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Lumabri

Run Moe Models on a P2P Swarm with Colibri

Details

External ID
49293523
Source
HN
Company
—
Product
Lumabri
Website domain
github.com
Launched
Aug. 14, 2026
Cohort
—
Upvotes
48
Upvotes percentile
0.8561827956989247
Tags
—
Fetched at
Sept. 10, 2026, 5:32 a.m.
Updated at
Sept. 10, 2026, 5:32 a.m.

Enrichment

Theme
developer tools for ai agents
Vertical
Horizontal
Function
Model & infra
Audience
Developer
AI stance
AI-native
Project type
Commercial product
Normalized one-liner
run models on p2p swarm
Manually corrected
False

Could you build this?

No Running decentralized Mixture-of-Experts (MoE) LLM inference across a peer-to-peer swarm demands specialized distributed systems engineering, tensor parallelization, and P2P networking protocols.

What it would actually take: Architecture requires a custom peer-to-peer transport protocol (e.g., libp2p) combined with distributed dynamic tensor parallelism (similar to Petals or Hivemind). Hard challenges include latency-sensitive pipelined expert routing across asymmetric consumer internet connections, fault tolerance against dynamic peer dropouts, and custom CUDA/Triton kernels for decentralized MoE execution.

Discussion

19 comments analyzed.

Competitors mentioned: MeshLLM, llama.cpp

Concerns raised: Floating point determinism issues across different hardware and instruction sets, High latency for inference, especially over internet vs local networks, Accumulation order differs across CPU/GPU peers in same swarm, Lack of concrete performance benchmarks and statistics in documentation

Feature requests: Add performance stats and experiment logs (throughput, latency, bandwidth per model), Integrate HuggingFace model support directly like llama.cpp does, Use specific model repository names instead of vague model identifiers, Support for arbitrary layer chunking to increase memory throughput, Support for IoT/busybox devices contributing to inference swarms

Competitors

Other products that read as similar to this one — 1013 launches clear the similarity bar, closest 8 shown.

Attention rank: #116 of 1014 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 287 days after the earliest competitor.

Other launches for this product

Same idea, different domain

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