Model reference · open weights

bloom-7b1-petals

LLMs bigscience Text gen 1 build Licence not stated 204 dl/mo

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.

  • bloom-7b1-petals is a text-generation model developed by bigscience.
  • It is a post-processed version of the original bloom-7b1 model, designed to run on the Petals swarm for distributed inference.
  • The model contains 1.0B parameters and is provided primarily for testing purposes.

Summary of the bigscience/bloom-7b1-petals model card, 2026-10-01

What it is

Released bybigscience
Released2023-01-22
Parameters1.0B
VRAM2.1 GB for the weights

What it runs on

Memory and cards for bloom-7b1-petals (FP16)

2.1 GBweights, file size
492 MBcache per 1K tokens
888 MBruntime overhead, at least
CardRequests at onceContext maxMemory
8K each32K each
RTX 3060 12 GB2—17K11.6 GB
RTX 4060 Ti 16 GB3—24K15.4 GB
RTX 3090 24 GB5140K23.4 GB
RTX 4090 24 GB5140K23.4 GB
RTX 5090 32 GB6155K31.0 GB
L40S 48 GB10281K44.0 GB
A100 80 GB184128K78.2 GB
H100 80 GB174128K78.1 GB
RTX PRO 6000 Blackwell 96 GB215128K93.8 GB
DGX Spark (GB10) 128 GB unified246128K107 GB
H200 141 GB328128K138 GB
B200 180 GB4110128K176 GB
2× RTX 3060 12 GB
tensor parallel
4138K11.6 GB a card
2× RTX 4060 Ti 16 GB
tensor parallel
6153K15.4 GB a card
2× RTX 4090 24 GB
tensor parallel
10285K23.4 GB a card
2× RTX 3090 24 GB
tensor parallel
10285K23.4 GB a card
2× RTX 5090 32 GB
tensor parallel
143115K31.0 GB a card
Memory needed at each load
Requests at once8K tokens each32K tokens each
17.0 GB19.0 GB
523.1 GB83.5 GB
835.2 GB132 GB
1667.4 GB261 GB
32132 GB518 GB
64261 GB1034 GB

One card, with vLLM's small-card settings.

From the model card

What bigscience says about bloom-7b1-petals

Read 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:

  • The original model card to learn about the model's capabilities, specifications, and terms of use.
  • The Petals repository to learn how to install Petals and run this model over the Petals swarm.

We provide minimal code examples below.

Using the model

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...

Serving the model blocks

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.

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