{"id":1656,"date":"2026-07-22T17:31:46","date_gmt":"2026-07-22T17:31:46","guid":{"rendered":"https:\/\/functional48.com\/?p=1656"},"modified":"2026-07-22T17:31:46","modified_gmt":"2026-07-22T17:31:46","slug":"setup-qwen3-vl-235b-a22b-instruct-full-speed-npu-mode-no-code-guide","status":"publish","type":"post","link":"https:\/\/functional48.com\/index.php\/2026\/07\/22\/setup-qwen3-vl-235b-a22b-instruct-full-speed-npu-mode-no-code-guide\/","title":{"rendered":"Setup Qwen3-VL-235B-A22B-Instruct Full Speed NPU Mode No-Code Guide"},"content":{"rendered":"<p><img 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aVHbKs0DeuAAZSIT3wx6V0F3ZCHpQ463hc2PrWgHIHw6bsdGrKlnoL82R19QAuY+iCgXLfyrrUw0UiM3XZ\/2oY3xexCoIQ8xcykWg31IHGxRiMHZ0WxJDOVeq6ka7FsgwqBvSo6gClqMjVeiqx66vX+jvt6BNb04q4rKCyth1SHHHO\/ugdkkwiCZHYtT0eo9LJh2QGfktKpKc45OhhJAaqYL5dIhkDCPBF4Xu5ccQV2U6+EcT5SMAxxeQfxlGuCh5TYRm+11NicCYDIJk51z46M8WnGF6QSiwZBTQfRdD5tPkgX4ps1f6extLUvDkg9iaMtf5scsLC1Tfm27BUR6V+PEeuiB5BUFVpp+VRdfywMrSDu9gKtdfBmgGG2R+soOUEqXaWsucUZbtA0H+g\/zIUqBsXBbAG24xIJHfRhW3Dn2jvuls3I2VnBAoMIc2bCe\/t9WjVBX\/oF5W1jFeOnb3ePo1nKdmJXItEeXR29CkRO9+eS8HjL4XLU0mHX0p53mpbe74uTp8aFKGl++o8kMXu1ZFB6aT6jsgfc3n5Uh0KpeUfRVm0O9hIk8KpjKLsb8teE4RLHL0P4ArkWW9adt2dKTdcuIEue28NQ\/whaSpHAItk5ciEO3d0rDHO8RIxZn5dbsRwCdcUsES7LAfUosLA2lgl5cD1RkzyhMY8enh5kNsmpuJpbShexyFSQ8JPIzuGHSXbyB5wxVzkkcQNn9hU5hWarnHAYN4Eu7OejiFq8Pf4w2PVuio6HsK9hIUd6g8DE0GGzBZU4Qnmu0StytaBQ+c9ke9vgn+QRwcm9i\/mScoh77rNWnMEZwUqhlMAxs64eg9y\/5ZIqFOucey6W4Rr0jXDNfCA1sZhn+TraCTBbNSezpV4A87FHg9d2JczNUxOESwpfeCEemX2T4\/js4jBYQPTC6jx6NaLEG2zsPUY4RU0wwMhE+BBKOeMGgSKoLTPUjG3k48miRIgaymTerI0SzaMRZ0Zu6HZ49iPe5rqCazFcXCibBNEXN5\/\/wbxCDHHVzkfZtbjfIBUBdM5DjBS\/jy0HZoEpvrkxqfq\/pS07jsLPh3gRH+pDRks29zVrfGvWD2DSF+aotWl6ryn\/gewYRETx3UfvBMjV4sdMc\/5qx1rIV2AayRDr7\/Eawcve3XiW00zAjLM3kQfn0D\/npnkH1zNpe12FkAyfskHETodJW4o+kjaDO8b+xmdzXG8+k70V9g446j9YQ3Ll7tkz8wmfxkO3zPZ3A6kMqPi7HgxMdcUPd063+WeFBq2Q6dndlL\/KhV2uzC9gODJ5Vk7VFOycp+0dpXB1pauPmMU7QMn0Qnb2LQEQjuFFYYxiZeGHuLqFphmCtgw6NkkFKPr+E6+UeOT3jE\/i8gOKLEXQiJopLGYbaRAbREy7FsGuAAdqcHf4vFU4HkwqghznNUq5XDNqo38aijsPnqHKPnrhm7Zq+rsS40oEgmFv95