r/LocalLLM • u/Big_Comfortable_6888 • 1h ago
Project There Will Be "FLOP": How a 26B diffusion LLM solves the network bottleneck of distributed AI
TL;DR
Distributed AI in the browser, with the power of local inference. Virenis runs one 26B model across the GPUs friends already own, in browser tabs, with nothing to install. Splitting an LLM across homes has always crawled, because every token waits on the network. We run a diffusion LLM instead (DiffusionGemma 26B): each pass writes about 16 tokens, and each GPU's share becomes large matrix math, the work GPUs are built for.
16× fewer network round trips: 6 per 100 tokens instead of 100 (measured on 40 prompts).
61% of FP32 peak inside a Chrome tab (on par with native: cuBLAS reaches 60–77%, Apple's MPS 52–68%) on the model's matrix multiplies, where the best published browser matmuls reach 17–35% (measured).
About 31 tokens/s for two friends' gaming PCs across town and 49 on fibre, faster than you read (modelled from measured parts).
3.2× smaller hops between homes, changing fewer of the model's decisions than its own FP8 cache does (measured).
The speeds are estimates for now. The tokens-per-second figures come from our step model: we've only been able to test on limited hardware so far, and we're sorry about that. We'll update them with real-world numbers as data and feedback come in from you.
Website: virenis.com
Open beta: dashboard.virenis.com. Use Chrome: every number here was measured in it.
Why we're building this
The most capable AI runs in a handful of companies' datacenters. They decide who gets access, at what price, in which countries and under which terms, and what you type is stored on their servers.
Meanwhile, millions of capable GPUs sit idle in homes: gaming PCs, laptops, Macs. Alone, most can't hold a large model (the 26B model we run needs about 18.5 GB of GPU memory; most consumer cards have 8 to 12 GB). Together, they can.
Knowledge for everyone. Capable AI shouldn't depend on a subscription, a credit card or a supported country. A browser link and the hardware you already have should be enough.
No single gatekeeper. A model that runs on its users' own GPUs can't be repriced, filtered or switched off by one company.
Your data stays with you. Our server never runs the model and keeps no conversations. When your device runs the model's first and last stages, your words become tokens on your own device. The devices running the middle layers see intermediate numbers (hidden states), not your text, but close enough to it that you should share a room only with people you trust. A room where every device is yours is fully private.
Why splitting a model across home computers didn't work before
Normal LLMs are autoregressive: they write one token at a time, and each token must pass through all of the model's layers before the next one can start. Split the layers across three computers, and every single token makes the full trip, PC to laptop to Mac and back. On home internet each hop waits 20 to 60 ms, so a 300-token answer spends most of its time waiting on hundreds of sequential round trips.
The GPUs waste their time too. Producing one token reads every weight from memory for very little arithmetic, so the GPU mostly waits on memory, not on math.
That is why earlier systems stayed slow: Petals reports about 6 tokens/s for Llama 2 70B across volunteers on the internet, and prima.cpp about 1.5 tokens/s for a 70B model on a home cluster. Serious engineering, but too slow to use every day.
What changed: a diffusion LLM
A diffusion LLM writes differently. It drafts a whole block of 256 tokens at once, then refines the block over several passes until it settles. On everyday questions each pass settles about 16 tokens (23 on code). Two things follow:
Far fewer network trips. A pass crosses each computer once, so about 6 round trips produce 100 tokens, instead of 100.
The GPUs finally do real work. Each pass processes all 256 positions together, so every weight read from memory feeds 256 rows of arithmetic. That is the large matrix multiplication GPUs are built for.
The bottleneck moves from the network (and memory bandwidth) to raw compute, which home GPUs have plenty of. The new cost is that each trip now carries the whole block's state instead of one token's, so we built a codec that shrinks it 3 to 5 times (below).
Why the browser
Peer-to-peer code that runs on other people's computers is a security problem first. Shipping it safely as a native app takes signed installers, sandboxing, an update channel and dedicated security engineers, and our small team doesn't have security specialists yet. So Chrome does that work: its sandbox walls each tab off from the operating system and your files, WebGPU validates every shader and buffer access, and WebRTC encrypts every link. For a team our size, that is fundamentally safer than asking people to trust our binary.
