Reward 500 Tiger-coins$Broadcom(AVGO)$ the next Nvidia? Broadcom 🚀 soared 12% after earnings.
Q3 revenue: $15.95B, +22% YoY (beat est. $15.84B)
Non-GAAP net profit: $8.40B, +37.3% YoY
EPS: $1.69, also beat expectations
On the call, CEO Hock Tan dropped a bombshell:
A new mystery customer placed a $10B order for Broadcom’s custom AI accelerator XPU. Rumor has it — OpenAI is set to begin mass production with Broadcom, shipping next year.
Why do tech giants like $Alphabet(GOOG)$ turn to Broadcom for AI ASICs?
Morgan Stanley notes cloud providers like Google, Amazon, Microsoft are aggressively investing in ASIC design.
Because Nvidia’s GPUs are insanely expensive 💰 and hard to get. Plus, you’re locked into CUDA software + NVLink for scaling. For hyperscalers and AI players desperate for compute but allergic to Nvidia’s price tag, ASICs are the alternative.
ASICs = lower cost, lower power, more efficient for inference workloads
Nvidia GPUs = superior raw performance, but higher risk & cost
What’s next for the stock? 📉📈
History shows:
If Broadcom pops big on earnings → stock usually dips for 2+ months.
If it dumps on earnings → tends to rebound hard for 2 months.
Right now, AVGO is up +12%. But Nvidia’s stock is falling below $170. NVIDIA’s PE & PEG are both at a reasonable level.
So the big question: Do you chase AVGO’s rally, or buy NVDA’s dip?
REWARDS
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Still, I’m cautious about chasing AVGO after a +12% spike, since history shows it often cools off post-earnings. Meanwhile, Nvidia’s dip looks more attractive — despite competition, it still dominates training workloads and the CUDA ecosystem, which are hard to replace.
For now, I’d watch Broadcom for consolidation while eyeing NVDA’s $NVIDIA Corp(NVDA)$ pullback as a better entry. Both can benefit from the AI boom, but I see Broadcom as the custom chip play and Nvidia as the core AI bet, with each serving different needs in the market.
@Tiger_comments @TigerStars
一個新的神祕客戶放了一個$10B訂單對於博通的定製AI加速器XPU。有傳言說-OpenAI將與博通開始大規模生產，明年發貨。
因为Nvidia的GPU非常昂贵💰而且很难得到。另外，您被锁定在CUDA软件+NVLink中进行扩展。对于渴望计算但对Nvidia的价格标签过敏的超大规模企业和人工智能玩家来说，ASIC是另一种选择。