MLX beats GGUF on a Mac, just
Gemma 4 31B: 12.6 tokens a second against 11.7 on our M4 Pro.
7 minGemma 4 31B: 12.6 tokens a second against 11.7 on our M4 Pro.
7 minQwen3.6 35B-A3B took in 20,000 tokens in 30 seconds on MLX and 75 on llama.cpp.
12 minGoogle Tag Manager and Microsoft Clarity load first. The source has no switch to turn them off.
7 minPointed at local hardware it works; 74 percent of the 29,424 tokens it sends before starting are tool definitions.
7 minBoth loaded in 26.78 GB of RAM, and generating together dropped each one to about 9 tokens a second.
7 minMeasured on a 48 GB Mac: 80 KB of RAM per token, and LM Studio reports 18.85 GB whether you load 8K or 64K.
8 minAn M4 Pro reads prompts at 65 tokens a second; repeat the prompt and the cache reports 13,763, which is 182 times too fast.
8 minNVIDIA's 22.46 GB Lightning GGUF holds 2.67 GB of BF16: the MTP head, which four publishers ship four ways.
8 minAn mmproj GGUF holds the whole vision encoder; four publishers ship it unquantised, and the one that compresses stops at 8.
7 minAn opt-out variable, a send function nothing uses, and the real trail: a user-agent header naming your Python and torch.
7 minEvery shard summed: the smallest complete build is 589.5 GB, the family runs to 769, and two builds disagree by 58.
7 minWhat the app sends, read from the wire.
8 minThe requirement is not in the source. Routing is by model format, and the only Darwin memory constant is 512 MiB.
7 minEight builds of one model audited: a file called UD-Q4_K_XL holds no Q4_K, and none had the silent experts people warn about.
9 minA covert tracker in Claude Code, telemetry in others, and how to catch both.
8 minWe watched every connection over two passes: none left the desk, and the code graph cost zero tokens.
8 minWe captured what each sends. One has a working off switch; the other, the agent we use daily, has none we could find.
7 min