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🔥 Score 48.2
general • Confidence 40%

QuantEdge: On-Device LLM Optimizer

Large language models that run on consumer hardware are slowed by heavy memory usage and latency in initial prompt processing. QuantEdge automatically quantizes and hardware‑tailors models so developers can deploy 80‑B‑parameter LLMs on Macs and 35‑B‑parameter LLMs on iPhones with only a few gigabytes of RAM and near‑instant response.

Quantitative Score Breakdown

complaint frequency
3
growth rate
10
competition density
10.5
monetization potential
11.62
technical feasibility
7.5
search interest
5.6

Evidence Signal (2)

Raw Posts
hn • r/hackernews

Comment on: Show HN: Run an 80B Qwen in 4.3 GB of RAM on a Mac, and a 35B on an iPhone

Problem is prefill on these, right? Initial prompt processing takes forever? I suppose you’re right. Cost is not a thing on its own. It’s a performance-cost frontier and one can do CPU inference in the worst case.
hn • r/hackernews

Comment on: Show HN: Run an 80B Qwen in 4.3 GB of RAM on a Mac, and a 35B on an iPhone

There are several aspects to this:1. Better hardware optimized for matrix operations and other ML/NN specific workflows (NVIDIA hardware, Apple M4/M5 chips, Intel AI Boost, other NPU/TPU chips, etc.).2. Bigger RAM/VRAM/unified for loading models in memory -- with 3090/4090 you can run 22-24B models in VRAM; with 5090 you can run 30-32B models; with Apple hardware with 128GB unified RAM you can run 70-128B models.3. Better quantization (smaller, better value representations/lower errors/loss, optimized CUDA/etc. kernels) will lead to larger models being able to be run on the lower VRAM hardware