Martin | GorroAI

@DMBGorgeCrew Dave always giving 💯 ❤️

Summer tour heating up with ‘Halloween’ in Tampa! 🎥 twosteph41(ig) 5.26.2026 #dmb #davematthewsband #davematthews #dmbgorgecrew #dmbgc

M I C x ‘Bl L gciuimiiconii I hhm b kn I’m kh VC hc UI u F C l v I kill o j oh

Adopting Claude speak in my regular life, episode 1: Partner: Did you do the dishes tonight? Me: Yes they're done. Partner: Why are they still dirty? Me: You're right to push back. I didn't actually do them.

@LordCharizard33 I miss the abercrombie when the vibe was all nice red variarions. Now its all bland and worse quality.

Do people still wear American Eagle and Abercrombie?

@ClassicAdvertz youtu.be/0JEzYONrWPM?si… sure dont make bands like they used to. This rocks!

No phones, no filters, just 200,000 ravers and The Prodigy in their prime, 1997!

@JohnnyCrambo Vmax has better hands

Which Gengar is the TRUE KING?

@BitcoinatBlock Its a scam to sign up on cash app

The bitcoin faucet is back. 04.06.26 btc.day

Ollama 0.19 just dropped with MLX backend hitting 112 tok/s on Qwen3.5-35B on M5 Max. Running autoresearch on @anemll’s flash-mlx I hit 55.7 tok/s on the same model via SSD streaming. Different problems: they need it in RAM, I run models larger than RAM. ollama.com/blog/mlx

Full day of repairing my github since everybody has getting 404 Error. Had to make a new github account LINK to full GITHUT REPO: Paper + code: github.com/gorroai/flash-…

@anemll @danveloper CORRECTED github: github.com/gorroai/flash-…

@anemll @danveloper Thanks for the foundation — your Q3-GGUF expert support was essential to getting here. Full writeup with 36 experiments on r/LocalLLaMA: reddit.com/r/LocalLLaMA/c… Paper + release: github.com/iluvclubs/flas…

Just hit 20.34 tok/s on Qwen3.5-397B on a MacBook Pro M5 Max. 4.67x over the baseline by @danveloper. 36 experiments, 58% discard rate. Here’s what worked and what didn’t: github.com/iluvclubs/flas… cc @anemll

CORRECTED github link: github.com/gorroai/flash-…

Here is link to the study drive.google.com/file/d/1xPu6bX… github.com/iluvclubs/flas…

@heynavtoor We just pushed it to 20.34 tok/s on M5 Max 128GB — 4.67x over that baseline. 36 experiments, temporal expert prediction, fused Metal scheduling. Full writeup: reddit.com/r/LocalLLaMA/c…

🚨 397 billion parameters. On a MacBook. No cloud. No GPU cluster. No data center. A laptop. Someone ran one of the largest AI models on Earth on a machine you can buy at the Apple Store. It's called flash-moe. A pure C and Metal inference engine that runs Qwen3.5-397B on a MacBook Pro with 48GB RAM. At 4.4 tokens per second. With tool calling. No Python. No PyTorch. No frameworks. Just raw C and hand-tuned Metal shaders. Here's why this should not be possible: → The model is 209GB. The laptop has 48GB of RAM. → It streams the entire model from the SSD in real time → Only loads the 4 experts needed per token out of 512 → Uses just 5.5GB of actual memory during inference → Production-quality output with full tool calling → 58 experiments. Hand-optimized Metal compute kernels. → The entire engine is ~7,000 lines of C and ~1,200 lines of Metal shaders Here's the wildest part: One person built this. A VP of AI at CVS Health. Not Google. Not OpenAI. A healthcare company executive. Side project. Used Claude Code as his coding partner. Built the entire engine in 24 hours. Running a 397B model on cloud GPUs costs hundreds of dollars per hour. Companies spend millions per year on inference infrastructure for models this size. This runs on a $3,499 laptop. Offline. Private. No API key. No monthly bill. Forever. Trending on GitHub. 332 points on Hacker News. 100% Open Source.

@aaawwwd_w @danveloper @anemll It works now. Thank you!

@DrPhoto @danveloper @anemll

@tom_doerr @tom_doerr @danveloper Running the same model at 20.34 tok/s on M5 Max 128GB — 4.67x faster. Temporal prediction + Metal optimizations. Paper pending ArXiv.

Runs 397B model on laptop github.com/danveloper/fla…

🚀 Just hit 20.34 tok/s on Qwen3.5-397B running locally on M5 Max—4.67× faster than the prior benchmark by @danveloper! Paper incoming, pending ArXiv endorsement.

@LottoLabs Running Qwen3.5-397B locally on M5 Max at 19 tok/s — way bigger than 27B, no GPU needed, no API bill. For privacy-sensitive work where you can’t send data to any cloud this is the only option. Full benchmark: reddit.com/r/LocalLLaMA/s…

Qwen 27b on the 3090 saving me a bag. This is cost savings for 7 days of usage, w/ Hermes agent. Assuming 80% cache hit (unlikely) and no cache timeout. This is conservative. 27b is between sonnet and 5.4 mini This is just my tokens in/out w/ api costs, assuming no rate limits. Obviously cheaper w/ coding plans $200/m but would be hitting limits likely.

@TheGeorgePu We’re running Qwen3.5-397B locally on a MacBook Pro M5 Max at 19+ tok/s. No cloud. 4.4x faster than the first published benchmark. Full data: reddit.com/r/LocalLLaMA/s…

Almost signed up for ElevenLabs to narrate my blog. $330/month. Then I tried running an open-source model on my own laptop. Qwen 3.5 14B. Sounds fine. 200 posts a month. Costs me electricity. I almost paid $4,000 a year to rent a model I can run myself. Most AI subscriptions right now are just a nice UI on top of something free.