Mesh LLM: distributed AI computing on iroh(iroh.computer)
iroh.computer
Mesh LLM: distributed AI computing on iroh
https://www.iroh.computer/blog/mesh-llm
31 comments
This was done on my home lab simulating 5ms latency and jitter between machines. Splits work quite well if you your nodes are over WAN at metro latency’s but not super fast on global WAN.
The idea is that you could take several machines without dedicated RDMA or NVLINK fabric and use them to serve a large model on hardware you own then share it with others.
I’m currently working on GLM 5.2 on my lab environment with around 10 tok/s on the same split.
The idea is that you could take several machines without dedicated RDMA or NVLINK fabric and use them to serve a large model on hardware you own then share it with others.
I’m currently working on GLM 5.2 on my lab environment with around 10 tok/s on the same split.
That sounds cool, but it's still pretty meaningless without information about what your home lab looks like. A few DGX Sparks wired up with their fancy super fast network is much different than a few laptops on wifi.
What hardware (CPU/GPU/memory) and network was used for this? What quantization for GLM 5.2? How much tuning of the split was needed?
The lab features two Mac Studios: an Apple M3 Ultra (32 CPU cores, 80 GPU cores, 256 GB unified memory) and an Apple M1 Ultra (20 CPU cores, 48 GPU cores, 128 GB unified memory), both connected via 1Gbit Ethernet.
We use a customized Q2 quantization that preserves sensitive tensors at Q8.
To reduce compute time per layer, we are developing a custom GLM DSA Metal graph.
While we are not yet approaching MTP, we plan to port our existing MTP implementations from versions 4.7 and 5.1 to 5.2.
Since GLM's MTP acceptance rate is very high for a single predicted token, we are exploring token prediction techniques to widen the predicted tokens and utilize parallelism for verification.
We use a customized Q2 quantization that preserves sensitive tensors at Q8.
To reduce compute time per layer, we are developing a custom GLM DSA Metal graph.
While we are not yet approaching MTP, we plan to port our existing MTP implementations from versions 4.7 and 5.1 to 5.2.
Since GLM's MTP acceptance rate is very high for a single predicted token, we are exploring token prediction techniques to widen the predicted tokens and utilize parallelism for verification.
Equivalent M3 machines no longer for sale from Apple (only up to 96 GB) but can be had on eBay for around $14,000 each
Perf should be fairly straightforward to ballpark. You'll need to transfer roughly 2 . hidden_size . num_shards bytes over the network per token during autoregressive decoding. And divide that number by chunk size during prefill.
If you split sequential layers across GPUs, latency is often the main constraint, not bandwidth.
I’ve been curious what a polymorphic botnet that runs one (or multiple) distributed LLMs would be capable of doing. The idea would be to evolve the botnet delivery and payload using the clustered compute of all hosts in the botnet to run LLMs that guides the evolution of various botnet clusters. Bad cluster morphs get caught and cleaned off and bad delivery methods never spread, but the best versions survive to continue to grow.
What I envisioned for how it works is fairly similar to this, QUIC can actually be more difficult to detect than it seems since it’s very dynamic.
What I envisioned for how it works is fairly similar to this, QUIC can actually be more difficult to detect than it seems since it’s very dynamic.
I’m one of the contributors to Mesh LLM and happy to answer any questions. I authored the skippy engine that allows you to split large models across nodes.
I have never really delved into kv cache implementation, do they run effectively separate caches per layer?
If so I can see it all dividing nicely, computation and data size wise and the only slowdown would be in search layer waiting for it's turn. If you pipelined it you could run multiple queries.
Is anyone doing best-of-n with a n stage pipeline running each query offset by one?
If so I can see it all dividing nicely, computation and data size wise and the only slowdown would be in search layer waiting for it's turn. If you pipelined it you could run multiple queries.
Is anyone doing best-of-n with a n stage pipeline running each query offset by one?
Curious about: does it have fault tolerance if one of the machines goes down mid-inference? Can it dynamically reroute, or does it just retry?
This is super impressive, We have a lab with lots of different epycs and different models - to bring them together this way is amazing. Well done!
Thank you! AMD is a weak spot in our testing right now. If you’re willing to contribute or let us borrow some compute time, drop in on the Discord.
Is skippy related to a certain beer can?
Does Mesh LLM encrypt the payload between nodes? Is it possible to read requests from other users?
I'm not affiliated, but yes – the main 'point' of iroh is that it's 'dial-a-key', QUIC with encryption based on the keys of the endpoints.
Just wondering, why do you care about encryption in this context?
If payloads to LLMs are being passed around to various nodes, even trusted ones (like friends and family), it gets awkward if you send something very personal. Think sending a medical question to medgemma:27b.
Even if transport is encrypted, the LLM computations will always be clear text, right?
I thought about this too, but the throughput over a network is incredibly slow. It’s not usable for interactive use.
That isn’t true. llama RPC is incredibly slow but staged splits in skippy are orders of magnitude faster.
It sounds like iroh enables distributed compute without having to finangle custom hardware.
cocompute.ai is already doing this really well.
Is it? I don't see anything on the website about splitting a model across multiple devices, only about putting local models on the internet, a wholly orthogonal problem (which is already easy with existing tools, since models use an http API).
Good point. I know cocompute is working on splitting, but it’s not there yet; I was referring to the round-robin delegation within a trusted pool. Mesh LLM looks great too!
Cool, always good to have more in the ecosystem. I love Iroh and hope this continues to succeed.
I just wish I had the hardware to try it out!
Are we talking 1 token per second for a split model? Less?
Edit: Found a number. On the models list, Qwen 235B A22B says "MoE 235B/22B, proven at 16 tok/s across 2 nodes". They don't say what the nodes are and what network connection they have, but that's a respectable speed. Not quite comfortable for interactive use, but pretty close.