Large capacity cost-effectiveness · Officially released product · Updated 2026-09-06

This is a choice aimed at loading a 128GB model at a low body price.

Ryzen AI Max+ 395 128GB local LLM cost-effectiveness

It is attractive when you have large amounts of integrated memory at a low price and can handle runtime settings directly.

Core specifications

available memory: 116GB
Memory bandwidth: 256GB/s
local LLM sustained load: about 125W
Korean price range (KRW): 530–670 × KRW 10,000

a person who fits well

Loading large models
Select Windows/Linux
128GB cost-effectiveness

Check before purchasing

Even if there is ample memory, the actual speed difference is large depending on the runtime and GPU acceleration support, so you should check compatibility for each model first.

What tasks is it suitable for?

task	determination	reason
Large LLMs	fit	We can review the larger Q4 model with 112GB of available memory.
Document/RAG	fit	It has a large capacity to hold both the model and long context.
Convenience of installation	conditional	You should check runtime support for each model yourself.

Configuration Selection Criteria

The first bottleneck encountered

GPU acceleration support by runtime and kernel completeness determine actual usability rather than hardware capacity.

Recommended purchase setup

Ryzen AI Max+ 395 · System memory 128GB · NVMe 2TB recommended

Criteria for spending more money

It is advantageous if you want to put up with the settings and install a large capacity model cheaply. If you want reliable tools out of the box, you're better off with a Mac or NVIDIA finished product.

4K context · Q4 model-specific expected performance

Model	required memory	Decode	First token	Prefill
Gemma 4 12B llama.cpp HIP · Q4_K_M	8GB	25.4 ~ 28.3 tok/s	21.9 ~ 40.6 s	101 ~ 187 tok/s
Qwen3.8-Flash-Next llama.cpp HIP · Q4_K_M	107.13GB	15.2 ~ 21.4 tok/s	27.2 ~ 64.1 s	64 ~ 151 tok/s

Single-user expected range and may vary depending on runtime, cooling, and quantization files.

Frequently Asked Questions

Which local LLM can be run on Ryzen AI Max+ 395 128GB?

Gemma 4 12B, Qwen3.8-Flash-Next, and others can be compared with 4K input and device-specific recommended formats. The required memory varies depending on the model and context length.

How much power does the local LLM of Ryzen AI Max+ 395 with 128GB consume?

Expected power consumption under sustained load is approximately 125W, with an actual range anticipated between 80–170W.

Are the displayed token speeds ground truth?

This is an estimated range calibrated to the calculator against official hardware specifications and verified benchmark ranges. Runtime, cooling, quantization may vary depending on file and context length.

Equipment to compare together

MLX completeness: Studio M4 Max 128GB — Designed for users who want to conduct broad comparisons from single, all-in-one products, covering models up to 70B parameters and high-precision models.
CUDA environment: DGX Spark 128GB — Requires large-scale unified memory and NVIDIA development environment, and is appropriate when model loading takes priority over single-GPU peak speed.
Single GPU speed: RTX 5090 32GB — Suitable when prioritizing single-GPU inference speed and 32GB VRAM, accepting higher power consumption and deployment cost.