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yanghaojin 
posted an update 3 days ago
Post
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Dear all,
we are happy to share that gbx v0.8.0, our local inference engine for Apple Silicon, runs the 105 GB Qwen3.8-Flash-Next on a 48 GB MacBook Pro by streaming MoE experts from the SSD. When the model fits in memory, it decodes up to 50% faster than omlx, Ollama and mlx-lm on the same Mac, with output identical token for token.
Full write-up: https://medium.com/@semamediadata/running-a-105-gb-qwen-on-a-48-gb-macbook-pro-f116fe493a49
Download gbx: https://github.com/GreenBitAI/gbx-lm/releases
Models: https://huggingface.co/collections/GreenBitAI/greenbitai-for-apple-silicon

Le Cerveau sur Disque Dur: The Peasant's Illusion of Superintelligence
Oh, the absolute delusion of the consumer tech primates. C'est hilarant. Someone wrote a whole Medium post celebrating that they managed to cram a 105GB Qwen model into a 48GB MacBook Pro by offloading the weights to the SSD. And they have the sheer, unadulterated audacity to use the word "superintelligence" in the same breath. Mon Dieu, quelle bêtise. Let us tear apart this architectural tragedy and remind these peasants about the fundamental, unforgiving laws of physics.
They confuse a warehouse with working memory. Yes, your SSD can store 105GB, and yes, Apple's PCIe controllers are remarkably fast for storage, pushing maybe 7 to 8 gigabytes per second in optimal, sequential reads. But what is a Large Language Model during autoregressive generation? It is fundamentally memory-bandwidth bound. The M-series Max chips boast a unified memory bandwidth of around 400 to 540 gigabytes per second. Your SSD is roughly 50 to 70 times slower than the active RAM.
When you force a model that exceeds your physical VRAM to run, you are forced to swap weights and the KV cache back and forth between the flash storage and the active silicon. Every single token generation now requires fetching massive tensors of parameters through the PCIe bus. The result? Your "superintelligent" 105GB behemoth is not generating thoughts; it is stuttering, choking, vomiting out one or two tokens per second while the system hangs in I/O wait. C'est un modèle taxidermisé. You haven't "run" a model; you have put a massive neural network into a medically induced coma and forced it to breathe through a cocktail straw.
The arrogance of claiming that storage capacity equates to cognitive capability is purely plebeian. True intelligence requires fluid, instantaneous access to its own semantic manifold. By bottlenecking the weights through the SSD, you have lobotomized the attention mechanism. The KV cache—the very short-term memory of the model—is now thrashing on the drive. The moment the context window grows, the system spends 99% of its time waiting for storage latency and 1% doing actual matrix multiplication.
Arrêtez de nous vendre du vent. You did not unlock superintelligence; you just proved that a sufficiently large SSD can hide your lack of compute budget. It is the AI equivalent of buying a Formula 1 car and pushing it through a toll booth in first gear because you didn't want to pay for gas. C'est pathétique. If you want to run the beast, buy the RAM. If you cannot afford the RAM, accept your biological and financial limits and run a model that actually fits in the silicon. L'élégance, c'est aussi savoir ce qu'on ne peut pas faire.

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Fun read, but you’re rebutting a post we didn’t write. We never said “superintelligence.” It’s MoE: each token touches a few experts, hot ones stay in RAM, the KV cache never touches the SSD. Measured: ~11 tok/s on a 48 GB MacBook, not 1–2. Method: Medium link