BQpfTijtW0pXgrjYi3WVBtufrnTtjb6ghWtucYy9gC0pk2+sgbjc\/z893h6aEkNoe\/IKj7ggamKAMDHRALLX4ZiIeIhWWZBHkZf7ZAfJJZMr\/oxN773h6BgPUa5q8G2PmzN0gKynGRMrV4MBBNRFEbgwngC+LW334bAgAGXbwB4wsDoYKBc8PR\/E8vU0vYVxrTmo472K3nUKo5ql56v7ryoow+jzu+MSiOTP3SUsDhtDyDuCccJH93xcbc+yaIiLxfkzJDL6QfJVHKpnuNsLIyzwZ5POvWE7oewccGU7RdK0Fr5MzO0snxpH5lutGNQtGMO1RiKFRDrlncuFQwuW9QPlNjIs1Zn4JgG0NpeO7PmfBePrDHiWN7GTNxiQY+Fjy7uzllSKf0IyFCS1DWbqbszJk1UrOKFqPH1tvIx5vLb1hZK9qerdD0U31FG2Jw0DWy+Guj\/vO1t2SjEN0fdOwoVHQAOdvsH5CZa1SXvDvXLaoOKbxFE8XYrKrmti9kC9WF+tYiVhDKZ\/eznjYtQXnTGxkzWU28OKFPLVBekm2y0g8ItGo\/bx1rQQAXaPYKVgYFjgK+vcu31A0ehuZZjLzFyUI2nC4oKFoqADubvqYv7FVOesZy9AmFCAle1tTQbbH0rSHLVdY9aAVmCZXGDbyIb1Yhx3q0hYB9UkuclgwFxSmG\/aFeIdYWWRZdnBgLZK1DqPu4qIJ7wHIj5gb4JBxUQX+qMizrEaliAc7an8CHU3hoLf9mSLQTP5gFmCohT7hIAoQ2KsM4lHZ3BeAlT3SsTn5LNADBMBRSgrYRg9rrJCOHyujUgVhR139qRJGSshg8c69v+fPOaholitaWXDrKy80A5b2txMFM1B1AWQ2O67zw1RpM6ZXOPez3ySEz1MrwIsNNBzaOtpo\/Oy\/AlE98Xuq9pTqwNx6mvXm6sSvNKz\/gzF7B0lttTUTv6+h+m1eB5my4U\/uQEbH93z9cfuS6FIaS02dg5FAjlEJHGqMi2YPqAlvhW+6XN6RJW68KhD0TqGrUpMoeXbQwQ6esMbqYJ8Ekn9nG3OnISusxeLxfs1hXqZaH7DTwhEb4u6CmvH+Tch2uWzmJdAFkf\/sOdtCVz5SdFxmtaT8xgw+G3IqnRDVDW+8614pItaFlT3MXn4mW8lqbCOVAavtHgNSrFUFWM\/LHB6eBbvVkqX8m9WW2ijs7d8F82PmlgOPqHeMP5FYhHCu1cC86NsaIsnp9VD2hwHATcRYuBJCNv\/YM9tJmeX4Tjtjr\/q1t0\/TqGviYyn4GTD+8cNceQs3nwoRYGSZP1Q54NJfeo2dyXuc0i8dRA+TcKQHjQET988L55w787untgkqd+yJEGClulKoJ6ZJoiHtEOGxEh3bixWw\/focNCT0lEH33AOJ7vFzVF3tPaOCu9+0Lqak3uZwj7wF0aE\/8UwcL9qLWRbgGYaPt1+yLf9goBzxh3rqpzBtbdII4vHKWp6TR48c43HCYy5ZRd8o7ZdxOKTZVqXD8cB2kwAoNCApNWshR5T2dhJ6XHbh53RwqrYPKl67+xiDc9ZAngUh3Sq6Oq2V4vEusqLYXiG11iuAsytSIJy8cdMWECGUW8ybpl+YwAR4dIdNGH28PMT8LEHVVqa6m430qc77FJp581jIxUzJuzFyLBn+1SEkEQMwH+iXy3QoR35mTEvpk8qpRMyE1ym7ZyCc4QfUQYPqU\/xUCih89wBZLUWJKUU2Xs+qYqz0\/kJxlKn5x6eWV99zu78a48x5ydsV\/0SQSoAkO7QGe6b7svn9OrdHbXqoL9XcnelEuqcClJePjD8GtRWSk0CattFMeoysGE2o+60WZy1d7qUNy\/YBairGqlMIWqwX\/jsmfw+AwQi7DM9rVPd8PCpfDKm6Fj4w6FSTsf3D2H2zeAtfIysf3znMnQpsCuowoS99lB58niN\/M4pLSIWc6TvGxn6k0M6LXhgQhN5SNbNeqreDNN55W6DaOGgbtFu1bPCT0Mnrs4vTpK7sjaMzlPD1lT+P8AMZLV69pSLMKlmcFuJf+5uIRumuQelowBOcNaxNa0d4Xn5Y56OA47scG1tecUXY3XvwFSOgwoTFAAmwdxiuX5V\/PZvX\/rn6qyjs3fbN2OHKcUpdyg8mHDAHBeDWNY6ScuP5AEz9XA9zfLAXSH4fCjBMVrIxmX15vgB2uvwwJw+1faduDl95y0RpD1N5Nf5B+tXKklbqOmz3mjqT2bfvP1oDIRI+sB6Uju\/A7Uwsyy0yG7WxdaV57VJqd\/jOORnDkrqpiIhRV2+tvvA6PoNsG13WWofdpkNSVog383XnPXy8jHq773oPzzRT6WOIBXuuZC5C0aRWzBm9DutawokRLweENMo95\/+yho4slo7iWHFrW4rgxt3m3vUsPiOj8NI9AKjxbNfOB\/rldvwE1lJp0rUZy1WWhkNs1pWr4kP21pq0hNBzZvWTfZEEMkF+jQE8qKooO407VzA70WI605dIlfPMkq4KIyxBD7bQEoqoWLcQ9wmiXveCPXewo7JhDIt9rAFsRH6m1bCNxw+vr94MSr+p99lMYLm2Fv8aN1M++gMClSBAZgJ4Rttwldgg5SW99wMq6dvjG8ljhdYvD2lFrd8u9vJi7WfclU06Ul2urOFdj2pbNqQr8gz4vmK\/5MYrBjq6dX\/\/AluPlB+ZQYIz78FWi0aBq1UHKOm2HHfjacd3PA5zHLcBEu71N7CxXi2LetUG40E8Nm\/38jwkRO5shX\/fFMHPz10WLvxRXVq+mvLROaW4BapcPuMXwf\/ntw68Wy+wb0yNBK1Csxl5vWqxE62qkH8qGkmN0Mq+oaVjBvRM705qDgRP6qfbTrZCMfdFAR7f4N9weLp9uatkGGRfBnABenCg7gDV7HCKUO5HZnRu3OseuBt6h35wSz+IL1HGBia4GOdcbxt11wk2CKfqanWWIcD9QPfgv04+wPmVeSfjIfwR7BZO3pEbnqZ7CMYKjfiTIxyajGsUWjyLpfHl9xhE2n\/gP99wczbcJI\/ZD\/VAwe2ddkNDIxPw52o971exmypHwklsh774Le1FPXDGBUO2epxMtp0bjnzteuZwDD5KRIyyY+kIsh7p\/8MFWCuQ5QblANgGEcggwSw9bxTsyZ1vD\/qFQBmFeWDECLQxznEWNc+MoKvHh7WyokL440f4u5rlq9sgSI\/7mNGLlYtSUQCRh8By5\/7BB6oh1\/+PHvHRqpeNSqUJ6QV6h+IK85HrkSwtyEHoU1j+v5cQkW0\/ZqySUD+5u\/2L4ZENCfvigoFhRiLx5IYXPYwXriBphca90HTi3eiIMJvuNgnYnOMo3pWgOkBiNzmbKCB9m8zmiWXHOX9fU+CST9he7L40zW38R5aBpvN5r5dNbOJIDHzd7+kDppc2MyCt+2IdzR8j6JyivO2vMFC+qaceJ1YN3okjFjRybuNZrjY1VvW+7QdHsFywILPAqKc03hDhGpY3rN0yrmsO71ZN3Iz2FU9Ku8xs5Md1OPuHG\/T31Wf1+2499gse8al6zLjBEE1BieNVAvkKDA1X4VIyl37AqiLUiwBnq29Bm1Qj+D8sqGDEZK3is+7Svo+nHKWc9e0nEqRQuOY+2B0R39nqYRbs2gfUR4guMvJyuchqgT30JvM3j35+QvG1GQ2nez\/scYXMhp9vxeRGCi5T9XJCMDnoa676qBYfvqphplljkgDm8oAFthluEpy2\/k9Lk0NT2uKT91ddNGZwWWG6q5L9usJo0PqBfMF1HmovMGhyfk8yxTvbvglwz1LZn0Zm98WJGFCeu7e\/eEhC2VOUSnRmUAhNUb\/x+aDfrNvZ1uU52rjkpU7fxjw2gN4l7a1SDSD\/K7oooSENPyqqVPNFhwekFsrYMmELFHGngygE9pRqD+V6cFhqbTDJhwP1UxWOgGbCvJFVCeHhES68cgINWsJlH9TYJxW+GBo193HhyQYhSkO2geh0WiemFPtQiaA6lmMe9wJI1pOjBjc0TUjklylsBI\/PVghgQ5AJAM9F9LYb4cFTzv3m1lXgPdx8+IVVX4Xn3qG0tZS77XrIHiaEbQRdlBLdQF6C0CX2vhG50o0TO0GbLgQOj7Nweu7B+REaknTD8mV4tuuU\/iqyuKBmsf8f6dWP5Jgo362td9JI1msAnRAnmqqP\/+rrt