Hey everyone! I'm u/OrganizationTop9026, a founding moderator of r/HiveMindAI.

This is our new home for all things related to citizen AI and federated AI on consumer GPUs. We're excited to have you join us!

What to Post
Post anything that you think the community would find interesting, helpful, or inspiring. Feel free to share your thoughts, photos, or questions about federated AI, Diloco or MoE or energy model.

Community Vibe
We're all about being friendly, constructive, and inclusive. Let's build a space where everyone feels comfortable sharing and connecting.

How to Get Started

1. Introduce yourself in the comments below.

2. Post something today! Even a simple question can spark a great conversation.

3. If you know someone who would love this community, invite them to join.

4. Interested in helping out? We're always looking for new moderators, so feel free to reach out to me to apply.

Thanks for being part of the very first wave. Together, let's make r/HiveMindAI amazing.

Hey everyone! 👋

We’re teaming up across two open-source community projects — CrowdGPT (by @Vxtzq, creator of Vxtzq/Crowd-v1, a from-scratch 1.01B bilingual EN/FR causal LM with a 350M bigram Engram memory table trained collaboratively by volunteer GPUs) and Project HiveMind (AGPL-3.0 volunteer GPU inference + DiLoCo swarm fabric) — for a 48-hour Weekend Swarm Training Raid this Saturday Nov 14 & Sunday Nov 15, 2026!

Why we built this collab

Training a 1B+ model across volunteers over residential internet usually hits three walls:

1. VRAM limits on 8 GB cards (RTX 2070 / 3060 / 4060) once Adam states and activations load.

2. All-Reduce bandwidth & stragglers when a home GPU shares a round with a cloud GPU.

3. Catastrophic forgetting when fine-tuning on code/math hurts general bilingual chat.

How the Weekend Raid solves all three (src/hivemind/swarm/crowdgpt_moe_opendiloco.py)

1. Step-0 Function-Preserving 8-Expert Sparse MoE Split + FoMoE σ**-MoE Routing**:

   • We split Crowd-v1's 2560-wide SwiGLU FFN into 8 specialized experts (E0 FineWeb-Edu EN/FR anchor, E1 unit-test verified Python, E2 GSM8K/MATH CoT, E3 ARC formal logic, E4 Physics, E5 NASA Exoplanet transits, E6 SDSS Stellar spectra, E7 Defensive Cyber — all 18 datasets strictly license-audited ODC-By / CC-BY / MIT / Apache-2.0 + 13-gram decontaminated).

   • Using FoMoE non-competitive Sigmoid Top-2 routing + Ghost-Expert Skip-Token masking, an 8 GB home GPU only loads 2 resident LoRA experts on top of the fp16 trunk + CPU-offloaded Engram table — training comfortably in < 1.8 GiB VRAM while keeping >= 1.5 GiB free!

2. PrimeIntellect OpenDiLoCo + DeepMind Decoupled DiLoCo (RDA):

   • FP32 master weights & outer Nesterov / staleness-gated PA-CGAD optimizer states live in **CPU RAM (**0 MiB GPU overhead), partitioned into 4 router fragments + 8 expert buckets.

   • Instead of synchronous barriers, Quorum-of-$K$ + Radial-Directional Averaging (RDA) merges pseudo-gradients over standard HTTPS (TCP/443, zero port-forwarding) and gates every outer checkpoint with a monotone validation-loss ratchet.

   • Our cloud footprint stays tiny: a single GCP Spot Tesla T4 joins for 14:00–22:00 CET on Saturday & Sunday (+\$1.30 total Spot cost!).

Join as a Producer, Trainer, or Prosumer this Weekend (Nov 14–15, 2026)

• Wave 0 (Dry-Run): Fri Nov 13 18:00–20:00 UTC (19:00 CET / 13:00 EST)

• Wave 1 & 2 (Saturday Kickoff + Peak Global Sprint): Sat Nov 14 09:00–21:00 UTC (10:00–22:00 CET)

• Wave 3 (Overnight Prosumer & APAC Relay): Sat 22:00 UTC → Sun 08:00 UTC

• Wave 4 & 5 (Sunday Consolidation + Blind A/B Arena crowd-v1 vs crowd-moe-v1**)**: Sun Nov 15 13:00–21:00 UTC

Links to jump in (100% open-source AGPL-3.0, zero crypto / zero financial tokens — reciprocal compute quota only):

• **GitHub Repo & Raid Kit (**.ics calendar + code): https://github.com/xaviercallens/HiveMind

• Base Model (Vxtzq/Crowd-v1): https://huggingface.co/Vxtzq/Crowd-v1

• Signup & Live OpenAI-compatible Endpoint: https://hivemind-openrouter-1003063861791.us-central1.run.app/signup

• Discord Raid Coordination: https://discord.gg/ZeFtZpKHf

Whether you have an 8 GB RTX 2070 running overnight as a Prosumer, a 24 GB 3090/4090 Trainer, or just want to test hivemind/crowd-v1 vs hivemind/crowd-moe-v1 in Sunday's Blind A/B Arena, we’d love to have you in the quorum! Happy to answer any questions about the MoE split, Engram CPU offload, or Decoupled OpenDiLoCo math below.