AMD Launches ‘Powerful’ Consumer-Grade Processor for LLMs
American chip maker AMD announced a new processor on Tuesday for consumer laptops to run LLMs efficiently. AMD Ryzen AI Max+ 395 is the most powerful x86 APU currently on the market and delivers a significant performance boost over the competition, the company claimed. The processor is powered by 16 “Zen 5” CPU cores, 50 peak AI TOPS XDNA 2 NPU and an integrated GPU powered by 40 AMD RDNA 3.5 CUs. It has system memory options ranging from 32 GB to 128 GB of unified memory. 96 GB of it can be converted to VRAM through AMD Variable Graphics Memory. AMD stated that the processor excels in consumer AI workloads like the llama.cpp-powered application: LM Studio, and is a significant upgrade for thin and light devices. To demonstrate the performance capabilities, AMD used the ASUS ROG Flow Z13 laptop with 64 GB of unified memory. AMD claims the processor is up to 4x faster than the competition in time-to-first token benchmarks when using smaller models like Llama 3.2 3b Instruct. When considering 7/8 billion parameter models like DeepSeek R1 Distill Qwen 7b and 14 billion models, the ASUS laptop is up to 9.1x and 12.2x faster, respectively. The company shared benchmarks with vision models and claimed that the Ryzen AI Max+ 395 processor is up to 7x faster in IBM Granite Vision 3.2 3b, up to 4.6x faster in Google Gemma 3 4b, and up to 6x faster in Google Gemma 3 12b. One can see the processor running the Google Gemma 3 27B Vision model in the official demo: In addition to the new processor, AMD highlighted EPYC CPUs benchmarks that outperformed Nvidia’s Grace Superchip for enterprise AI use cases. The company also announced on Monday that AMD Versal AI Edge XQRVE2302 SoC is their second flight-qualified device, which is available to order.
AMD is trying to make it easy for consumers to run LLMs on their laptops.
AMD is trying to make it easy for consumers to run LLMs on their laptops.
["AMD"]
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["AI", "Gemma 3", "DeepSeek R1", "IBM Granite", "Aim", "R", "Go", "llm_models:Llama", "programming_languages:R", "programming_languages:Go"]
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