If you want to run 100B-scale LLMs locally on a desktop, three 128GB unified-memory machines keep coming up: AMD Ryzen AI Max+ 395, NVIDIA DGX Spark, and Apple Mac Studio M5 Max. They share one pool of 128GB memory, so they can fit 100B-class models — yet prices span ¥19k-45k and decode speed on the same model ranges from 13 to 27 tps. This post lays out positioning, specs, measured speed and prices for all three, sourced from official claims and third-party reviews (collected Sept 2026).

One-liner: AMD 395 = best value (all-round Windows box); DGX Spark = pure AI research tool (CUDA ecosystem); Mac Studio M5 Max = creative workstation (smoothest output).

Why these tiny boxes run big models: unified memory

The key is unified memory. On a normal PC, RAM belongs to the CPU and VRAM to the GPU; an LLM must fit entirely in VRAM or it won't run. With unified memory, CPU and GPU share one pool — all 128GB is usable by the GPU. On all three machines '128GB' means exactly that: memory is VRAM.

Specs at a glance (128GB)

Same Qwen3.5-27B benchmark: it's all bandwidth

Under one uniform benchmark — Qwen3.5-27B (IQ4 quantized) — decode speed tells the whole story. The M5 Max has 2.4× the bandwidth of the AMD 395 and 2.25× the DGX Spark, and decode speed opens up a full tier:

27 tps is close to reading speed and feels smooth; 13-15 tps produces text but stutters, which gets tiring for long outputs. DGX Spark fights back on prefill (understanding your prompt): prompt throughput reaches 1957 tok/s (AMD 395 ~956), roughly 3× the Mac — DGX thinks fast but talks with a stutter; the Mac talks smoothest.

Street prices (JD, Sept 2026)

DRAM price hikes pushed all three up between early and late 2026: AMD 128GB units went from ¥13-15k to ¥19-23k, NVIDIA raised DGX Spark 17.5% (+$700), and Apple dropped the big-RAM M3 Ultra options. Buying AI hardware now is pricier than in January — if you're not in a hurry, waiting pays.

The catch with each

• AMD 395: 256 GB/s bandwidth = slow decode (~15 tps on 27B); ROCm ecosystem lags CUDA; memory is soldered, and the 64GB version is the one to avoid.
• DGX Spark: Linux (DGX OS) only, no 3A gaming; 273 GB/s = slow decode (~13 tps on 27B); post-hike price near ¥40k hurts value; multi-node clustering is fiddly.
• Mac Studio M5 Max: expensive (~¥18k more than the AMD); no CUDA/vLLM, weak batch + finetuning (LoRA ~4× slower than DGX, no full 70B tuning); MLX ecosystem less mature than CUDA.

How to choose

• On a budget, want local LLMs plus office/gaming → AMD AI Max+ 395 (cheapest 128GB, all-round Windows, slower output is tolerable)
• Professional AI research, finetuning, batch jobs, need CUDA → NVIDIA DGX Spark (most compute, full ecosystem, expandable, Linux only)
• Creative pro, want smooth local inference, in the macOS world → Mac Studio M5 Max (highest bandwidth, smoothest output, all-round workstation, just pricey)
• Occasional tinkering, not heavy production → you don't need a ¥20-45k box; a discrete-GPU build or waiting for price drops makes more sense

One user → M5 Max; team / many agents → dual DGX Spark; tight budget but want an all-round Windows box → AMD 395.

Data notes: speed figures come from AMD official ROCm numbers, NVIDIA marketing and third-party reviews (Zhihu, SMZDM, ITHome, Notebookcheck, ZDNet, etc., collected Sept 2026); results vary by test env and are for reference only. Prices are JD.com street prices as of 2026-09-01 and fluctuate.