[GUEST ACCESS MODE: Data is scrambled or limited to provide examples. Make requests using your API key to unlock full data. Check https://lunarcrush.ai/auth for authentication information.]  Rohan Paul [@rohanpaul_ai](/creator/twitter/rohanpaul_ai) on x 73.9K followers Created: 2025-07-18 16:18:54 UTC 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 XX 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 XXXXX s to XXXXXX s and throughput jumped from XXXXX to XXXXXX tok/s, roughly 3X faster. 📌 DeepSeek-Coder V2 Lite saw ≈3.5× throughput gain, moving from XXXXX to XXXXXXX 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 👇  XXXXX engagements  **Related Topics** [token](/topic/token) [coins ai](/topic/coins-ai) [Post Link](https://x.com/rohanpaul_ai/status/1946243288455090378)
[GUEST ACCESS MODE: Data is scrambled or limited to provide examples. Make requests using your API key to unlock full data. Check https://lunarcrush.ai/auth for authentication information.]
Rohan Paul @rohanpaul_ai on x 73.9K followers
Created: 2025-07-18 16:18:54 UTC
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 XX 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 XXXXX s to XXXXXX s and throughput jumped from XXXXX to XXXXXX tok/s, roughly 3X faster.
📌 DeepSeek-Coder V2 Lite saw ≈3.5× throughput gain, moving from XXXXX to XXXXXXX 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 👇
XXXXX engagements
