Text generation · served with vLLM · ↓ 367 downloads a month · View on Hugging Face
HuggingFaceH4/starchat-beta needs about 45.7 GB of GPU memory for conversations of up to 8,192 tokens. It runs on one Nvidia A40 from $0.61/h right now; best value: RTX PRO 5000 at $0.64/h.
Weights
31 GB
GPU memory needed
about 45.7 GB
Conversation length
8,192 tokens
Model allows
8,192 tokens
Memory · disk
23.5 GB · 75 GB
Architecture
GPTBigCodeForCausalLM
Licence
bigcode-openrail-m
Hardware and deploy
$0.64/h from your balance while it runs, billed by the hour; stop it from its page. GridPup rents the machine, which downloads the model from Hugging Face (usually ready in 10 to 15 minutes), and gives you an https address, keys and a Playground.
Questions
How much GPU memory does starchat-beta need?
About 45.7 GB for conversations of up to 8,192 tokens: 31.04 GB of weights, the cache for one full-length conversation and the runtime's own memory.
What is the cheapest GPU to run starchat-beta?
Right now one Nvidia A40 at $0.61 an hour; RTX PRO 5000 at $0.64 an hour is the best value for its speed. Prices are live offers from GPU clouds, checked every 15 minutes.
Can starchat-beta run on an RTX 4090?
Not on one: it needs about 45.7 GB and an RTX 4090 has 24 GB. Use a bigger GPU, or a smaller (quantized) version of the model.
How do I deploy starchat-beta as an API?
Pick a machine on its GridPup page and press Deploy: GridPup rents the GPU, downloads the model from Hugging Face and serves it at an https address with keys and OpenAI's chat API, billed by the hour.
Prices are per hour for the whole machine and its disk, right now. Speed is Vast's DLPerf score of the machine, relative to the cheapest. Best value: the most speed per dollar. Only machines with a Vast reliability above 95% that download at 400 Mbit/s or more are offered: the machine fetches the model itself.