I WAS WRONG... MacBook Neo Can LLM?! 🧐 | Local AI + TurboQuant REVIEW
Credibility score: 75/100 — Mostly Credible. Mixed credibility - some claims are solid, others need verification.
Claims analyzed
Intro: Testing MacBook Neo for LLM/ALM/VLM at Apple Store undercover after getting banned for filming. — Just Vibes (50/100)
Undercover Apple Store filming for MacBook Neo AI test? This setup screams chaos incoming 💀🕵️
Llama 3.2 4-bit 3B runs at 150 tokens/sec on 10k context — Personal Story (70/100)
Demo on Apple Store Mac shows solid speeds for tiny model — Llama 3.2 3B is made for this exact nonsense ✅😤
10k token prompt succeeds, generates at 13 tokens/sec — Personal Story (75/100)
Handled 10k context like a champ on 'mobile processor' — hate that this flexes correctly 😤✅
Inference uses 3.4GB, system at 6.7GB total RAM — Personal Story (80/100)
3.4GB for 3B Q4 inference? Spot on for Mac Neo — Activity Monitor doesn't lie ✅🔥
Gemma 4 is new Google model with audio/image/LLM support, E2B/E4B 4-bit quants — Verified (95/100)
Gemma 4 checks out perfectly — multimodal beast from Google, E2B/E4B real as hell ✅😤
Bonzai 8B is 8B param 1-bit quant model, running 2-bit compatibility version — Solid (85/100)
Bonsai-8B (typo aside) is legit 1-bit quantized 8B — 2-bit compat makes sense for Metal ✅🔥
Bonzai 8B loaded for 10k tokens but hit memory thrashing at 7.4GB on 8GB system — Personal Story (70/100)
Real demo struggle on 8GB Neo — macOS overhead eating half your RAM is peak Apple 💀😤
LM Studio used 17.1GB for Qwen, Infer used 15.4GB — Personal Story (65/100)
App choice swings 2GB RAM? Real talk for power users — pick wisely on tight hardware 💀📉
macOS reserves 4GB memory on 8GB MacBook Neo, causing LLM failure at 10k tokens — Personal Story (75/100)
4GB OS tax on 8GB machine for LLMs? Brutally honest — smaller prompts woulda worked ✅💀
4B model uses only 2.3-2.5GB memory, less than other apps — Solid (80/100)
2.5GB for 4B model? Efficient as hell on that Neo chip. Numbers track ✅😤
E4B uses 5.7GB, system at 7.77GB, hits ~4k tokens limit — Solid (85/100)
5.7GB model + system overhead on 8GB total? Realistic physics of memory limits. Checks out 😤✅
A18 Pro is like 2-year-old mobile chip but runs LLMs amazingly — Opinion (50/100)
2-year-old phone chip flex? A18 Pro crushes it anyway. Fair take 🔥
TurboQuant 2-bit at context precision reduces memory for 10k token prompt — Solid (80/100)
TurboQuant does slash KV cache memory — they're demoing it live and it works 😤✅
Latest Gemma 4 (not Gemma 2) runs 10k tokens with TurboQuant on Neo — Verified (90/100)
Gemma 4 is real, released April 2026 — and they're pushing it on entry-level hardware. Hate that it works 💀✅
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