Latest news with #NVIDIA
Yahoo
3 hours ago
- Business
- Yahoo
Texas Instruments Incorporated (TXN): Congratulations To Elliot For Buying The Stock Low
We recently published . Texas Instruments Incorporated (NASDAQ:TXN) is one of the stocks Jim Cramer recently discussed. Texas Instruments Incorporated (NASDAQ:TXN) is an American semiconductor company that makes and sells chips used in power management and other applications. Its shares have gained 17% year-to-date, primarily due to a 50% gain since late April. Texas Instruments Incorporated (NASDAQ:TXN)'s shares rose after the firm's $4.35 billion in midpoint revenue forecast for the June quarter beat analyst estimates of $4.10 billion. The shares continued their upward trajectory after NVIDIA's earnings report in May. Cramer discussed an analyst note about Texas Instruments Incorporated (NASDAQ:TXN): '[On TD Cowen goes to Buy, $245 on confidence that destocking has ended]'At 175, 180 is when the industrial revolution started that thing that was doing well. Now people are catching on. Congratulations by the way to Elliot who bought that stock low. They did great A robotic arm in the process of assembling a complex circuit board - showing the industrial scale the company operates at. Cramer discussed Texas Instruments Incorporated (NASDAQ:TXN) after its earnings. Here's what he said: 'Given that the whole world's thinking we're about to have a recession here because of the extreme tariff turmoil, these are the kinds of companies that should be slashing the numbers, but they're not. They're raising the numbers. Oh, and just tonight, the storied Texas Instruments, which has been struggling mighty of late, shed the weaknesses, put up terrific numbers that might be enough to ignite what had been a more abundant chip cohort.' While we acknowledge the potential of TXN as an investment, our conviction lies in the belief that some AI stocks hold greater promise for delivering higher returns and have limited downside risk. If you are looking for an extremely cheap AI stock that is also a major beneficiary of Trump tariffs and onshoring, see our free report on the . READ NEXT: 30 Stocks That Should Double in 3 Years and 11 Hidden AI Stocks to Buy Right Now. Disclosure: None. This article is originally published at Insider Monkey.
Yahoo
3 hours ago
- Business
- Yahoo
Advanced Micro Devices (AMD): A Bull Case Theory
We came across a bullish thesis on Advanced Micro Devices on Rebound Capital's Substack. As of 5ᵗʰ July, Advanced Micro Devices's share was trading at $137.91. AMD's trailing and forward P/E were 100.66 and 27.04 respectively according to Yahoo Finance. A close up of a complex looking PCB board with several intergrated semiconductor parts. Advanced Micro Devices (AMD) is a semiconductor company that designs high-performance processors and graphics cards for servers and PCs. The company's stock has been in a deep drawdown, having lost more than half its value from April 2024 to April 2025. This decline is in contrast to its competitor, NVIDIA Corporation (NVDA), which has risen 26% over the last year. AMD's recent Q2' 2025 earnings report showed a 59% drop in gaming hardware revenue due to a fall in demand for semi-custom chips used in PlayStation and Xbox. However, the company's data center sales rose 57% in the first quarter, suggesting that AMD's AI chips may finally be gaining traction. The company has launched its new Instinct MI350 series, which claims to have 60% more memory than Nvidia chips, and can create 40% more generated tokens for every dollar spent. This could force AI data centers to diversify away from Nvidia sooner than expected. AMD also announced a $6 billion buyback program and a string of quick acquisitions focused on AI. These developments have contributed to a 57% increase in the company's stock price from its bottom in April. AMD's rebound catalysts are promising, with potential for significant upside. The company's AI chips are gaining traction, and its new product launches could lead to increased market share. Additionally, the buyback program and acquisitions focused on AI demonstrate the company's commitment to growth. While there are risks associated with the stock, the potential rewards make AMD an attractive investment opportunity. Previously, we covered a bullish thesis on Advanced Micro Devices by Business Model Mastery in June 2025, which highlighted the company's chiplet architecture advantage, AI momentum, and strategic breadth. The company's stock price has appreciated by approximately 19% since our coverage. This is because the thesis played out. This is because AMD's structural strengths continue to anchor growth. Rebound Capital shares a similar view but emphasizes AMD's product launches and $6 billion buyback as key upside drivers. Advanced Micro Devices (AMD)is not on our list of the 30 Most Popular Stocks Among Hedge Funds. As per our database, 97 hedge fund portfolios held AMD at the end of the first quarter which was 96 in the previous quarter. While we acknowledge the potential of AMD as an investment, we believe certain AI stocks offer greater upside potential and carry less downside risk. If you're looking for an extremely undervalued AI stock that also stands to benefit significantly from Trump-era tariffs and the onshoring trend, see our free report on the best short-term AI stock. READ NEXT: 8 Best Wide Moat Stocks to Buy Now and 30 Most Important AI Stocks According to BlackRock. Disclosure: None. Error in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data
