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TinyLlama

A 1.1B model with the Llama 2 architecture, trained on 3 trillion tokens. Now behind newer small models, but still a popular base for experiments and fine-tuning.

The last open version came out in Mar 2024. The family has not been updated for a long time: the model still works, but do not expect fixes or new sizes.

Developer
TinyLlama (SUTD researchers), Singapore
First release
Sep 2023
Latest release
Mar 2024
Sizes
1.1B
License
Commercial use allowedApache 2.0
Russian
Not supported
Ready-made builds
GGUF, AWQ, GPTQ
Running
Available in OllamaAlso runs without a GPU
Industries
Education, Science and research, Software development
Deploy on your server Hugging Face GitHub

What it does

  • Simple chatbots on low-end hardware
  • Experiments and team training
  • A base for fine-tuning on a narrow task

Where it is used

EducationResearchPrototypes

Hardware requirements

LaptopLaptop or regular PC, up to 8 GB of VRAM — smaller versions
fits
1 GPUOne GPU with 16–80 GB — mid-size versions
no versions
ClusterServer with several GPUs — flagship versions
no versions

Versions

  1. TinyLlama v1.1 (общая, код и математика, китайская)
  2. TinyLlama-1.1B-Chat v1.0
  3. TinyLlama-1.1B (промежуточные чекпоинты)

How to run it

With Ollama — the fastest wayInstall Ollama on your computer or server and find the model in its library by name. Good enough to try the model on your own tasks in one evening.Find it in the Ollama library
On your own serverWeights are downloaded from Hugging Face and served with vLLM (load and API) or llama.cpp (modest hardware). The model then runs in a closed network with no per-request fees.Hugging Face
How much hardware you needCalculate the VRAM for the model size, context length and number of concurrent requests.Open the hardware calculator

I can set this up end to end: pick the model size, deploy it on your server and connect it to your systems. Quantization compresses a model so it takes less video memory and runs on more modest hardware. Answers change slightly, so quality is checked on your own examples.

Frequently asked questions

Can TinyLlama be used in a commercial project?

Yes. License: Apache 2.0. It allows commercial use, but it is still worth having a lawyer review the license before launch.

What hardware does TinyLlama need?

At minimum: Laptop or regular PC, up to 8 GB of VRAM — smaller versions. Some versions also run on an ordinary CPU, without a GPU. You can calculate the exact VRAM for your model size and context in the hardware calculator.

Does TinyLlama support Russian?

No. The model card lists its languages and Russian is not among them.

Where can I download TinyLlama and what does it cost?

The TinyLlama weights are open and free to download. You only pay for the hardware it runs on and for the setup. Source links are at the bottom of this page.

How I deploy it for clients

  1. SelectionI pick the model size for your task and hardware and test it on your examples.
  2. DeploymentI deploy it on your server or in a closed network and provide an API.
  3. Fine-tuningI fine-tune it on your data (LoRA) or connect a knowledge base — whichever is cheaper for the task.
  4. IntegrationI connect it to your CRM, ERP, bot, website or team chat and set up monitoring.

Comparisons

Similar models

TextSmolLMHugging Face · USACommercial use allowed

Tiny open Hugging Face models for phones and laptops. SmolLM3 (3B) can reason and handle long context; the full training recipe is open.

DetailsTextPhiMicrosoft · USACommercial use allowed

Small Microsoft models trained on carefully selected data: strong at logic and math for their modest size. Versions with images and speech are available.

DetailsTextPythia и GPT-NeoXEleutherAI · USACommercial use allowed

Fully open models from the non-profit lab EleutherAI: GPT-NeoX-20B and the Pythia series with published intermediate training checkpoints.

Details

Source: huggingface.co/TinyLlama/TinyLlama_v1.1. Data checked against the model card on 22 Sep 2026. Have a lawyer review the license before commercial launch.