Every Way to Get 512GB of VRAM for Local AI (at Full Context)

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

Source video:
- Title: Every Way to Get 512GB of VRAM for Local AI (at Full Context)
- URL: https://www.youtube.com/watch?v=6tihP50zlcE
- Topic: Creative Automation
- My current learning frame: Choose a target model and workload, calculate its full memory and prompt-time budget, then compare two hardware paths with the API using privacy, concurrency, energy, and payback as decision criteria.
- Why this matters: New playlist item from Kai; queued for transcript-backed review, topic mapping, and a practical learning artifact.

Transcript anchors from this exact video:
- 0:59 / Evidence 1: "local inference at scale, I completely ignored the context side of the equation. I bought for the model file size and thought that once the weights loaded, I was done, which was a rookie mistake that cost me..."
- 3:28 / Evidence 2: "want, right? So, a million tokens of context should be fine since the model fits. And that's exactly what I thought. But it doesn't work like that. Every single token the model reads leaves a small set of..."
- 6:29 / Evidence 3: "the back of a book where we look up one entry at a time instead of carrying the whole book around. So, once we strip out engram, Dwarf Star's 4-bit build has 294 GB of main weights and..."
- 10:19 / Evidence 4: "million tokens total, which is enough for eight full-length sessions at the same time. Their benchmark numbers come out at 87.7 tokens per second for a single user, roughly 5,000 tokens per second reading prompts, and 342.7 tokens..."
- 14:49 / Evidence 5: "that it reads prompts slowly because CPU memory bandwidth is nowhere near GPU or Apple silicon bandwidth. One owner running an older DeepSeek-V4 flash model on an RTX 5090 with desktop DDR5 waited 40 minutes and 30 seconds..."
- 16:56 / Evidence 6: "strong machine for the price. Now, I want to be straight with you about something. The title says 512 gigs and for DeepSeek, that's mostly true. But for the other models, 512 starts to not be enough. GLM..."
- 21:25 / Evidence 7: "comfortably both. So, consider the 320 billion parameter GLM 5.3 flash instead, which is smaller and faster and fits better. Or accept a shorter context window if you need the full model. If it's μSpark open, use the..."

Video-aware target:
- Prompt lane: Local model/runtime
- Mechanism to extract: Identify why the local setup works or fails for this specific agent task, not whether local models are generally good.
- Artifact to produce: A local model/runtime fit sheet with hardware constraints, model choice, endpoint setup, task benchmark, and fallback rule.
- Artifact must include: hardware; runtime; model/quantization; endpoint; agent integration; benchmark/fallback

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, transcript support, what the demo proves, confidence, and what still needs verification.
3. Extract the actual teachable mechanism from the video: Identify why the local setup works or fails for this specific agent task, not whether local models are generally good. Do not invent claims that are not supported by the title, lesson frame, or transcript anchors.
4. Build a reusable learning artifact: A local model/runtime fit sheet with hardware constraints, model choice, endpoint setup, task benchmark, and fallback rule.
5. Include:
   - a plain-English definition of the core idea
   - a diagram or structured model using this sequence: Task -> Hardware -> Model/quantization -> Runtime endpoint -> Agent tool loop -> Benchmark task -> Fallback
   - answers to these source questions: What machine/runtime is shown? | What task exposes the model limit? | What setup change improves the loop?
   - 3 concrete examples that apply the video idea to real agentic work, such as Ollama or LM Studio coding endpoint; MLX Apple Silicon runner; DGX-backed Hermes session
   - 2 failure modes the video helps prevent, chosen from the transcript evidence and these likely risks: using a local model like ChatGPT; ignoring latency/context limits; no benchmark task
   - a checklist for the next real workflow, focused on: task fit, runtime setup, latency/context, tool loop, fallback
   - one practical exercise with a clear done signal: Choose one real coding task and specify the pass/fail benchmark for a local model.
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 "Every Way to Get 512GB of VRAM for Local AI (at Full Context)", not a generic Creative Automation essay.
- Tie each harness element to a transcript anchor that names a tool, state boundary, permission, model behavior, or verification step.
- Prefer operational examples, failure modes, and reusable artifacts over broad definitions.
- Call out uncertainty instead of smoothing over weak evidence.
- Avoid these generic drifts: local-model ideology; hardware specs without workflow fit; benchmarks unrelated to the actual task.
- If evidence is weak or missing, stop and say what transcript segment or timestamp needs review instead of guessing.
- Finish with a concise artifact I could paste into my learning app.