AMD Ryzen AI 300 vs Intel Lunar Lake: The Local LLM Winner

Memory capacity dictates model size

AMD Ryzen AI 300 systems handle 32B parameter models that Intel Lunar Lake systems struggle to run. A 32B parameter model quantized to four bits requires roughly 20GB of memory. Intel Lunar Lake platforms use on-package LPDDR5X memory capped at 32GB. This limit creates a bottleneck when the operating system and context windows also require memory. AMD Ryzen AI 300 systems use a unified memory architecture. Users can configure 16GB to 64GB of LPDDR5X memory. This allows the NPU and the integrated GPU to access the same pool of system RAM. A 32B coding model like Qwen 2.5 Coder requires 20GB of memory at four-bit quantization. The Ryzen AI 300 fits this model with room for context. The Intel Lunar Lake cannot match this flexibility.

AMD’s Ryzen AI 400 series, based on the Strix Point platform, provides a 10% to 12% performance bump over the prior generation. The Ryzen AI 9 HX 370 has 12 cores and 24 threads using a dual-cluster layout of four Zen 5 cores and eight Zen 5c cores. This chip uses TSMC 4nm manufacturing and 24 MB of L3 cache. AMD’s Zen 5 microarchitecture delivers a 16% increase in IPC over Zen 4. Intel Panther Lake uses the Intel 18A process. Panther Lake scales up to 16 cores with four Performance cores, eight Efficiency cores, and four Low-Power Efficiency cores. This configuration provides 18 MB of L3 cache. Intel’s Lion Cove Performance cores provide a 14% improvement in IPC compared to Redwood Cove. Panther Lake’s 18A process delivers a 30% transistor density gain. AMD Ryzen AI 300 series chips use LPDDR5X-8533 memory, while Intel’s X9 388H reaches an LPDDR5X-9600 ceiling.

NPU vs iGPU inference reality

The Ryzen AI 9 HX 375 delivers 50.7 tokens per second on Meta Llama 3.2 1b, which surpasses the 39.9 tokens per second that the Intel Core Ultra 7 258V produces for the same workload. AMD’s chip represents a 27% lead in these LM Studio tests. The Ryzen AI 9 HX 375 also shows 3.5x lower latency than Intel on Meta Llama 3.2 1b.

The NPU is mostly idle.

The XDNA 2 NPU on Ryzen AI 300 provides 50 TOPS. Most popular runtimes like Llama.cpp and Ollama route LLM workloads to the iGPU through Vulkan or ROCm, which leaves the NPU mostly idle. The Radeon 890M handles the heavy lifting. Intel’s NPU 4 delivers 48 TOPS. The Intel Arc B390 GPU provides 122 GPU TOPS for AI workloads. Intel Panther Lake provides 180 total platform TOPS, which includes 50 TOPS from the NPU and 130 TOPS from the Xe3 graphics architecture. The Intel Arc B390 has 12 Xe3 graphics cores. Qualcomm’s Snapdragon X Elite delivers 75 to 85 TOPS of dedicated AI performance. AMD’s Ryzen AI 9 HX 475 reaches a 60 TOPS rating for its dedicated neural processing unit.

Hardware selection rules

AMD is the winner.

If you want to run 32B coding models or experiment with mixture of experts architectures, buy the Ryzen AI 300 mini PC with 32GB of unified memory. You will accept slower tokens per second on small models in exchange for being able to run bigger models at all. The Ryzen AI 300 can load a 32B model that an RTX 3060 cannot fit. An RTX 3060 with 12GB of VRAM costs roughly 600 dollars. A Ryzen AI 300 mini PC with 32GB of unified memory also costs between 600 and 700 dollars. You pay roughly 20 dollars per gigabyte of usable model memory on the Ryzen AI 300. You pay 50 dollars per gigabyte of model capacity on the 3060.

Do you need the speed of an RTX 3060 for small 7B models? For 7B models like Mistral 7B, the RTX 3060 pushes 30 to 50 tokens per second. The Ryzen AI 300 iGPU manages 10 to 15 tokens per second. You should prioritize unified memory architectures with 32GB+ RAM over peak TOPS ratings. AMD provides the flexibility.