads images.

Speed on popular 16 GB cards

Every 16 GB card runs the same models, because VRAM decides what fits. What changes is speed, which follows memory bandwidth.

The RTX 4060 Ti 16GB is the slowest here because of its narrow memory bus, but it still runs everything above at a usable speed. If you are choosing between two cards, our RTX 4060 Ti vs RTX 5060 Ti comparison shows the gap model by model. AMD and Intel cards run these models through Vulkan or ROCm; setup takes a little longer than on Nvidia, but the speeds above are realistic once it works.

What does not fit on 16 GB

Dense models from 22B upwards spill out of VRAM. Mistral Small 3 24B needs about 17.7 GB at Q4, and the 27B to 32B models need 19 to 24 GB. They will still load with part of the model in system RAM, but speed drops to 2 to 4 tokens per second, which is too slow for chat.

Mixture-of-experts models are the exception. Qwen3 30B-A3B needs about 20.6 GB, so it does not fit, but because only 3 billion parameters work on each word it still runs at roughly 8 to 17 tokens per second with part of it in RAM, depending on the card. Gemma 4 26B-A4B behaves the same way. If you have 32 GB of system RAM, both are worth trying as a step up from the 14B models.

For 27B to 30B models running fully on the GPU, you need 24 GB, such as a used RTX 3090. See RTX 4070 Ti Super vs RTX 3090 for that trade-off.

Which quantization to download

On 16 GB the rule is simple: use the highest quality that still leaves room for the context you need.

• gpt-oss 20B: Q4_K_M, or the default MXFP4 build Ollama downloads. There is no room for more.
• 12B to 14B models: Q5_K_M with up to 16K of context. If you need 32K or more, switch to Qwen3.5 9B rather than dropping quality.
• 7B to 9B models: Q8_0. They fit easily at near full quality.

Not sure what these labels mean? Read Q4, Q5 and Q8 explained.

Getting started

Install Ollama or LM Studio, then pull one model to start. A good first pair is gpt-oss 20B for general questions and Qwen3 14B for code. Both download in a few minutes on a normal connection and run straight away on any 16 GB card.

Have a smaller card? See the best models for 12 GB and for 8 GB. To check your exact card, open it in the GPU list.