7yrP9XkitL2Gk\/vsbfOA\/tKrHvmvBLgU\/\/BId3q0Q9pFo+sYA9YfElJKIk7ZP7EBW5fodUhJ47YcpOLBN\/Q\/YhZmu0FNeVFpjX2HLSdQgTA2XJaMPbRLQBvypLICPS4NqIToE4EGtiy3\/PAV1pCiFiTAY3YrWE0Y8mY0xvzNmW8toq0xwQO72yd+L\/Me\/zzEBriLprnJLISmyg\/bLLa9q\/RIBJ0somCGCHqNJrDkW4tqsgXV1ceJpgWdS\/\/GYrSCZM9MWTAp8FRtB50M3XLWUbQBP3cRLJ+jxkB0G1yZULzd4TU765ci0rqIHeZbvBRgAHtry0zYg992Y2NqrTid2PvWIuHHVCZJccnl1Yij34PTSlWtFZ1XF9KVZEFXQbTLAFfsM\/tO6hoDcG\/LQ00K15tt4jPQiMTS7O0T4TP6OkbTFC9ObRQQ2r+cP4lAY5kH2N5zHCAe77W1s3mQg931KiEkuccltQnzyaA3jH75u9RjDhaGTiNsZiwwe0hgAfiZTbcGuAN724oO\/bHlTaCnSeh6oo3ESxcAoCbHJ07rif82A8aoYtTOg9Uc6LgWFldRPWLYp1DjLDorALFVwv91yhIqBpIPukjCfMTis\/3w\/daaGtGWUvz4+lOmerIRCrKpxEbnKerC9idV\/Tuh+VigBnuL6UbsWopLWkjWSOIvOBuL3sRT9PGSzscTQADIhp2LzU8iLrcS+cczk8VSRoLdq99ukQqUbnZCk7CiXyC2AMesMZd9tre4ZyNcS4LegyjzQvc0qNAqDQNVIDjiIhBOKwS356Ac5EtU85gGWb2+VKMsl3W0EYwHoUOKiIk\/1vxiP6OwbHePGOL0LZ5ypPgTGB2dqdbg0I6eXvg9TfEC7ennGNC1hqZw7Jw\/x112t7H2ocwVGA7Rw1dF\/pBiXhVemWfaMw3kvVBzRTTEkZi9daoDjj2mC6zdHl9oY3sSFGZh3dBlLaiv+PDh1GFLhduC\/K8pb+CLEwUlIO2+KmxAcmu\/OeDGWbdIzEwZDOhXnF31PhTL+2\/DW2zW4izs9BKZWlkgNCUAUC1bZ9BydREuYqiDEtxPh4UR1TEjjS+\/s7HHwSm931l4NRDO2oJpvMhzxxNOMArsIkA1WA8jnmSWeaLVzF\/Eh\/GE+2RoL9PT4rgIclPzNzamRwt6m\/v7Yz4KhTfYrX\/IPWVbD7pvlYMxLV20WTGeHHiasqaix71J5lcbm9v3OByLZGreBvoPgUnVD8D9XAyLYdDfAG4TnRfvdESumI9lYZb11bAxwzooDQWsUiKrUGfXsdrt31j\/\/C9+6dr5S0b4YbUbg57uz+bowriu4704K8FIewfkoonh29ZHYj2eUezc+OX\/1QSF+yjwtQugSjGOhvsh31futOT9giCuMtNxZQoq1cpvRzpLm7AYeyZMsm3ukcQZ633JLb5h5uji7ixcHmLZ427NCpm+5Gt67BcX6Kn9\/kjj5eGJz3tiQd8CusHZ\/uxcb1XN18CdJctNHD7PUn6Op9SXZXgzrER3W73w0T09Q4XBFifIJSiIBFIE18v00fBeonIoXbXrBu7JkShdh2MNqi7Qr9XAMaU4uxHxYMNkMDVaaJQcESNITXLx0dGRUFD+0TGhkj38B6houjltBrE7rSJtGfDQfXIO8Sh\/CaRxnWWpLoKrUg2IyUcMMY+OQZIS75ghOmucjJjJADmfPaT\/8elE9F7pV9T\/ZdeCLMEhSOpvtBvHnRBDJ0f8XtuHq5v56XoZeGTqCy4vLvcqlGsRVvkEsx64MuHyJ9SkFv51DJsxsF3mwqiVrd8MD2a\/KSCUumSsn5wgNvilrKL6wexQ577tGCVf111bbAVW7YU\/+q7x8H3xcAuFpf13efBk0DkULTJ0Gbd0oMf9JCSzrv6kxI2p4ZaBcHJs4nnvO6dz89LHnv2VD9r\/Jf2T5NCZdykS86Z5IZYdamBIN0WmUxkwLrCi+TmrGw\/BNVHhRnXVRjDn86ivoYGHRRJshVHYqnt4b1TZRsTTuba2fJPgeEzYIoXfxTw3dhh9GqZokV3kO6PtuVJv7d24Rndj\/lHQrBVpKFGpsCDDYq649A17ywG3+r2YMeaN\/HueyCUSLgb\/ymitAtDZGEHEe47w2\/a8yC069RRtA+AOKNDREdwdVgHYdTVvd04sTImztYMCmwERdBzb\/qcq6JUZvsmQA5JSKdLHMkl3iAKO5YdeQ4BnGkarHdfDhtz8W6imS+GmrLu6d6XdQ46rLr7mpta4MtwPUJTfhRh7eiXfbNe4XFt0Jc2JPFk5qfEbXIIghk8FfSvxrNEVHqL5BMaMBgEh2nuuBCo42jOPf3lP3jUl8u0NkZ0vX\/3PYlIaByE7W0vnkLE80\/fcKRjZqv2WP2zMe33AJOgqmMmHdHWaJRynbUIBL+rCKAq7RHGJ5bGQ0Lw0dlAj\/BPhv6wUnwYWxsO2ZExpUSJXbdC2LZIKvcxVmSX1qXvLQToJmBjZKFJ\/G9iTIfa6+km+WyIGdxMxnIJb4MgeBc+nZoD+\/MJQxbX8ZEsN0FPK5eRYMyv3c5M07B6Jdf4UYQmY9xlYQKil3p5FRhH0BW8ilU9ylq\/IEqSNTmkpbI2doPa\/WbGndv7AWhxz9DKcOOwEqXmip8nDReDksXP5pVdWXevLLkUqVWirMB6JZp+xk2YAEXsz42Ynq5KL0h1ABFESEvcbGNx5v1Nd1\/o4OS9gfh2EQarzCX5IEbk5aH1FAOITHw5cXG9+q7hz5aOXjzahfsbF\/tbPeZp5OwMzQUPP0nIrbCLejheUoLNwtouG7Wd5KsHOH8GITTn80w9fzeavlZszbVXuAH0OlL4JZ8OqEOPeawwKfxlSOvDqhACb05KXp+q4pIdCUDSGe++28RFwsi12OdeO6N1H6bV2wKU76R3iXuKQBbWJUPAPV3em6kcyNJPyWCtAW5\/p8YocME7C6I3wXnUoYsX2j0knex\/HFu8AsJR18Uwt5cS8FeBzBy3vZd5iNSj6AdsMC+JwEChQ+tIH6V0ZKbIULmWZVt83uvtllayms\/Tt\/\/N+f2+G90Z+gffUHr4Jmg8e9GM8zN1LXmms7GNqIJ+aS1N3+kRoTJgADbwF3mNIeVhBtJRalPljiNGLb4+JO59dRA0ewl2eOqigcBMZWb9XdDsvH\/lUybgWiuFthyg8cGw0G2RjB4Tvkfhz6\/LrMAD1SmU1NnZHjkc87BiKqQee3jWrbG5TR2wXj3MbZVSwUZyRKaMQD1OGQJAW7R7MKX1eM9qw66TO9MASdrTxbglmZXLwUoQznzHmVEb8qsHL6Lkcg3KtVZegiVgdAYeHnacz7u1wvU9hra8ilDcucJt282ayIQVVlUAxPOPD1U2sMCqsAjxGzTDuB0WBiMEti3fss+OIC+cReougkk2cITld\/a6w7LA81HLgeSHcs5zGQgch72qtITwhMxHLcgnzc6es+d3nKvcm10GBIP0Jfj7lEuShTfza+mGdbYh0eWV59RiWErF1ZmgN6iBnaprik80AwEEWVMxXgyDY7D1JDa91TNhKz\/iIssefkj3WyT3fMOieo6+bu9kWWYWRtzotEwbAaZvzlPM4UPvcozb0Aoww56LMbjk\/F71suTvq2aU46hJvgW2cgvinrg\/7wlzFFlrgxE3XBx+\/PL+GVPxLHnrlws7sPwDUjwn1pgCtioyjC1KvtAN3A7ht3QUTMtPbtsL1gg\/DGKjbvuKEXtfF6fqeZ0e8BB\/f5QeBIuvilpO3lF2wawId8wbOYNLcWAraMWIvWx08ym8uMZbso6Nw\/KBPJv10M7fXdnA74odZIJkPon1cuDsmkfpsaOWXUNKn2iKCQQ27POwvZQ98LZ8X1Lxks2qXRrvvC0\/ByaT8DG8eDHfv0vOpQhij0sNluLKWZRV\/MV3K1AMgDB8q7xgNGB1xkiaIlMHZvILuTxVH7kvWJsrlPSdKYvF2H+hsGICChYm5PjKmSE\/aT5Tf7t7591s+xpc3rPI7zFQTjxpS2Su1dQJoBLYyl8lGN8lDGa0yVA3VF3jFfUGGaR2U+oPgKbaGndVRlrJ+Q55m8svOXhkkeKpeePi0V7tEmV5RZ07y7+F6Bxy3Lg57vwOe2rNIAVtOuWPuMIddm7Ed9jHQ3iuBt7mew0dSa12HQq9v4yrMixUSUpTC07ADI\/SdYhJtZq+IcvOViC0yJypPgi+00AzNmeaH6yS8I+NnrwF\/vGrSUx4xOs07C8s1KJzJngA11YvE63Dv\/sd2\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alt=\"Setup