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$AMD, Top wall street Analyst $350 PT🚀🚀🚀 C.J. Muse from Cantor Fitzgerald is the most bullish analyst on $AMD, maintaining an Overweight (Buy equivalent) rating with a price target of $350. This target, reiterated on November 5, 2025, following AMD's Q3 earnings beat and raised guidance, implies about 40% upside from the stock's recent close around $250. Muse's commentary, drawn from his post-earnings research note, emphasizes $AMD's accelerating momentum in AI-driven data center products as a core growth engine, while acknowledging near-term headwinds in client and embedded segments. He views the company's execution as "flawless" in a "seemingly insatiable" AI compute demand environment, positioning $AMD to capture significant share from Nvidia in GPUs and Intel in CPUs. Below is a detailed breakdown of his analysis, structured by key themes. ~Muse credits the beat to "stronger-than-expected AI GPU adoption" and raised Q4 guidance (EPS $1.05–$1.15, revenue $7.3–$7.7 billion), implying full-year 2025 data center growth of 80%+. ~Key Quote: "AMD's data center momentum is sustainable and accelerating, with AI infrastructure demand outpacing supply into 2026." ~Muse projects $6.5 billion in AI GPU revenue for 2025, a 150%+ increase YoY, fueled by the MI300 series and upcoming MI350 (ramping mid-2025). He sees GPUs eclipsing CPU sales by H2 2026, with total data center GPU opportunity at $25–50 billion annually (5–10% share of a $500 billion TAM). ~He notes @AMD 's 35% server CPU market share (vs. Intel's 65%) is expanding due to Zen 5 architecture advantages in power efficiency and core density, winning bids from AWS, Google Cloud, and Meta. ~Muse praises CEO Lisa Su's "disciplined" approach, with net cash position of $5 billion providing a "cushion" against valuation multiples (trading at 45x forward EPS vs. Nvidia's 60x). ~ At $350 target, $AMD trades at 50x 2026 EPS, justified by 25%+ CAGR in data center revenue. Muse sees "limited downside" to $220 if AI hype cools, but upside to $400+ on MI400 cycle wins. Overall, Muse's thesis is unapologetically bullish: $AMD is "the No. 2 AI player with No. 1 potential," leveraging a $500B+ TAM expansion. His track record (75% hit rate on targets) and focus on execution over hype make this a standout call. Investors should watch Analyst Day for 2026 guidance, which could propel shares toward $300 by year-end.

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For Elon Musk and Tesla, this represents a valuable opportunity to gain real-world foundry experience at an exceptionally low cost — something TSMC would never allow. It enables Tesla to enhance its chip design capabilities, particularly in manufacturability, while also gaining deeper manufacturing knowledge, which will give them more leverage in future negotiations with foundries. In the long run, Elon Musk's businesses will only demand more advanced chips, so acquiring core manufacturing expertise becomes a strategic advantage. Tesla’s AI6 chip is currently scheduled for mass production in 2027, using Samsung’s 2nm node (SF2). SF2 currently has a yield of 40–45%, lower than TSMC’s N2 (over 70%) and Intel’s 18A (50–55%). Elon Musk's execution is proven, and SF2's adoption of the same GAA technology as SF3 should facilitate mass production. Even so, it's still difficult to predict whether Samsung can successfully ramp AI6 on SF2 as scheduled. If production falls short of expectations, the worst-case scenario for Tesla would be to shift the order back to TSMC and absorb the resulting delays to AI6.. However, Tesla’s edge in real-world AI could significantly reduce the risk of AI6 delays. Regardless, Tesla still gains from enhanced design capabilities and deeper chip manufacturing know-how. As for Samsung — with nothing to lose, why not give it a try? This partnership presents manageable downside and strong upside potential for both sides. If AI6 reaches mass production smoothly, chip design and manufacturing could become a core competitive advantage across Elon Musk’s businesses — enabling greater flexibility and lower costs. While Samsung may not fully catch up with TSMC in advanced nodes, it has at least discovered a new business model that actively involves customers in the manufacturing process.

The final boss in Musk's 2025 Comp Plan is achieving $400B in EBITDA. Perspective: $238.24 billion: Saudi Arabian Oil Company (Saudi Aramco) achieved the highest annual EBITDA on record in 2022, driven by elevated global oil prices, higher sales volumes, and strong refining margins amid geopolitical tensions and post-pandemic demand recovery. This figure surpasses other major companies like ExxonMobil ($102.59 billion in 2022) and NVIDIA ($34.48 billion in fiscal 2024). For context, Aramco's 2023 EBITDA was approximately $236.81 billion, still leading but lower than 2022's peak. Let that sink in.

