[vLLM Recipes 개편 - 모델+하드웨어 조합별 설정을 딸각 한방으로

vLLM Recipes가 대폭 개편되어 모델과 하드웨어 조합별 최적화 설정을 인터랙티브하게 제공합니다. 주요 변경 사항으로는 HuggingFace 미러 URL 통합, 인터랙티브 커맨드 빌더, 플러그형 하드웨어 지원(AMD/NVIDIA 원클릭 전환), JSON API 제공, 에이전트 기반 레시피 기여 기능 등이 포함됩니다. 또한 vLLM Compose와 관련 프로젝트(예: GGML, llama.cpp)와의 연동성도 강조됩니다.

https://news.hada.io/topic?id=28808

#vllm #llmdeployment #hardwareoptimization #aiinfrastructure

vLLM Recipes 개편 - 모델+하드웨어 조합별 설정을 딸각 한방으로 | GeekNews

recipes.vllm.ai가 대폭 개편되었습니다. vLLM 으로 "모델 X를 하드웨어 Y에서 어떻게 돌리지?"에 대한 답을 인터랙티브하게 얻을 수 있습니다.주요 변경사항HuggingFace 미러 URL — huggingface.co를 recipes.vllm.ai로 바꾸면 해당 모델 레시피로 바로 이동 (예: recipes.vllm.ai/Qwen/Qwen3

GeekNews

Companies are moving AI models to their own servers, a big change from using cloud services. This means they can keep their data more private and change the AI to fit their exact needs.

#AIinHouse, #DataPrivacy, #LLMDeployment, #CustomAI, #TechTrends
https://newsletter.tf/companies-move-ai-models-to-own-servers-privacy/

Companies move AI models to own servers for privacy

Many companies are now putting their AI models on their own computers instead of using cloud services. This is because they want more privacy and control over their data. It also allows them to change the AI for their specific needs. This change means businesses need powerful computers, like those with Nvidia A100 GPUs, to run these models effectively. Tools like Ollama and LM Studio help manage these in-house AI systems. The main reasons for this shift are better data security, customisation, and cost management. This trend is important for businesses handling sensitive information.

NewsletterTF

[AI 리서치의 미래: 레시피에서 밀키트로

AI 논문 폭증으로 인한 'Noise Tax' 증가와 논문-to-프로덕션 실패 모드 4가지가 강조되며, 2026년에는 패키징된 AI 솔루션(밀키트)이 DIY 구현을 대체할 것으로 전망됩니다. NVIDIA NIM, SLM, Ollama 등 표준화된 패키징 솔루션이 주목받고 있습니다.

https://news.hada.io/topic?id=25979

#airesearch #packaging #llmdeployment #nvidianim #slm

AI 리서치의 미래: 레시피에서 밀키트로

<h3>핵심 요약 (TL;DR)</h3> <ul> <li> <p><strong>AI 논문 폭증 = 진보 + 동시에 ‘Noise Tax’</strong></p> <ul> <li>2013 → 2023 연간 AI 논문: ...

GeekNews

🚨 Still deploying your LLMs on GPUs? You’re wasting time and money.
Groq’s LPU runs at ⚡500 tokens/sec⚡ with 1ms latency. That’s not hype—it’s production-ready speed.
Discover 6 real-world apps that prove Groq is rewriting the rules of AI deployment.👇

👉 https://medium.com/@rogt.x1997/train-llms-in-minutes-not-hours-6-use-cases-that-prove-groq-is-the-fastest-way-to-serve-llms-c8fc98e45dfb
#LLMDeployment #Groq #AIAcceleration
https://medium.com/@rogt.x1997/train-llms-in-minutes-not-hours-6-use-cases-that-prove-groq-is-the-fastest-way-to-serve-llms-c8fc98e45dfb

Train LLMs in Minutes, Not Hours: 6 Use Cases That Prove Groq Is the Fastest Way to Serve LLMs

There’s a moment — right after you hit run on your training script — when every AI developer quietly prays to the GPU gods. You’ve waited hours, sometimes days, for a response. And when it finally…

Medium