1 link tagged with all of: privacy + quantization + hardware comparison + open source + local llm
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This article walks through why and how to run large language models locally, covering privacy, cost, offline access, and control. It breaks down hardware needs, quantization, PC versus Mac setups, and starter software to get models up and running.
- Local open-source models (GLM-5.1, Kimi K2.6) now match or beat top cloud models like Claude Opus 4.6 on coding benchmarks, handling 70-80% of typical daily AI tasks.
- 4-bit quantization (Q4_K_M) shrinks memory needs to ~0.6-0.7 GB per billion parameters with no noticeable quality loss.
- RTX 4090 PCs outperform M3 Max Macs on speed (100-140 vs 40-60 tokens/sec) for sub-24GB models, but Mac Studios with unified memory (up to 512GB) can run massive 671B-parameter models entirely in RAM.
- A 3090 PC build costs $1,500-2,000 and draws 450W with heavy noise, versus a quieter Mac Studio pulling only ~60W while supporting far larger models.
local llm
quantization
hardware comparison
privacy
open source