its not! AO3 is open source the code is on github to repurpose, squidge is a more modern webframe cuz ao3 is super outdated and doesnt have coders equipped to fix it :/

@osvaldo @angelo @webframe

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Or something in a webframe ie 99% of everything these days

On the latest TinyLlama 1.1B benchmarks (MacBook Pro M4): Unquantized: ~8× faster than llama.cpp 8-bit & 4-bit quantized: still leading — and getting faster month over month From July → August, every variant of webframe-cpp improved its speed.

🧵 2/n But how exactly a model can be split, and still it will generate response to my question A transformer is just a long stack of math layers packed into weight matrices that live in RAM. so webFrame starts by slicing the full checkpoint into several “shards” on disk. Each shard holds only the slice that a given computer will need, following the same tensor-parallel idea first popularized in Megatron-LM. When the cluster boots, every Mac loads just its slice, which keeps memory use under control. Once a prompt arrives, the layer-by-layer forward pass still happens in the usual order, but matrix multiplications are now spread across machines at each step. For example, if a weight matrix is split column-wise, every Mac multiplies its sub-matrix with the same input activations at the same time, then they share partial results through a lightweight all-reduce call, the same trick outlined in NVIDIA’s tensor_parallel docs. That collective sums the pieces so the math matches a single-box result byte-for-byte. Layers can also be staged across nodes, one chunk of consecutive layers per machine, which is called pipeline parallelism. webFrame mixes both methods, so some Macs hold early layers while others hold later layers, and each stage may still run tensor splits inside. A tiny micro-batch of tokens flows through stage 1, then hops to stage 2, and so on, overlapping compute and network time the same way GPipe demonstrated years ago. During generation, the loop is autoregressive: produce logits, pick the next token, append it to the prompt, repeat. The node that owns the final linear-to-vocab layer collects the last hidden vector from its peers, runs the logit math, samples the token, and streams that single byte back to the caller. All other machines cache their local activations so the next step can skip recomputing earlier layers, which cuts latency the same way DeepSpeed’s inference engine does Because the shards never leave the building, only small activation tensors and single-token logits cross the wire, so user data stays on-prem. Thunderbolt or Ethernet moves those tensors fast enough that webFrame still shows the first word in about 1.8 s for Llama-3 70B on a 4-Mini cluster To squeeze even more into each Mac, webFrame applies entropy-weighted quantization so low-information layers drop from 16-bit to 4-bit while keeping accuracy over 99 %. Quantized shards mean less RAM, faster math, and smaller network packets, which is why the system streams ≈5.8 tokens / s versus 2.3 tokens / s for an unoptimized baseline. Finally, Navigator, a daemon that ships with webFrame, auto-detects the fastest path between nodes, builds either a ring or full mesh, and sets up the collective calls so engineers never hand-write MPI scripts. If one link slows down, traffic reroutes mid-stream, echoing the ideas in RingAttention’s overlap-compute-with-communication design. In short, webFrame works because matrix math is naturally splittable, collective ops can reassemble results with millisecond overhead, and a thin orchestration layer hides the wiring, so several modest Macs behave like one giant GPU without leaking a single token to the cloud.

Wow this is such a brilliant idea for running AI models locally. 🎯 webFrame is @thewebAI 's backend that slices a huge language model into smaller shards, sends each shard to a different computer on your own network, then stitches the answers back together on the fly. Because every shard stays local, no token or user data leaves the building, and even a modest Mac Mini cluster can serve a state-of-the-art model in real time. Its redefining what’s possible on local hardware. And they just published their benchmark results. 📌 webFrame pushed out ≈3X more tokens each second than a SOTA open‑source rival on a 4‑Mac Mini cluster 📌 First token showed up ≈35% sooner for Llama‑3 70B Basically webAI compared its webFrame inference stack with a well-known open source cluster framework on identical four-node Mac Mini M4 Pro machines, each with 64 GB RAM and shared prompts. The test used Llama-3 70B (4-bit) and DeepSeek-Coder V2 Lite (4-bit), measuring time-to-first-token (TTFT) and tokens-per-second (tok/s). 📌 For Llama-3 70B, TTFT dropped from 2.732 s to 1.7868 s and throughput jumped from 2.342 to 5.8055 tok/s, roughly 3X faster. 📌 DeepSeek-Coder V2 Lite saw ≈3.5× throughput gain, moving from 9.284 to 32.4747 tok/s. Users keep data inside the building while still getting sub‑2‑second answers. No data leaves your network. No vendor lock-in. No compliance headaches. No expanded attack surface. Why it matters webFrame shards a model across local nodes, then coordinates them through its Navigator tool, keeping data on-prem while squeezing more work from the same chips. Flexible networking—Ethernet mesh or Thunderbolt ring—removes the full-mesh requirement that slows the baseline. Faster responses mean fewer machines, lower power, and private, real-time apps for health, factory, or edge settings. 🧵 Read on 👇

We benchmarked webFrame against another distributed inference framework on identical Apple Silicon Mac Mini clusters. webFrame delivers 2.5-3.5x higher throughput and 35% lower latency on the same hardware. Data speaks louder than claims.

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How does webframe compare to github.com/b4rtaz/distribute…, what would a basic pro/con list between the two look like?

Imo if an integral feature in an OS now requires a webframe to draw, something’s gone deeply wrong with the feature itself

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