Mac Unified Memory & DGX Spark Redefine 70B+ Local Inference
High-bandwidth unified memory lets laptops address 64–128 GB for inference, making 70B-class models runnable on a single portable machine.
What Actually Changed
High-bandwidth unified memory lets laptops address 64–128 GB for inference, making 70B-class models runnable on a single portable machine.
Hardware Gravity Check
Directly impacts how builders allocate local GPU VRAM versus hosted API token budgets across production stacks.
How to Play It
Verify license terms, test quantised Q4_K_M builds on your own hardware, and avoid paying wrapper markups for commodity inference.
Unified memory removes the hard VRAM ceiling that has defined local inference for years.
The trade-off is bandwidth. Unified memory is typically slower than dedicated GPU memory.
01 Architectural & Hardware Breakdown
When evaluating this development through the lens of hardware gravity and real total cost of ownership, the dividing line is clear: teams that understand memory bandwidth, quantisation tolerances (Q4_K_M vs FP8/FP16), and license boundaries capture 80% of frontier capability at a fraction of hosted API spend.
For batch work and long-context analysis, that trade is often acceptable. For interactive chat, it is noticeable.
“High-bandwidth unified memory lets laptops address 64–128 GB for inference, making 70B-class models runnable on a single portable machine.”