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.
BSmeter analyzed "I WAS WRONG... MacBook Neo Can LLM?! 🧐 | Local AI + TurboQuant REVIEW" and rated it 75/100 for credibility (a BS score of 25/100 — mostly credible), on 2026-04-26. Its weakest claim — "Intro: Testing MacBook Neo for LLM/ALM/VLM at Apple Store undercover after getting banned for filming." — scored 50/100 and was flagged as just vibes. 14 claims were checked against the video transcript. Scores are produced by BSmeter's AI analysis of the transcript, not independent human 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 💀✅
See the full analysis with sources and timestamps →