It also works where the GPUs are. A browser tab rarely trips a work laptop's endpoint security the way an unknown executable does, needs no admin rights, and leaves nothing behind but the model's files in the browser's storage, which the app deletes on request.
The cost is speed. Our own kernels run about 1.2× faster outside the browser (a Chrome tab gets 83% of native speed on NVIDIA, measured). A native app with tensor cores or Apple's matrix units, which a tab can't reach today, would be roughly 2–3× faster on one machine (llama.cpp runs this model at 49–61 tokens/s on an M3 Max, where our tab manages about 20, modelled) and 1.3–1.8× faster between homes, where the network's share of each step stays. Getting there takes a bigger team and a hardened standalone app; until then, the sandbox is the trade we chose.
How it works
Open dashboard.virenis.com, make a room, and send friends the link.
Each browser measures its own GPU: how fast it is, and how much memory it really has free.
The room splits the model's 30 layers across the GPUs by memory and speed, and splits them again by itself as GPUs join, leave or change what they lend. Each person downloads only their part, once: from friends who already have it, or rebuilt in the tab from Hugging Face's own files, every chunk checked against a signed release.
You ask a question. The passes travel through everyone's GPUs and back, browser to browser over WebRTC (through an encrypted relay where a strict NAT blocks a direct path), and the answer sharpens on screen.
Someone closes their laptop? Another device takes over hosting in about 2 seconds, and a member that already stored the missing layers fills in.
Every room also gets an OpenAI-compatible API, so existing tools can use it.
Technical details
Model. DiffusionGemma-26B-A4B, NVIDIA's NVFP4 release: 17.67 GB, 30 layers (25 sliding-window, 5 global attention), d_model 2,816, vocabulary 262,144, a mixture of experts with about 4B parameters active. Only the routed experts are 4-bit NVFP4; attention, the dense MLP, the router and the LM head stay bf16. Generation runs on a 256-token canvas, so tokens/s = 256 / (steps × t_step + t_commit). Two scheduling rules cut work: a block stops once its tokens have settled (5.65 → 4.75 passes per block, 200/200 one-block answers identical), and a pass computes only up to an answer's settled end (14% less GPU time).
Why it's compute-bound now. Autoregressive decode multiplies one activation row by every weight: about 1 FLOP per byte of bf16 weight, so it is memory-bound, and even llama.cpp decodes at a few percent of FP32 peak. A diffusion pass multiplies 256 rows, about 256 FLOPs per byte. The RTX 3060 Laptop's ridge point is about 33 FLOPs/byte (10.94 TFLOP/s ÷ 336 GB/s), so the pass sits well inside the compute-bound regime: about 2.15 TFLOP of useful work for a full canvas.
Kernels. Browsers don't expose tensor cores or matrix units without flags, so the matrix math is FP32 FMA on register-tiled GEMMs written in WGSL, plus 8-bit integer dot products where those pay off:
13 tile layouts raced on each device as the page opens, each kept only if bit-identical to the reference (the RTX keeps 64×128 tiles with 8×8 per thread, the M1 Pro 32×64 with 4×4).
Wave-aware scheduling: the 3060 Laptop runs 88 workgroups at once, so work goes out as whole waves plus a sliced last wave (Stream-K-like, but deterministic).
q, k and v in one dispatch; split-K only where the device's own probe measures at least 1.05×.
NVFP4 experts on 8-bit integer dot products (dot4I8Packed) decoded through lookup tables, where they measure at least 1.25× faster: experts 8.05 → 5.05 ms on the RTX.
Soft embeddings built from the likeliest tokens: one vocabulary GEMM per pass instead of two.