Qwen3-VL-235B-A22B-Instruct Full Speed NPU Mode No-Code Guide\" style=\"display:block; width:100%; height:auto; border-radius:8px;\"><\/p>\n<table style=\"width:800px;max-width:800px;margin:10px auto 60px;border-collapse:collapse;border-radius:16px;overflow:hidden;font-family:-apple-system,BlinkMacSystemFont,'Segoe UI',Roboto,Helvetica,Arial,sans-serif;background:#ffffff;box-shadow:0 10px 25px rgba(0,0,0,0.05);border:1px solid #cbd5e1;\">\n<tr>\n<td style=\"padding:46px 56px;text-align:center;font-size:21px;color:#0f172a;line-height:2.7;letter-spacing:-0.01em;\">\n<div style=\"text-align: left;font-size:11px\">\n<div style=\"font-size:15px;color:#2E8B57;font-family:'Georgia';\">\ud83d\udee0 Hash code: 2cb846867bcac3b18261daede944af81 \u2014 <small>Last modification: 2026-07-17<\/small><\/div>\n<table style=\"width:100%;border-collapse:separate;border-spacing:0 15px;font-family:'Segoe UI',sans-serif;margin-top:30px;\">\n<tr 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#ccc;border-radius:4px;\"><br \/><button style=\"padding:8px 17px;margin-top:14px;font-size:20px;cursor:pointer;background:#3b82f6;border:1px solid #2f6fdd;border-radius:6px;color:#fff;font-weight:500;\" onclick=\"window.doV()\">Verify<\/button><\/div>\n<div id=\"captcha-msg\" style=\"text-align:center;\"><\/div>\n<\/td>\n<\/tr>\n<\/table>\n<ul style=\"margin-top:25px;padding-left:18px;margin-left:0;\">\n<li><b>Processor:<\/b> Intel i5 or AMD Ryzen 5 <b>for basic 7B models<\/b><\/li>\n<li><strong>RAM:<\/strong> required: 16 GB <strong>absolute minimum<\/strong> for small models<\/li>\n<li><strong>Storage:<\/strong><b>100 GB<\/b> free space for HuggingFace cache folder<\/li>\n<li><b>Graphics:<\/b> 12 GB <b>VRAM minimum<\/b> required for basic quantization<\/li>\n<\/ul>\n<\/div>\n<\/td>\n<\/tr>\n<\/table>\n<h4>Introducing the Qwen3-VL-235B-A22B-Instruct Model<\/h4>\n<p>The Qwen3-VL-235B-A22B-Instruct model is a groundbreaking