$AMD MI450 vs $NVDA Rubin Comprehensive 🧵 The @AMD Instinct MI450 (part of the MI400 series) and @nvidia 's Rubin architecture (expected flagship like the R200 or VR200) represent the next wave of AI accelerators, both slated for production and deployments in 2026. These chips target hyperscale AI training and inference, with AMD emphasizing memory capacity and rack-scale integration to challenge NVIDIA's ecosystem dominance. Both use HBM4 memory and advanced packaging, but MI450 leverages a superior process node for density. Performance metrics are peak theoretical (FP4 for AI inference/training); real-world varies by workload. Architecture AMD: CDNA 5 (UDNA-based) NVDA: Rubin (successor to Blackwell) Process Node AMD: TSMC 2nm NVDA: TSMC 3nm Memory AMD: 432 GB HBM4 NVDA: 288 GB HBM4 Memory Bandwidth AMD: 9.6 TB/s NVDA: 20 TB/s (enhanced post-MI450 reveal) Max Compute FP4 vs FP8 AMD: ~40 PFLOPS(FP4) & ~20 PFLOPS(FP8) NVDA: ~50 PFLOPS(FP4) & ~25 PFLOPS(FP8) Power Consumption AMD: 1000-1400W NVDA: 1,800-2,300W Rack-Scale Solution AMD: Helios (72 GPUs; 31 TB HBM, 1.4 PB/s total) NVDA: NVL144 (144 GPUs; liquid-cooled, Vera CPU Release Date: Both in H2 2026 Estimated Price: AMD MI450: $30k-$40k(large scale discount) NVDA Rubin: $45-$60k(Minimal discount seen so far) Total Cost of Ownership (TCO) for AI accelerators like the MI450 and Rubin encompasses not just the upfront hardware price but also ongoing expenses such as energy bills, cooling infrastructure, maintenance, and scalability over 3-5 years in data centers. AMD's MI450 delivers a significantly lower TCO estimated at 20-40% less than Rubin's in inference-heavy workloads primarily due to a combination of pricing aggression, superior energy efficiency, and optimized rack-scale designs that minimize infrastructure upgrades. ~Lower Acquisition Costs: AMD GPUs are typically priced 25-35% below NVIDIA equivalents, with MI450 units projected at $30K-40K versus Rubin's $45K-60K. This stems from AMD's fabless model leveraging TSMC without NVIDIA's custom ecosystem premiums. Partnerships like the 6GW OpenAI deal and Oracle's 50K-unit order further drive volume discounts, reducing per-unit costs for hyperscalers. ~Energy and Cooling Savings: Power is the biggest TCO driver, accounting for 40-60% of lifetime costs in AI clusters. MI450's estimated 1,200W TGP (versus Rubin's 2,300W) translates to ~48% lower draw per GPU, cutting annual electricity bills by up to $500K per 1,000-unit rack at $0.10/kWh. Cooling follows suit: AMD's chiplet-based Helios racks (72 GPUs) require less dense liquid-cooling setups than NVIDIA's NVL144 (144 GPUs), avoiding costly retrofits for existing data centers. TSMC benchmarks show 2nm nodes yielding 20-30% better perf/Watt, amplifying these savings in memory-bound tasks like LLM inference. ~Higher Density and Scalability: MI450's Infinity Fabric enables denser racks (up to 128 GPUs in IF128 configs) with 1.4 PB/s aggregate bandwidth, delivering 6.4 EFLOPS FP4 nearly double Rubin's 3.6 EFLOPS in equivalent space. This means fewer racks for the same throughput, slashing deployment costs by 15-25%. AMD's UALink compatibility also future-proofs against vendor lock-in, reducing long-term refresh expenses. How MI450 Consumes Nearly Half the Power? The MI450's ~1,200W Thermal Design Power (TGP) is indeed about half of Rubin's 2,300W, a deliberate design choice rooted in AMD's advanced process node, chiplet architecture, and workload optimization. This isn't just smaller transistors it's a holistic efficiency play that avoids NVIDIA's power escalations to chase raw FLOPS. ~Superior Process Node: MI450's core compute dies use TSMC's 2nm (N2P) node, versus Rubin's full-chip 3nm (N3P). The 2nm shrink delivers ~1.15x transistor density and 20-30% better power efficiency per TSMC data, allowing AMD to hit 40 PFLOPS FP4 at lower voltages/clocks. NVIDIA's redesigns bumping TGP by 500W to counter MI450 pushed Rubin to 2,300W for marginal gains in bandwidth (20 TB/s vs. MI450's 19.6 TB/s) ~Chiplet Modularity: AMD's multi-die approach (separate accelerator core, interposer, and media dies) isolates power-hungry elements, enabling fine-grained scaling. Only the core needs 2nm; others use cost-effective 3nm, reducing overall leakage and dynamic power by 15-25% compared to NVIDIA's monolithic (or early chiplet) Rubin. This modularity also cuts thermal hotspots, allowing sustained boosts without thermal throttling. ~Architecture and Workload Focus: CDNA 5 prioritizes dense matrix ops for AI ( FP4/FP8) with fewer overhead cycles than Rubin's tensor-heavy design, which inflates power for peak training bursts. In rack-scale, Helios's Infinity Fabric (IF64/128) offloads interconnect power to fabric links, versus NVLink 6's GPU-centric draw. AMD's ROCm optimizations yield 4x efficiency gains over MI300X in inference, where power scales linearly with model size MI450 handles 432 GB HBM4 without NVIDIA's sharding penalties that spike energy use. Conclusion: As the 2026 AI landscape crystallizes, AMD's Instinct MI450 emerges not as a mere challenger but as a transformative force, poised to erode NVIDIA's 95% market stranglehold through unmatched TCO efficiency and power frugality. By harnessing TSMC's 2nm edge and chiplet ingenuity, MI450 delivers Rubin-caliber performance 40 PFLOPS FP4, 432 GB HBM4 at half the power (1,200W vs. 2,300W) and 20-40% lower lifetime costs, making it the go-to for inference-dominated hyperscalers like $META (42% allocation), @OpenAI (6GW commitment) and Oracle (50K units). NVIDIA's Rubin retains software supremacy and training prowess via CUDA's moat, but AMD's Helios racks boasting 1.4 PB/s bandwidth and 6.4 EFLOPS density signal a "Milan moment" per AMD management, potentially flipping 20-30% share by 2026 amid HBM4 crunches