# Someone just ran a 2.78-trillion-parameter model on a laptop. The memory wall is breaking > Source: > Published: 2026-07-31 20:17:38+00:00 # Someone just ran a 2.78-trillion-parameter model on a laptop. The memory wall is breaking ### A frontier model that needs 1.42 TB now runs on a MacBook with 64 GB of RAM. The full playbook on local AI: what runs today, the cloud-vs-own math, and the workloads to pull off the cloud now On July 30, an open-source project did something the textbooks said was impossible. It ran **Kimi K3**, the 2.78-trillion-parameter model that beats Claude Opus 4.8 on measured intelligence, on a single MacBook Pro with **64 GB of RAM**. The full model. Zero pruning, zero distillation. A checkpoint that occupies 1.42 TB, executing on a machine with less memory than the model needs by a factor of more than twenty. It runs at **0.3 tokens per second**, so nobody is doing serious work with K3 on a laptop tomorrow. That is the honest headline. But the thing it proves is the story: **available memory no longer sets a hard ceiling on the size of model you can run on hardware you own.** The wall between you and frontier intelligence, the one that forced everyone onto someone else’s cloud, just developed a crack. And here is what almost nobody covering the laptop-K3 demo will tell you: the boring version of this is *already usable today*. On the same Apple Silicon, capable mixture-of-experts models run at **30 to 130 tokens per second** right now, fast enough for capable agents, live coding, and private workflows, on machines you already have. Which raises the question every builder and every cost-conscious founder should be asking this week. What should you actually run on your own hardware, and what should stay in the cloud? Behind the paywall, the complete **Local AI Playbook**: ▫️ the six factors that decide where each workload belongs, scored, with the routing ruleThe cloud-vs-local decision matrix,▫️ what actually works locally today, at what speed, on what hardware, from proof-of-concept to production-readyThe runnable-models tier list,▫️ exactly which machine for which workload, and the specs that actually move tokens per secondThe hardware buyer’s guide,▫️ owned hardware versus token bills, the breakeven math, and the consolidation multiplier most people missThe TCO calculator,▫️ which of your workflows should never leave your machine, and the sales line it hands a founderThe privacy audit,▫️ Ollama, MLX, and WASTE, which to use when, with the commands and the quantization cheat sheetThe stack setup,▫️ the one engineering discipline that saves you weeks, lifted from WASTE’s own build logThe measurement rule,▫️ how to move your first workload local this week without breaking anythingThe migration sequence,▫️ so you are positioned the moment this gets fast, and know exactly when to revisitThe get-ready plan and the re-evaluate triggers, ### One subscription unlocks every system This is one build in a growing library. Premium opens all of them: ▫️ [The AI Tools and Models library](https://www.the-ai-corner.com/t/ai-tools-and-models?r=1krivi) ▫️ [The Prompting and Context Engineering library](https://www.the-ai-corner.com/t/prompting-and-context-engineering?r=1krivi) ▫️ [The Claude and Anthropic library](https://www.the-ai-corner.com/t/claude-and-anthropic?r=1krivi) ▫️ [The Business and Investing library](https://www.the-ai-corner.com/t/business-and-investing?r=1krivi) Plus 3 fresh systems every week. One workload moved off the cloud can cover the subscription for years. # 🖥️ The Local AI Playbook The decision matrix, the tier list, the hardware guide, the TCO math, the privacy audit, the stack setup, and the migration sequence, in one system. #### Get **The Local AI Playbook** below 👇 **Try premium free for 7 days. Or get 50% off this week only.** ## Keep reading with a 7-day free trial Subscribe to The AI Corner to keep reading this post and get 7 days of free access to the full post archives.