Caminhos para reduzir custos com IA

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[{"selector":"#anim-52873395-e67a-4e7c-aa4d-30901d416a8a","keyframes":{"opacity":[0,1]},"delay":0,"duration":2000,"easing":"cubic-bezier(0.2, 0.6, 0.0, 1)","fill":"both"}] [{"selector":"#anim-3c8336ea-d943-438a-bda4-4e9e55cec6b7","keyframes":{"transform":["translate3d(116.39871%, 0px, 0)","translate3d(0px, 0px, 0)"]},"delay":0,"duration":2000,"easing":"cubic-bezier(0.2, 0.6, 0.0, 1)","fill":"both"}] O foco atual da IA migra de apenas ampliar modelos para arquiteturas que cortem custos de inferência sem perder desempenho Foto: Angnaont / Shutterstock / Modificada com IA

[{"selector":"#anim-8a7dcedd-2962-419a-bfc5-3354566e7cc8","keyframes":{"opacity":[0,1]},"delay":0,"duration":2000,"easing":"cubic-bezier(0.2, 0.6, 0.0, 1)","fill":"both"}] [{"selector":"#anim-ed0bec0a-526c-4103-b5bb-4b6b8fff6b03","keyframes":{"transform":["translate3d(-115.55555%, 0px, 0)","translate3d(0px, 0px, 0)"]},"delay":0,"duration":2000,"easing":"cubic-bezier(0.2, 0.6, 0.0, 1)","fill":"both"}] O Google DeepMind Gemma 4 usa Mixture of Experts para ativar apenas subpartes conforme a tarefa, reduzindo carga computacional e memória sem sacrificar performance Foto: krungchingpixs / Shutterstock / Modificada com IA

[{"selector":"#anim-07714633-901f-4005-a19a-f6b6278379eb","keyframes":{"opacity":[0,1]},"delay":0,"duration":2000,"easing":"cubic-bezier(0.2, 0.6, 0.0, 1)","fill":"both"}] [{"selector":"#anim-663676b5-8cc0-4b25-926f-ecb95aa19e90","keyframes":{"transform":["translate3d(-115.00001%, 0px, 0)","translate3d(0px, 0px, 0)"]},"delay":0,"duration":2000,"easing":"cubic-bezier(0.2, 0.6, 0.0, 1)","fill":"both"}] Já o DeepSeek (DSpark) aplica speculative decoding e eleva a velocidade de geração (60–85%) sem hardware novo Foto: Melinda Nagy / Shutterstock / Modificada com IA

[{"selector":"#anim-f31272fd-e234-4830-b531-a16476632c5c","keyframes":{"opacity":[0,1]},"delay":0,"duration":2000,"easing":"cubic-bezier(0.2, 0.6, 0.0, 1)","fill":"both"}] [{"selector":"#anim-ef7143c3-75a6-48b0-b7ae-8fb6fbb0b5d9","keyframes":{"transform":["translate3d(112.73886%, 0px, 0)","translate3d(0px, 0px, 0)"]},"delay":0,"duration":2000,"easing":"cubic-bezier(0.2, 0.6, 0.0, 1)","fill":"both"}] A Nvidia propõe difusão para texto (Nemotron-Labs-Diffusion) e gera tokens em paralelo, refinando sequências inteiras

[{"selector":"#anim-e0268ef4-5aaf-460c-a2d3-a8c57de60580","keyframes":{"opacity":[0,1]},"delay":0,"duration":2000,"easing":"cubic-bezier(0.2, 0.6, 0.0, 1)","fill":"both"}] [{"selector":"#anim-abdd52bc-292e-42cd-8e52-eba955722815","keyframes":{"transform":["translate3d(117.76315%, 0px, 0)","translate3d(0px, 0px, 0)"]},"delay":0,"duration":2000,"easing":"cubic-bezier(0.2, 0.6, 0.0, 1)","fill":"both"}] A redução de recursos por token democratiza IA, diminui custos operacionais e amplia adoção sem depender apenas de modelos gigantes Imagem gerada digitalmente

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