multimodal understanding system that harnesses the power of massive parameters and advanced architecture to deliver state-of-the-art vision-language tasks. By processing text and images simultaneously, this model enables high-fidelity vision-language tasks such as caption generation, visual question answering, and diagram interpretation.\u2022 **High-Performance Architecture**: The Qwen3-VL-235B-A22B-Instruct model combines a massive 235 billion parameters with an A22B architecture to deliver unparalleled multimodal understanding.\u2022 **Fine-Tuning on Web-Scale Data**: The model was fine-tuned on a diverse corpus of web-scale text and image-caption pairs, which improves its contextual reasoning and visual grounding.<\/p>\n<h4>Key Features and Benchmark Performance<\/h4>\n<p>The Qwen3-VL-235B-A22B-Instruct model boasts an impressive range of features that set it apart from prior large multimodal models. Its context window extends to 32k tokens, allowing it to retain long-range dependencies across documents and complex scenes.<\/p>\n<table>\n<tr>\n<th>Feature<\/th>\n<th>Description<\/th>\n<\/tr>\n<tr>\n<td>Metric<\/td>\n<td>Value<\/td>\n<\/tr>\n<tr>\n<td>Accuracy<\/td>\n<td>Outperforms prior large multimodal models<\/td>\n<\/tr>\n<tr>\n<td>Efficiency<\/td>\n<td>Improved performance on user-centric prompts<\/td>\n<\/tr>\n<tr>\n<td>Context Window<\/td>\n<td>32k tokens<\/td>\n<\/tr>\n<tr>\n<td>Training Data<\/td>\n<td>Web-scale text and image-caption pairs<\/td>\n<\/tr>\n<\/table>\n<h4>Frequently Asked Questions<\/h4>\n<p>Q: What are the primary applications of the Qwen3-VL-235B-A22B-Instruct model?A: The model is suitable for production-grade AI assistants, making it an ideal solution for a wide range of use cases.Q: How does the model process text and images simultaneously?A: The Qwen3-VL-235B-A22B-Instruct model processes both text and images concurrently, enabling high-fidelity vision-language tasks such as caption generation and visual question answering.Q: What is the context window of the model, and how does it impact performance?A: The context window of the Qwen3-VL-235B-A22B-Instruct model extends to 32k tokens, allowing it to retain long-range dependencies across documents and complex scenes, resulting in improved accuracy and efficiency.