Model reference · open weights
bloom-7b1-petals is an open-weight language model from bigscience. bloom-7b1-petals (FP16) weighs 2.1 GB; the smallest configuration that runs it is RTX 3060 12 GB.
Summary of the bigscience/bloom-7b1-petals model card, 2026-10-01
What it is
| Released by | bigscience |
|---|---|
| Released | 2023-01-22 |
| Parameters | 1.0B |
| VRAM | 2.1 GB for the weights |
What it runs on
| Card | Requests at once | Context max | Memory | |
|---|---|---|---|---|
| 8K each | 32K each | |||
| RTX 3060 12 GB | 2 | — | 17K | 11.6 GB |
| RTX 4060 Ti 16 GB | 3 | — | 24K | 15.4 GB |
| RTX 3090 24 GB | 5 | 1 | 40K | 23.4 GB |
| RTX 4090 24 GB | 5 | 1 | 40K | 23.4 GB |
| RTX 5090 32 GB | 6 | 1 | 55K | 31.0 GB |
| L40S 48 GB | 10 | 2 | 81K | 44.0 GB |
| A100 80 GB | 18 | 4 | 128K | 78.2 GB |
| H100 80 GB | 17 | 4 | 128K | 78.1 GB |
| RTX PRO 6000 Blackwell 96 GB | 21 | 5 | 128K | 93.8 GB |
| DGX Spark (GB10) 128 GB unified | 24 | 6 | 128K | 107 GB |
| H200 141 GB | 32 | 8 | 128K | 138 GB |
| B200 180 GB | 41 | 10 | 128K | 176 GB |
| 2× RTX 3060 12 GB tensor parallel | 4 | 1 | 38K | 11.6 GB a card |
| 2× RTX 4060 Ti 16 GB tensor parallel | 6 | 1 | 53K | 15.4 GB a card |
| 2× RTX 4090 24 GB tensor parallel | 10 | 2 | 85K | 23.4 GB a card |
| 2× RTX 3090 24 GB tensor parallel | 10 | 2 | 85K | 23.4 GB a card |
| 2× RTX 5090 32 GB tensor parallel | 14 | 3 | 115K | 31.0 GB a card |
| Requests at once | 8K tokens each | 32K tokens each |
|---|---|---|
| 1 | 7.0 GB | 19.0 GB |
| 5 | 23.1 GB | 83.5 GB |
| 8 | 35.2 GB | 132 GB |
| 16 | 67.4 GB | 261 GB |
| 32 | 132 GB | 518 GB |
| 64 | 261 GB | 1034 GB |
One card, with vLLM's small-card settings.
From the model card
This model is a version of bigscience/bloom-7b1 post-processed to be run at home using the Petals swarm.
Note: Petals is developed to run 100B+ models like the full-scale BLOOM or BLOOMZ. This model is provided for testing purposes only. It may be more efficient to run the original version of it locally.
Please check out:
We provide minimal code examples below.
from petals import DistributedBloomForCausalLM
model = DistributedBloomForCausalLM.from_pretrained("bigscience/bloom-7b1-petals")
# Embeddings & prompts are on your device, BLOOM blocks are distributed across the Internet
inputs = tokenizer("A cat sat", return_tensors="pt")["input_ids"]
outputs = model.generate(inputs, max_new_tokens=5)
print(tokenizer.decode(outputs[0])) # A cat sat on a mat...
python -m petals.cli.run_server bigscience/bloom-7b1-petals
Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.