DDR6 could be the unlock for on-device LLMs by 2028. If DDR6 hits 10,000+ MT/s and scales across dual- and quad-channel setups, memory bandwidth may finally stop bottlenecking larger local models [1].
DDR6 and Local LLMs In the LocalLLaMA discussions, rising RAM speed and smarter quantization could push 8B- to 27B-parameter models onto devices with chat-ready speeds. Proponents point to benchmarks like Gemma 3 27b, Deepseek V3, and GLM 4.5, and remind us that even modest GPUs—like Nvidia GTX 650—still matter for UI and display bottlenecks [1].
Sigma-Delta Quantization (SDQ-LLM) SDQ-LLM enables extremely low-bit LLMs by using upsampling and a Sigma-Delta Quantizer to encode high-precision parameters into 1-bit or roughly 1.58-bit representations, replacing multiplications with additions [2]. An adjustable Over-Sampling Ratio (OSR) lets you trade model size and accuracy, with MultiOSR distributing OSR across layers and within each layer according to weight variance [2]. Hadamard-based weight smoothing helps stabilize quantization, and tests on OPT and LLaMA families show the approach can stay robust under aggressive low-OSR settings [2].
Together, these bets sketch a path to on-device LLMs you can actually run by 2028. Keep an eye on real-world benchmarks, tooling, and the hardware-software handoff as DDR6 memory and SDQ-LLM push local inference from niche to normal.
References
Will DDR6 be the answer to LLM?
Discusses DDR6 memory for local LLMs, parameter sizes, MoE architectures, bandwidth, and cost vs cloud, predicting local use by 2028.
View sourceSDQ-LLM: Sigma-Delta Quantization for 1-bit LLMs of any size
Presents Sigma-Delta 1-bit quantization for LLMs; adjustable OSR, weight smoothing; compares OPT/LLaMA; seeks benchmarks and modest superiority over methods today
View source