Local AI On Apple Silicon uses 7X Less RAM

You are helping me turn one specific YouTube video into real, durable learning.

Source video:
- Title: Local AI On Apple Silicon uses 7X Less RAM
- URL: https://www.youtube.com/watch?v=vHhephsP6vU
- Topic: Agent Architecture
- My current learning frame: Run Turbo Fieldfare on an Apple Silicon Mac, log tokens per second and resident memory during a long generation, and write a short explanation of which measured number would change if the model's routing were random rather than skewed.
- Why this matters: New playlist item from Better Stack; queued for transcript-backed review, topic mapping, and a practical learning artifact.

Transcript anchors from this exact video:
- 0:00 / Evidence 1: "This project uses Apple Silicon's architecture to run a 26 billion per model on just 2 GB of RAM and it's actually usable. I tried it out locally and was able to get 23 tokens per second. The..."
- 1:53 / Evidence 2: "billion actually do any work on any given token. 85% of the file is idle at any instance. You don't need 14.3 GB in RAM. You need whatever the current token happens to touch. Turbo Fieldfare takes advantage..."
- 4:28 / Evidence 3: "metal buffer is just memory that both the CPU and the GPU can see. So the CPU reads by straight off the SSD into a buffer the GPU is about to run against, meaning we can skip a..."
- 6:11 / Evidence 4: "wanted a different random eight, you'd miss the cash nearly every time and lose performance. So, the system design here is very specific to take advantage of both the architecture of Gemma 4 and Apple Silicon. You can..."

Your task:
1. Use the transcript anchors above as the primary source packet. If you add outside context, label it clearly as outside context and keep it secondary.
2. Create a source-check table with columns: timestamp, claim, what the demo proves, confidence, and what still needs verification.
3. Extract the actual teachable claims from the video. Do not invent claims that are not supported by the title, lesson frame, or transcript anchors.
4. Build a reusable learning artifact: A one-page agent harness map with tool boundaries and proof signals.
5. Include:
   - a plain-English definition of the core idea
   - a diagram or structured model using this sequence: Intent -> Model -> Harness -> Tools -> Verifier -> Artifact
   - 3 concrete examples that apply the video idea to real agentic work
   - 2 failure modes the video helps prevent
   - a checklist I can use the next time I run Codex or Claude
   - one practical exercise with a clear done signal
6. Add a "learning transfer" section: what changes in my workflow tomorrow if I actually learned this?
7. Add a "source check" section that cites which transcript anchor supports each major takeaway.

Quality bar:
- Make this specific to "Local AI On Apple Silicon uses 7X Less RAM", not a generic Agent Architecture essay.
- Prefer operational examples, failure modes, and reusable artifacts over broad definitions.
- Call out uncertainty instead of smoothing over weak evidence.
- If evidence is weak, say what transcript segment or timestamp needs review instead of guessing.
- Finish with a concise artifact I could paste into my learning app.