by TinyLlama Project

TinyLlama 1.1B.

text open weights edge 1B params 2K ctx

Smallest GPU

1× Nvidia Titan V

1.1B Llama-arch model — 3T training tokens.

Smallest GPU to run it See all quantisations →

1× Nvidia Titan V.

Most-aggressive quantisation we have a working recommendation for. Lower precision = less VRAM = cheaper hardware, at a small accuracy cost.

Sources

Official references.

Hugging Face ↗ GitHub ↗ Paper ↗

Best for

Workloads.

Run LLMs (inference / serving)

FAQ

Frequently asked.

How do I run TinyLlama 1.1B?

TinyLlama 1.1B is open-weight, so you can self-host on rented GPUs. See the Run It Yourself tab for GPU configurations + cost estimates, or use one of the hosted inference providers listed on this page.

Where can I access TinyLlama 1.1B?

TinyLlama 1.1B's access providers haven't been catalogued yet on RentGPU. Check the maker's documentation for official endpoints.

How much does it cost to run TinyLlama 1.1B?

Self-hosting cost depends on the GPU you rent and the throughput you need. See the Run It Yourself tab for GPU configurations and hourly cost estimates.

Is TinyLlama 1.1B open-source or proprietary?

TinyLlama 1.1B is open-weight under the Apache-2.0 license. You can download and self-host it.

Run it yourself

Cheapest hardware per quantisation.

Each row is one quantisation tier (the same weights compressed differently). Lower precision → lower VRAM → cheaper hardware, at the cost of small accuracy loss. $/hr refreshed hourly from each provider's API.

Quantisation	Cheapest GPU config	Total VRAM	Live $/hr	tokens/sec
FP16 FP16 — half precision (default)	1× Nvidia Nvidia Titan V	3 GB	—	—	Compare →
FP8 FP8 — 8-bit float (Hopper / Blackwell)	1× Nvidia Nvidia Titan V	3 GB	—	—	Compare →
INT4 INT4 — 4-bit integer (~4× VRAM saving)	1× Nvidia Nvidia Titan V	3 GB	—	—	Compare →

Just need an API?

Skip the GPU rental and call a hosted endpoint instead. See access providers on the Overview tab →