[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 73.7K followers Created: 2025-07-18 16:18:55 UTC 𧔠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 âshardsâ 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 NVIDIAâs 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 DeepSpeedâs 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 XXX 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 XX %. Quantized shards mean less RAM, faster math, and smaller network packets, which is why the system streams â5.8 tokens / s versus XXX 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 RingAttentionâs 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.  XXXXX engagements  **Related Topics** [shards](/topic/shards) [checkpoint](/topic/checkpoint) [Post Link](https://x.com/rohanpaul_ai/status/1946243293391794472)
[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 73.7K followers
Created: 2025-07-18 16:18:55 UTC
𧔠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 âshardsâ 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 NVIDIAâs 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 DeepSpeedâs 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 XXX 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 XX %.
Quantized shards mean less RAM, faster math, and smaller network packets, which is why the system streams â5.8 tokens / s versus XXX 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 RingAttentionâs 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.
XXXXX engagements
Related Topics shards checkpoint