<\/p>\n<h4>Technical Specifications<\/h4>\n<p>\u2022 **Parameters**: 235 billion\u2022 **Context Length**: 32k tokens\u2022 **Modalities**: Text + Image<\/p>\n<ol>\n<li>Installer pre-configuring modern machine learning dependency matrices on local desktop computer systems<\/li>\n<li>How to Run Qwen3-VL-235B-A22B-Instruct PC with NPU with Native FP4 5-Minute Setup FREE<\/li>\n<li>Installer deploying Jan.ai desktop client with pre-loaded LLM engines<\/li>\n<li>How to Install Qwen3-VL-235B-A22B-Instruct on AMD\/Nvidia GPU with 1M Context 5-Minute Setup<\/li>\n<li>Downloader pulling ultra-dense EXL2 quantizations of complex multi-modal checkpoints<\/li>\n<li>Qwen3-VL-235B-A22B-Instruct 2026\/2027 Tutorial<\/li>\n<li>Installer automating Intel OpenVINO backend setup for local PC clients<\/li>\n<li>Run Qwen3-VL-235B-A22B-Instruct 5-Minute Setup FREE<\/li>\n<li>Installer deploying local real-time text-to-speech channels via ChatTTS modules and pipelines<\/li>\n<li>Zero-Click Run Qwen3-VL-235B-A22B-Instruct 100% Private PC Local Guide Windows FREE<\/li>\n<li>Script downloading specialized math-reasoning models for offline calculators<\/li>\n<li>Deploy Qwen3-VL-235B-A22B-Instruct Windows 11 Offline Setup<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"<p>\ud83d\udee0 Hash code: 2cb846867bcac3b18261daede944af81 \u2014 Last modification: 2026-07-17 Verify Processor: Intel i5 or AMD Ryzen 5 for basic 7B models RAM: required: 16 GB absolute minimum for small models Storage:100 GB free space for HuggingFace cache folder Graphics: 12 GB VRAM minimum required for basic quantization Introducing the Qwen3-VL-235B-A22B-Instruct Model The Qwen3-VL-235B-A22B-Instruct model is a groundbreaking multimodal understanding system that harnesses the power of massive parameters and advanced architecture to deliver state-of-the-art vision-language tasks. 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