Yahoo
6 hours ago
- Business
- Yahoo
Apple Inc. (AAPL): It's 'Outrageous' To Say Tim Cook Should Leave, Says Jim Cramer
We recently published . Apple Inc. (NASDAQ:AAPL) is one of the stocks Jim Cramer recently discussed. Apple Inc. (NASDAQ:AAPL) is a regular feature of Cramer's morning show. Earlier during the year, the CNBC host defended the stock after shares struggled due to weakness in the Chinese smartphone market. Cramer continued to defend Apple Inc. (NASDAQ:AAPL) after analysts soured on its Siri and perceived AI weakness. He continued to defend the company, and this time, Cramer discussed reports suggesting that Apple Inc. (NASDAQ:AAPL) CEO Tim Cook should leave the company: '[On analysts saying Tim Cook should leave] I wanted to say, I thought it was outrageous. I thought that was outrageous. I wish that I could've, that I would love to be able to be Tim Cook and say what I've done for the shareholders. And it's just incredible. The amount of wealth that man has created. I mean that's like when I saw, at one point today I saw in premarket that NVIDIA was down. I was going to call for Jensen's firing. . .I mean come on. . .you know Jensen has to go. Can we like stop? Like stop? Like Tim Cook's great.' A wide view of an Apple store, showing the range of products the company offers. Cramer discussed key traits for a potential Tim Cook successor earlier. Here is what he said: '[On Morgan Stanley saying Tim Cook's successor could benefit from having a hardware background] Well look, it's funny hardware is part of the, I'm glad you mentioned hardware, hardware's part of the issue of how NVIDIA got to where it is. This is an essentially, there's a belief in many people on Wall Street and in Silicon Valley, that hardware prevails here because we're gonna get rid of a huge number of people who would do SaaS, you know, software as a service, and that includes, yes, Salesforce, includes ServiceNow, includes DataDog which got out of the S&P. Because there are going to be fewer and fewer people who are actually in the organization who need that. But that does not mean that you wouldn't need more Apple.' While we acknowledge the potential of AAPL as an investment, our conviction lies in the belief that some AI stocks hold greater promise for delivering higher returns and have limited downside risk. If you are looking for an extremely cheap AI stock that is also a major beneficiary of Trump tariffs and onshoring, see our free report on the . READ NEXT: 30 Stocks That Should Double in 3 Years and 11 Hidden AI Stocks to Buy Right Now. Disclosure: None. This article is originally published at Insider Monkey.
Hans India
7 hours ago
- Hans India
How to Find the Best GPU for AI?
New Delhi [India], July 16: As artificial intelligence continues to reshape industries, the hunger for high-performance computing resources just keeps growing. And when it comes to powering AI innovation, one of the unsung heroes is the GPU VPS. From training those massive neural networks to running real-time inference that blows your mind, the GPU you choose literally shapes your entire AI pipeline. But let's be real, with so many models, specs, and VPS providers out there, figuring out the "best" GPU for AI can feel a bit tough. So, your first big step? getting a handle on the technical metrics and architectural advantages of what's on offer. GPU Architecture When you're sifting through GPUs for those demanding AI workloads, there are three critical elements you absolutely have to zero in on: tensor cores, CUDA cores, and memory bandwidth. These guys are the real muscle. Tensor cores, first popping up with NVIDIA's Volta architecture and continuously refined through the Ampere and Hopper generations, are specialized wizards at mixed-precision calculations (think FP16, BF16, INT8). They can dramatically slash your training times, which is a huge win. Then you've got CUDA cores, the general-purpose workhorses that determine how versatile your GPU will be across different frameworks. Bandwidth is often overlooked, but it can quickly become a bottleneck when you're training large models, especially with those hungry transformer architectures. For instance, the NVIDIA A100 boasts a whopping 2 TB/s of memory bandwidth. Here's a quick rundown of some leading GPUs: GPU Model VRAM CUDA Cores Tensor Cores Memory Bandwidth Ideal Use Case NVIDIA A100 40–80 GB 6912 432 1555 GB/s LLM training, multi-GPU setups RTX 4090 24 GB 16384 512 1008 GB/s Deep learning, generative AI RTX 3080 10–12 GB 8704 272 760 GB/s Model prototyping, DL training Tesla