GMKtec EVO-X2 (128GB)
Verdict
Enthusiast-tier mini workstation: AMD Ryzen AI Max+ 395 with 128 GB of unified memory. Runs Llama 3.3 70B and Mistral Small 3.1 at roughly 15-25 tokens/s — 70B-class models without a discrete GPU, in a small-form-factor chassis.
Where it wins, where it doesn't
Pros
- 128GB massive memory pool
- Ultra-compact form factor
- Dual NVMe storage slots
Cons
- APU graphics bottleneck token generation speed
- Audible fan noise under sustained load
Specifications
| Chip | AMD Ryzen AI Max+ 395 |
|---|---|
| Runs | Llama 3.3 70B, Mistral Small 3.1 |
| Throughput | 15-25 tok/s |
| Unified memory | 128 GB |
Editorial note
Packing 128GB of RAM into a mini-PC chassis enables running massive models that simply will not fit in standard consumer VRAM. While inference speed via CPU/APU is slower than discrete GPUs, the sheer memory capacity unlocks use cases previously restricted to cloud servers.
In-Depth Review
The EVO-X2 packs an AMD Ryzen AI Max+ 395 with 128GB of unified memory into a mini-PC chassis — enough to run Llama 3.3 70B and Mistral Small 3.1 at roughly 15–25 tokens/second without a discrete GPU.
What the memory unlocks
Models in the 70B class simply do not fit in standard consumer VRAM. 128GB addressable by the APU makes them loadable on a machine you can hold in one hand, drawing a fraction of the power a multi-GPU rig would. Dual NVMe slots handle the model library.
The trade-off is speed, as always with this architecture
The APU graphics bottleneck token generation — 15–25 tok/s is usable for interactive chat and retrieval, not for high-throughput serving or batch work. And the fan is audible under sustained load.
Who should buy
High-capacity context-retrieval workloads, CPU/APU-based LLM inference, and anyone who needs 70B-class capability in a small, quiet-ish box. If your target models fit in 16–24GB of VRAM, a discrete GPU runs them several times faster for less money.
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