Versus Engine
GMKtec EVO-X2 (128GB) vs Ryzen 5 + RTX 4060 Ti Build
Specs, price and the one trade-off that actually decides it — GMKtec EVO-X2 (128GB) against Ryzen 5 + RTX 4060 Ti Build, side by side.
Cheaper to start
Tie
Both start at a similar price.
Rated higher
GMKtec EVO-X2 (128GB)
8.0 vs 7.5 on our design scale.
Standout
GMKtec EVO-X2 (128GB)
128GB massive memory pool
GMKtec EVO-X2 (128GB)
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
- chipAMD Ryzen AI Max+ 395
- tierenthusiast
- example_modelsLlama 3.3 70B, Mistral Small 3.1
- tokens_per_second15-25
- unified_memory_gb128
- cpu—
- gpu—
- ram_gb—
- vram_gb—
Ryzen 5 + RTX 4060 Ti Build
Entry-tier DIY build for local AI: an AMD Ryzen 5 CPU paired with an NVIDIA GeForce RTX 4060 Ti (16 GB VRAM) and 64 GB of system RAM. Runs small models such as Phi-4-mini (3.8B) and Gemma 3 4B at roughly 90 tokens/s — the lowest-cost path in this index to useful local inference.
Where it wins, where it doesn't
Pros
- 16GB VRAM at entry-level pricing
- Accessible upgrade path
- Low power draw (160W GPU limit)
Cons
- Limited to 8B/14B models for fast inference
- Memory bandwidth restricts larger batch sizes
Specifications
- chip—
- tierentry
- example_modelsPhi-4-mini (3.8B), Gemma 3 4B
- tokens_per_second~90
- unified_memory_gb—
- cpuAMD Ryzen 5
- gpuNVIDIA GeForce RTX 4060 Ti
- ram_gb64
- vram_gb16