Laptops and Phones Can Now Run Multimodal AI — Here's Why
Published Jan 4, 2026
Worried about latency, privacy, or un‐auditable AI in your products? In the last two weeks vendors shifted multimodal and compiler work from “cloud‐only” to truly on‐device: Apple’s MLX added optimized kernels and commits (2024‐12‐28 to 2025‐01‐03) and independent llama.cpp benchmarks (2024‐12‐30) show a 7B model at ~20–30 tokens/s on M1/M2 at 4‐bit; Qualcomm’s Snapdragon 8 Gen 4 cites up to 45 TOPS (2024‐12‐17) and MediaTek’s Dimensity 9400 >60 TOPS (2024‐11‐18). At the same time GitHub (docs 2024‐12‐19; blog 2025‐01‐02) and JetBrains (2024‐12‐17, 2025‐01‐02) push plan–execute–verify agents with audit trails, while LangSmith (2024‐12‐22) and Arize Phoenix (commits through 2024‐12‐27) make LLM traces and evals first‐class. Practical takeaway: target hybrid architectures—local summarization/intent on-device, cloud for heavy retrieval—and bake in tests, traces, and governance now.