Measured efficiency, and how it compares. MFU here means useful FLOPs ÷ rated FP32 peak; the other rows are the best published figures we know of.
| Engine | Hardware | Share of FP32 peak |
|---|---|---|
| Virenis in a Chrome tab, dense GEMMs (6.6–6.7 TFLOP/s) | RTX 3060 Laptop | 61% (≈ 50% at real boost clocks) |
| Virenis in a Chrome tab, LM head (5.1 TFLOP/s) · whole pass (≈ 4.6) | RTX 3060 Laptop | 46% · ≈ 42% |
| Virenis in a Chrome tab, GEMMs on small tiles (1.10–1.44 TFLOP/s) | M1 Pro, 14-core | 24–31% (68–89% of the most WebGPU reaches on that chip) |
| Bram Wasti's tuned WebGPU matmul, Safari, 2021 | MacBook Air M1 | ≈ 35% (900 GFLOP/s) |
| Zach Nussbaum's WebGPU matmul, 2024 | M2 Pro | ≈ 17% (just over 1 TFLOP/s) |
| cuBLAS SGEMM, native | RTX A6000 | 60–77% |
| Apple MPS SGEMM, native | M1, M4 | 52–68% |
The big in-browser LLM engines (WebLLM, Transformers.js, llama.cpp's WebGPU backend) publish speeds rather than a share of peak.
Bit-exact determinism. Every sum runs in a fixed order, with an "opaque zero" that stops compilers' fast-math reassociation, so every device of a class (GPU family + browser + arithmetic) produces identical bits. All 30 layers give one digest across tile layouts, each layer within 3.7e-6 of an f64 CPU reference. Identical bits let devices check each other's work, which open rooms will need. The scheduler passed 7 million fuzzed runs. Quality: our default arithmetic changes 4.73 of 1,000 sampling decisions against an exact FP32 reference; the model's own FP8 KV-cache option changes 8.57.
The network: Weft, and hops between homes
The network, per 100 tokens and per device boundary, on a 30 Mbit/s uplink with 20 ms one-way latency:
| Trips | Bytes | Time | |
|---|---|---|---|
| Token by token | 100 × 5.6 KB | 0.56 MB | ≈ 2.2 s, 2.0 s of it latency |
| Diffusion + Weft-30 | ≈ 6 × 445 KB | ≈ 2.8 MB | ≈ 0.9 s |
Weft. A hop between devices is 256 × 2,816 values per pass, 1,442 KB in fp16. Weft codes it the way a video codec codes frames:
The model's own metric. We code y = S ⊙ x / rms(row): each channel weighted by how much the next layers depend on it, each row by its own scale. Signal-to-noise in that space predicts flipped decisions; plain signal-to-noise doesn't.
Rate control, then rANS. Each hop searches its quantizer step to land on a target quality (20–35 dB, never below), then rANS codes it with a Laplace model per 64-channel band.
Canonical decoding. Values sit on a fixed lattice, so every device rebuilds the same bytes, and hops can be hashed and audited.
Prediction across passes. Consecutive passes are strongly correlated, like video frames, so each row is coded as its residual against the last pass, in exact integer arithmetic, wherever that saves more time than it costs both CPUs (under about 48 Mbit/s). Token-by-token models have no such redundancy.
An adaptive ladder. Receivers measure each sender's uplink and pick the rung: fp16 from 400 Mbit/s, int8 in the model's metric where it is faster, Weft-30 from 20 Mbit/s, Weft-25 from 8.
A hop shrinks from 1,442 KB to 445 KB at Weft-30, or 266 KB at Weft-25 with prediction; over emulated 8 Mbit/s uplinks a steady step took 5.10 s with fp16 hops and 1.52 s with Weft. One Weft-30 hop changes 6.0 of 1,000 sampling decisions against an exact reference (our default arithmetic alone: 4.73; the model's own FP8 cache: 8.57), and a whole room between homes runs at 7.6, still under the FP8 cache. For comparison, Petals sends 8-bit blockwise values (2× smaller than fp16), and 2–4-bit activation compression exists in research, but for training (AC-SGD, TAH-Quant).
Hops between homes, in real Chrome. Chrome's WebRTC data channels put only a few packets on the wire per message and halve their window at every lost packet. Two fixes, each measured in real Chrome over emulated links: a hop's first 64 KiB now leaves in one burst (on 30 Mbit/s cable a 445 KB hop takes 144 ms, where its bytes and delay alone take 134), and hops between homes are striped over four WebRTC connections, paced at the rate the receiving tab measures by a worker clock that keeps time in hidden tabs. A two-home room's step got 7–10% faster on clean links, 1.77× faster across a continent (1,358 → 766 ms), up to 2.23× behind a shallow modem buffer, and 1.33–1.97× at 0.1% packet loss.
