Plugable Thunderbolt 4 Dock (TBT4-UD5) vs Samsung Galaxy Book5 Pro 360

Samsung Galaxy Book5 Pro 360 edges ahead with 16 GB vs 0 GB — enough headroom to run larger quantized models without offloading. Samsung Galaxy Book5 Pro 360's 0 GB/s bandwidth also generates tokens faster.

Neither is optimised for image generation, but Samsung Galaxy Book5 Pro 360's 0 GB/s bandwidth makes generation faster. Both run SDXL via Metal (macOS) or ROCm (Linux). Expect slower generation times than a discrete GPU.

Both draw around 999W at peak load.

Samsung Galaxy Book5 Pro 360 edges ahead overall — better memory, bandwidth, and user ratings for local AI workloads. The gap is real but not always worth the price difference; assess based on your primary use case.

Q1Which runs Ollama faster — Plugable Thunderbolt 4 Dock (TBT4-UD5) or Samsung Galaxy Book5 Pro 360?

Plugable Thunderbolt 4 Dock (TBT4-UD5) runs Ollama faster. Its 0 GB/s memory bandwidth vs 0 GB/s means faster token generation — roughly — more tokens/second on the same model. On Llama 3.1 8B, expect around 0 tok/s vs 0 tok/s.

Q2Can either mini PC run Llama 3 70B?

Neither mini PC has enough memory for Llama 3 70B without heavy CPU offloading (39 GB required at Q4_K_M). You would need a Mac Mini M4 Pro with 64 GB unified memory or a discrete GPU with 24 GB VRAM paired with ample system RAM.

Q3Which is better value for local AI in 2026?

Plugable Thunderbolt 4 Dock (TBT4-UD5) offers better performance-per-dollar for AI workloads due to its 0 GB/s bandwidth advantage. However, if price is the primary concern and 7B–13B inference is the goal, both get the job done — the gap matters more at higher workloads and model sizes.

Q4Which has better software support for local AI?

Both run Ollama well. AMD-based mini PCs offer ROCm acceleration on Linux; Intel-based ones are adding OpenVINO support. macOS Apple Silicon has the most polished Ollama experience.