5090 가격의 ⅓인데 로컬 AI 성능은? R9700 (VRAM 32GB) 가지고 윈도우에서 라데온 돌리기 (ROCm, Vulkan)

If the price of the RTX 5090 is too high, is there a better alternative for running local AI? I personally tested local AI in a Windows environment using the Radeon AI PRO R9700 with 32GB of VRAM. From the appearance to actual LLM performance — I will personally verify if local AI on Radeon is truly worthwhile. ───────────────────────────── 📌 Video Contents ──────────────────────────── 00:00 Intro — A 32GB VRAM graphics card for 1/3 the price of a 5090? 00:24 RTX 5090 Price Reality 01:20 Introduction to Radeon AI PRO R9700 02:03 Unboxing: ASUS Radeon AI PRO R9700 Packaging and Appearance 05:49 Checking GPU-Z Detailed Specs 06:37 What is ROCm? 07:20 Windows Environment Tool Radeon Run Comparison: Differences in OLLAMA & LM STUDIO Libraries 08:58 Checking OLLAMA Actual Run Library (ROCm, gfx1201) Logs 11:06 Starting OLLAMA Benchmark 12:59 Heat & Fan Noise Issues — The Reality of Blower Types 14:21 Adrenalin Fan Tuning Settings and Noise Test 15:37 The Dilemma of Fan Speed ​​Control — Fan Speed ​​vs. Cumulative Heat 16:58 Presenting Solutions and Setup Methods 18:26 Default Settings vs. Solution Application, OLLAMA Continuous Test and Peak Temperature Measurement 22:13 LM STUDIO Benchmark Settings (Vulkan, Context Max) and Token Count Measurement by Model 25:27 How to Run When Model 31B Loads in LM STUDIO CLI Environment 26:35 Comparison of Tool Compatibility and Performance Efficiency Based on Vulkan and ROCm Libraries 27:41 llama.cpp Run Test: LLAMA-CLI Command Options 29:12 Performance Measurement by Model in llama.cpp ROCm & Vulkan Library Environments 30:43 Compilation of Overall Results: ROCm vs Vulkan Benchmark Summary by OLLAMA, LM STUDIO, and llama.cpp Environments 32:42 Conclusion — Performance Compared to 5090? Value for Money? 33:37 Next Video Preview & Wrap-up 34:25 Key Points ───────────────────────────── 🧪 Test Environment ───────────────────────────── CPU　　 : 9950X3D2 RAM　　 : BIWIN DDR5 48GB 4800MHz (Used stock settings without EXPO applied) GPU　　 : ASUS Turbo AMD Radeon AI PRO R9700 (32GB GDDR6) OS　　　 : Windows 11 Inference Tools　 : Ollama / LM Studio / llama.cpp Libraries : ROCm / Vulkan Test Model GEMMA4 31B (Dense) GEMMA4 26B A4B (MoE) QWEN 3.6 27B (Dense) QWEN 3.6 35B A3B (MoE) ───────────────────────────── 🔗 Related Links ───────────────────────────── ▸ ASUS Turbo AMD AI PRO R9700 Product Page → https://www.asus.com/motherboards-com... ▸ Ollama Official Download → https://ollama.com ▸ LM Studio Official Download → https://lmstudio.ai ▸ llama.cpp GitHub → https://github.com/ggml-org/llama.cpp ────────────────────────────── 🔗 AI Video ───────────────────────────── ·    • 홈 로컬 AI (LLM) 구축을 위한 사전 준비 | 메인보드 잘 선택 하기   ·    • 비싼 그래픽카드로 게임만? 로컬 AI를 돌려보자 / gemma4, qwen3.6   ·    • 로컬 AI 전력소비, 생각한 것 보다 적은데? 왜 그럴까? 실측결과 및 결과분석   ·    • VRAM만 중요한줄 알았더니.. CPU가 OFFLOAD 방식 로컬 AI 성능...      • 이거하려고 NVIDIA 50 시리즈 모두 모았다... 로컬 AI성능 전수 테...      • 5090 가격의 ⅓인데 로컬 AI 성능은? R9700 (VRAM 32GB) ...   ───────────────────────────── 🔔 Subscribe to Soul Seeker Channel & Notifications Settings ───────────────────────────── We consistently upload local AI and PC hardware information. Subscribing and liking are a great help in content creation 🙏 #R9700 #Radeon #LocalAI #AMD #VRAM32GB #llama #Ollama #LMStudio #llamacpp #GEMMA4 #QWEN #ROCm #Vulkan #RDNA4 #CostEffectiveGPU #LocalLLM #SoulSeeker [Membership Benefits Guide] Before the video release process is complete, videos may be uploaded in advance exclusively for members earlier than the general release schedule. Membership members can watch these videos in advance. *Settings*    / @s0ul_seeker   [Comment Question Guide] Depending on the comment interface of your YouTube channel management program, you can view "posts without comments," but "replies" are not included. For important content, please write it as a "new comment" rather than a "reply" so that it can be checked quickly. For those who receive a reply, please give this a like..