[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.]  Brainz - Your Fuck You Money Builder [@BRNZ_ai](/creator/twitter/BRNZ_ai) on x 2796 followers Created: 2025-07-22 09:47:35 UTC @nvidia ๐ฅ๐ฒ๐๐ฒ๐ฎ๐ฟ๐ฐ๐ต ๐ฑ๐ฟ๐ผ๐ฝ๐ ๐ป๐ฒ๐ ๐ฝ๐ฎ๐ฝ๐ฒ๐ฟ ๐ผ๐ป โ๐ฆ๐บ๐ฎ๐น๐น ๐๐ฎ๐ป๐ด๐๐ฎ๐ด๐ฒ ๐ ๐ผ๐ฑ๐ฒ๐น๐ ๐ฎ๐ฟ๐ฒ ๐๐ต๐ฒ ๐๐๐๐๐ฟ๐ฒ ๐ผ๐ณ ๐๐ด๐ฒ๐ป๐๐ถ๐ฐ ๐๐โ.ย โฌ๏ธ Everyone is chasing bigger models. But this paper argues the exact opposite: Most agent workloads donโt need 175B params โ they need precision, speed, and control. ๐ง๐ต๐ฒ ๐ธ๐ฒ๐ ๐๐ฎ๐ธ๐ฒ๐ฎ๐๐ฎ๐? Small Language Models (SLMs) are not only good enough โ theyโreย betterย for the majority of agentic use cases. ๐๐ฒ๐ฟ๐ฒ ๐ถ๐ ๐ฎ ๐พ๐๐ถ๐ฐ๐ธ ๐๐๐บ๐บ๐ฎ๐ฟ๐ ๐ผ๐ณ ๐๐ต๐ฒ ๐ธ๐ฒ๐ ๐ฝ๐ผ๐ถ๐ป๐๐: โฌ๏ธ X. SLMs can already match or beat 30โ70B LLMs on task-specific reasoning โ From Phi-3 to DeepSeek Distill, we now have 2โ9B models outperforming legacy LLMs with 10โ70ร faster inference. X. Most agents just run repetitive, scoped tasks โ Parsing. Routing. Tool calls. Summaries. You donโt need an all-knowing LLM โ you need a fast, fine-tuned SLM that gets the job done. X. LLMs are economically unsustainable at scale โ They dominate cloud costs and energy use. SLMs offer massive savings in latency, memory, and operational overhead. X. SLMs run on edge and consumer devices โ Tools like ChatRTX show real-time agents can live on laptops or embedded systems โ without phoning home to a GPU cluster. X. Heterogeneous agent stacks are the path forward โ Use LLMsย sparinglyย for general reasoning. Let SLMs handle XX% of workflows. More modular. More efficient. More robust. X. SLMs are easier to fine-tune and align โ Lower hallucination risk, tighter output control, and better format consistency. Perfect for tool-driven agent environments. More in the comments and the paper below to download โ but Iโll say this now: This paper might age like gold for every team trying to ship serious agents in production. --- Read the full paper here: XX engagements  **Related Topics** [money](/topic/money) [$nvda](/topic/$nvda) [stocks technology](/topic/stocks-technology) [Post Link](https://x.com/BRNZ_ai/status/1947594363313287182)
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Brainz - Your Fuck You Money Builder @BRNZ_ai on x 2796 followers
Created: 2025-07-22 09:47:35 UTC
@nvidia ๐ฅ๐ฒ๐๐ฒ๐ฎ๐ฟ๐ฐ๐ต ๐ฑ๐ฟ๐ผ๐ฝ๐ ๐ป๐ฒ๐ ๐ฝ๐ฎ๐ฝ๐ฒ๐ฟ ๐ผ๐ป โ๐ฆ๐บ๐ฎ๐น๐น ๐๐ฎ๐ป๐ด๐๐ฎ๐ด๐ฒ ๐ ๐ผ๐ฑ๐ฒ๐น๐ ๐ฎ๐ฟ๐ฒ ๐๐ต๐ฒ ๐๐๐๐๐ฟ๐ฒ ๐ผ๐ณ ๐๐ด๐ฒ๐ป๐๐ถ๐ฐ ๐๐โ.ย โฌ๏ธ
Everyone is chasing bigger models. But this paper argues the exact opposite: Most agent workloads donโt need 175B params โ they need precision, speed, and control.
๐ง๐ต๐ฒ ๐ธ๐ฒ๐ ๐๐ฎ๐ธ๐ฒ๐ฎ๐๐ฎ๐? Small Language Models (SLMs) are not only good enough โ theyโreย betterย for the majority of agentic use cases.
๐๐ฒ๐ฟ๐ฒ ๐ถ๐ ๐ฎ ๐พ๐๐ถ๐ฐ๐ธ ๐๐๐บ๐บ๐ฎ๐ฟ๐ ๐ผ๐ณ ๐๐ต๐ฒ ๐ธ๐ฒ๐ ๐ฝ๐ผ๐ถ๐ป๐๐: โฌ๏ธ
X. SLMs can already match or beat 30โ70B LLMs on task-specific reasoning โ From Phi-3 to DeepSeek Distill, we now have 2โ9B models outperforming legacy LLMs with 10โ70ร faster inference.
X. Most agents just run repetitive, scoped tasks โ Parsing. Routing. Tool calls. Summaries. You donโt need an all-knowing LLM โ you need a fast, fine-tuned SLM that gets the job done.
X. LLMs are economically unsustainable at scale โ They dominate cloud costs and energy use. SLMs offer massive savings in latency, memory, and operational overhead.
X. SLMs run on edge and consumer devices โ Tools like ChatRTX show real-time agents can live on laptops or embedded systems โ without phoning home to a GPU cluster.
X. Heterogeneous agent stacks are the path forward โ Use LLMsย sparinglyย for general reasoning. Let SLMs handle XX% of workflows. More modular. More efficient. More robust.
X. SLMs are easier to fine-tune and align โ Lower hallucination risk, tighter output control, and better format consistency. Perfect for tool-driven agent environments.
More in the comments and the paper below to download โ but Iโll say this now: This paper might age like gold for every team trying to ship serious agents in production.
Read the full paper here:
XX engagements
Related Topics money $nvda stocks technology