Text generation · served with vLLM · ↓ 5.3K downloads a month · View on Hugging Face
ybelkada/Mixtral-8x7B-Instruct-v0.1-AWQ needs about 30.8 GB of GPU memory for conversations of up to 8,192 tokens. It runs on one RTX 4090 from $0.48/h right now.
Weights
25 GB
GPU memory needed
about 30.8 GB
Conversation length
8,192 tokens
Model allows
32,768 tokens
Memory · disk
20.3 GB · 68 GB
Architecture
MixtralForCausalLM
Licence
not stated
Hardware and deploy
$0.48/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 Mixtral-8x7B-Instruct-v0.1-AWQ need?
About 30.8 GB for conversations of up to 8,192 tokens: 24.65 GB of weights, the cache for one full-length conversation and the runtime's own memory.
What is the cheapest GPU to run Mixtral-8x7B-Instruct-v0.1-AWQ?
Right now one RTX 4090 at $0.48 an hour. Prices are live offers from GPU clouds, checked every 15 minutes.
Can Mixtral-8x7B-Instruct-v0.1-AWQ run on an RTX 4090?
Not on one: it needs about 30.8 GB and an RTX 4090 has 24 GB. An RTX 5090 (32 GB) is enough.
How do I deploy Mixtral-8x7B-Instruct-v0.1-AWQ 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.