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8 sparks cluster running

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Alex Ziskind

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111,427 次观看 • 6 个月前 •via X (Twitter)

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Best participated Assembly Constituency - #NelloreRural in 1st place. Best participated clusters - Cluster 2, cluster 9, cluster 8 from Nellore Rural. Cluster 2 - 3rd place Cluster 9 - 9th place Cluster 8 - 10th place 3 clusters from Nellore Rural in top ten in the state.
1:11

Best participated Assembly Constituency - #NelloreRural in 1st place. Best participated clusters - Cluster 2, cluster 9, cluster 8 from Nellore Rural. Cluster 2 - 3rd place Cluster 9 - 9th place Cluster 8 - 10th place 3 clusters from Nellore Rural in top ten in the state.

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Running Qwen3.5-397B-A17B (4bit quants, 177 GB) on two DGX Sparks using llama.cpp with RPC and RDMA:

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Cluster x Mind Network Every payment an AI agent makes through an open standard is public by default. Sender, receiver, token, amount, permanently onchain. Fine for a hobby agent. A non-starter for a business running agents at scale. Mind Network is fixing that with Cluster.

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AMG cluster
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AMG cluster

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THIS DEVELOPER CONNECTED 8 NVIDIA DGX SPARKS INTO ONE CLUSTER - AND RAN AN 800GB MODEL THAT MADE HIM 10X MORE PRODUCTIVE 21:47 he says it straight - "this is a terabyte of VRAM - we ran Quen 3.5, 800GB on disk, a model that doesn't even fit on a single Mac Studio - 24 tokens per second - I'd say that's a win" 8 Sparks connected through a $1,300 switch via RDMA over Ethernet - each node adding 128GB of memory into one unified pool of 1TB started with one Spark at 3 tokens per second - every added node doubled the speed - and eight together deliver 24 tokens on a model that physically cannot run anywhere else Kimi K2 at 600GB loaded in 15 minutes, 115GB per node, 13 tokens per second - a model that simply cannot run on anything smaller Claude helped configure the entire cluster - SSH mesh across all 8 machines, network config, jumbo frames, QSFP port speeds - all from one terminal most people rent cloud compute for models this size at $2,000+/month - he built the cluster once and now every token costs 20x less
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THIS DEVELOPER CONNECTED 8 NVIDIA DGX SPARKS INTO ONE CLUSTER - AND RAN AN 800GB MODEL THAT MADE HIM 10X MORE PRODUCTIVE 21:47 he says it straight - "this is a terabyte of VRAM - we ran Quen 3.5, 800GB on disk, a model that doesn't even fit on a single Mac Studio - 24 tokens per second - I'd say that's a win" 8 Sparks connected through a $1,300 switch via RDMA over Ethernet - each node adding 128GB of memory into one unified pool of 1TB started with one Spark at 3 tokens per second - every added node doubled the speed - and eight together deliver 24 tokens on a model that physically cannot run anywhere else Kimi K2 at 600GB loaded in 15 minutes, 115GB per node, 13 tokens per second - a model that simply cannot run on anything smaller Claude helped configure the entire cluster - SSH mesh across all 8 machines, network config, jumbo frames, QSFP port speeds - all from one terminal most people rent cloud compute for models this size at $2,000+/month - he built the cluster once and now every token costs 20x less

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A supercomputer simulation of a massive galaxy cluster (TNG-Cluster) forming over 13.8 billion years.
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A supercomputer simulation of a massive galaxy cluster (TNG-Cluster) forming over 13.8 billion years.

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running Qwen3.5 397B MoE (17B active/token) on 4x DGX Sparks in FP8 (~400GB) > OpenCode driving > agent exploring its own config > probing all 4 Sparks (via ssh) + reporting thermals > inspecting how vLLM is serving it > collecting + analyzing its own stats local AI is awesome
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running Qwen3.5 397B MoE (17B active/token) on 4x DGX Sparks in FP8 (~400GB) > OpenCode driving > agent exploring its own config > probing all 4 Sparks (via ssh) + reporting thermals > inspecting how vLLM is serving it > collecting + analyzing its own stats local AI is awesome

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