T4 16 GB 2560 320 320 GB/s Inference, low-power tasks RTX 3060 12 GB 3584 112 360 GB/s Entry-level experimentation Performance Benchmarks and Profiling Your AI Workload Before committing to a GPU VPS, it's crucial to test models with your specific AI workload. Real-world performance varies wildly based on model complexity and optimization. For example, CNNs for image classification behave differently than transformer-based architectures for natural language processing—it's like comparing apples and oranges! Forget raw core counts; FLOPS, memory latency, and inference throughput tell the real story. An RTX 4090 might have more CUDA cores than an A100, but its lower FP64 performance makes it less ideal for scientific AI, though it's a beast for generative tasks like GANs. See the difference? Profiling your workload with tools like NVIDIA Nsight or PyTorch's isn't just an option; it's a must-do. It'll pinpoint GPU utilization, highlight bottlenecks, and show how your model scales. Deployment Models Picking the best GPU for AI isn't just about raw power, but also how you deploy it. A GPU VPS offers sweet advantages: remote accessibility, elastic scaling, and less infrastructure overhead. But be smart—evaluate your provider's latency and virtualization overhead. Some GPUs shine in bare-metal configurations, while others excel in virtual environments using NVIDIA GRID and vGPU. For latency-sensitive apps, even slight virtualization overhead can impact performance. Look for PCIe Gen4 support and low I/O contention. Cost-wise, pricing scales with VRAM and GPU generation. A smart approach is to start with mid-range GPUs like the 3080 for inference, then step up to A100s or H100s for larger model training. It's all about playing it smart! Fresh GPU Insights A fascinating Cloudzy blog deep-dive recently showed how developers fine-tune AI by matching project scale with GPU architecture. It highlighted that memory bandwidth and tensor core utilization are often under-optimized due to poor GPU choices. For instance, an AI team saw their language translation's inference latency slashed by 35% by upgrading from a 3060 to a 3080 Ti, with minimal cost increase. This confirms that understanding workload demands beats just grabbing the most expensive GPU. Plus, Cloudzy's infrastructure offers pre-configured environments for TensorFlow, PyTorch, and JAX, meaning faster experimentation and iteration while keeping full control. Pretty neat, right? Wrapping Up To truly nail down the best GPU for your AI journey, look past brand names. Dive into architecture, workload requirements, and deployment contexts. Tensor core efficiency, memory bandwidth, and a scalable VPS infrastructure are your secret weapons for accelerating AI innovation without unnecessary costs. By dissecting your workload, benchmarking performance, and picking a GPU VPS that aligns with your strategy, you'll be in the best position to train, deploy, and optimize your AI models in today's competitive landscape. It's a bit of work, but trust me, it pays off big time!
Yahoo
8 hours ago
- Business
- Yahoo
Apollo economist warns: AI bubble now bigger than 1990s tech mania
-- Apollo Global Chief Economist Torsten Sløk is sounding the alarm on what he sees as an even more inflated market than the one that led to the dot-com crash. In a striking warning today, Sløk said, 'The difference between the IT bubble in the 1990s and the AI bubble today is that the top 10 companies in the S&P 500 today are more overvalued than they were in the 1990s.' His comments come as stocks continue to reach new highs, with investors increasingly betting big on artificial intelligence as the next transformative force in global markets. The enthusiasm has driven tech giants like NVIDIA (NASDAQ:NVDA), Microsoft Corporation (NASDAQ:MSFT), and Meta (NASDAQ:META), among others, to record-breaking valuations, with NVIDIA recently becoming the first company to trade over the $4 trillion valuation level. Slok highlights that the top 10 stocks have disproportionately high valuations relative to the overall market. Sløk argues that this extreme concentration and the sky-high expectations built into prices mirror and surpass the late-1990s mania, when investors poured money into internet stocks with little regard for profitability. Unlike the IT bubble, today's top companies are highly profitable, but Sløk cautions that even strong fundamentals can't justify unlimited multiples. As AI fever continues to spread from Silicon Valley to Wall Street, Sløk's warning serves as a sobering counterpoint to the hype. Whether his fears prove prophetic or overly cautious, his message is clear: this is not the 1990s but potentially something bigger, and riskier. Speaking of AI, read this: Surge of 50% since our AI selection, this chip giant still has great potential Related articles Apollo economist warns: AI bubble now bigger than 1990s tech mania Surge of 50% since our AI selection, this chip giant still has great potential Apollo economist warns: AI bubble now bigger than 1990s tech mania