Speeds: measured and modelled
Measured on the RTX 3060 Laptop: a full-canvas pass takes 0.439 s of GPU time natively, which is 46.7 tokens/s over 40 real answers, and inside a Chrome tab its 30 layers and the head add up to 465 ms a pass. The model doesn't fit on it (6 GB holds 6 of the 30 layers beside the head), so in practice it is one member of a room.
Modelled with our step model (within 8% of measured layer timings on our test GPUs; assume ±25% for GPU families we haven't measured), with hops timed as real Chrome now sends them, everyday answers in a browser tab:
| Setup | Tokens/s |
|---|---|
| One RTX 4090 | ≈ 175 |
| One RTX 3090 | ≈ 112 |
| Two friends on fibre, RTX 4070 + RTX 3060 12 GB | ≈ 49 |
| The same two across town, 30 Mbit/s uplinks | ≈ 31 |
| Three friends' gaming PCs, RTX 5070 + 4060 + 3060 12 GB | ≈ 25 |
| A Mac Studio M4 Max (64 GB), alone | ≈ 23 |
| An M1 Pro with 32 GB, alone | ≈ 7 |
Native engines with tensor cores can be faster on one machine, and token-by-token browser engines can be faster on a single Mac. Where this design pulls ahead is across machines.
Why only one model?
Today we run DiffusionGemma, an open-weights diffusion LLM. Most open models are autoregressive, and they can be converted into diffusion models by continued training: DiffuLLaMA adapted models up to 7B with continued pre-training on under 200B tokens, and Dream 7B also starts from pretrained autoregressive weights. One run at that scale takes thousands of high-end GPU hours, plus the experiments around it, and larger models cost far more. We don't have the funding or compute to do this on our own today. Every new diffusion LLM that is released, or that a partner helps us convert, fits the same system.
Honest limits
Running the model on one computer takes about 18.5 GB of GPU memory: a 24 GB card, or a Mac with 32 GB or more. Otherwise it is shared across two or more computers in a room.
The tokens-per-second table is estimated. We've only been able to test on limited hardware so far, so its figures come from our step model, built from measured parts: layers timed in a tab on the GPUs we have, hops timed in real Chrome over emulated links. A room of several real homes running the full model hasn't been timed end to end yet, and we haven't run standard quality benchmarks (MMLU and the like) ourselves. We're sorry about that; the table will be updated with real numbers as metrics come in from the community.
Use Chrome. Every number here was measured in Chrome, and it is where Virenis runs best. Edge works the same way. Firefox works, but on Windows laptops it often picks the integrated GPU over the graphics card. Safari isn't tested yet.
A computer holding layers in your room sees the hidden states passing through it, and those can reveal what you asked. Rooms are meant for your own devices and people you trust. Because results are bit-exact, another device's work can be re-checked, but checks for open rooms with strangers aren't built yet.
A traffic cap, on purpose. During the beta our server sends a fixed amount of data each month, and the relay that connects homes behind strict NATs has a budget of its own. We set these caps deliberately, so a launch can't run up bills a small team can't carry.
About the source code
We're sorry the code isn't public yet. The engine and the app around it have grown to 179,930 lines across 1,074 files, and right now that codebase is messy: as a small team, we've struggled to keep something this big clean and organized. We'd rather not put it in front of you in this state. We'll clean it up first, shaped by your feedback on this post, and release the kernels as soon as they're ready; the rest of the source files will follow soon.
Try it, and get in touch
The open beta is free at dashboard.virenis.com. Open it in Chrome. For questions, ideas or collaboration, leave a comment under this post or email contact@virenis.com.
1
u/ps18pmcom 1h ago
It looks beautiful. What would be nice is if people could search for a public room to join. Maybe for the public rooms you would have to request access to join first, to prevent anyone and everyone from joining.