AI

CrowdGPT v2.6 - The collaborative LLM

Read full article at source ↗

Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 01/11/2026, 06:47:26 UTC.

Briefings

CrowdGPT v2.6 is a collaborative LLM project where users train a 1B parameter model locally without datacenters, aiming to democratize AI training. The v2.6 update focused on security, implementing Byzantine cross-checks that reduced malicious data acceptance from 100% to 2-3% per round, and added cryptographic client signing to prevent credential theft. The project achieved training on 4.5B new tokens in 6 days, though the architecture remains early-stage with ongoing work-in-progress status.

Key points

• CrowdGPT v2.6 implements Byzantine cross-checks between clients to filter malicious contributions, reducing malicious data acceptance from 100% to 2-3% per training round.
• The project achieved training on 4.5B new tokens in 6 days, significantly faster than the 2-month timeframe previously reported for the project on a personal GPU.

Context

CrowdGPT is an open-source project designed to train a 1 billion parameter LLM collaboratively without datacenters or large AI company spending, allowing independent PC users to contribute data and influence model direction.

Limitations

Evidence is limited to a single author's post from October 2026; the project is early-stage with ongoing work-in-progress status. Security claims are based on user reports and code analysis rather than independent verification. The project website and GitHub link are provided but no third-party validation of the security improvements is included in the evidence.

Generated by Bittrees-hosted qwen35-2b sha256:aaf42c8b7c3c on 01/11/2026, 10:23:05 UTC. Based on publisher-provided feed evidence; may not cover the full original article. Check the source for details.

View archived briefing on IPFS
bafybeifqeu73fcepk5maegosl33w4vxvuxgyshqmpldkwvhxnw7l247ywq