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$AMD is easily a $1,200 stock IMO| CPUs TAM 🧵
Not Financial Advice! DYOR!
In this thread, I want to discuss the actual TAM for CPUs data center for just 2026, where many are giving different ranges, where I don't agree with. I will explain in detail why I disagree with these research firms and financial analysts using Math. And this thread should not be treated as Financial Advice. I'm just explaining my research and thought process so we can have a discussion.
In 2024/2025, I gave out $620 PT for FY2026 was too conservative for AMD potential. At the time, It was early and many were just laughing, that PT was unrealistic and the AI world is run on GPUs only. Today, most of these folks are laughing with me. That is ok, I dont offer financial advice, and I do not need everyone to agree with me. I respect other opinions.
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In early 2026, hyperscalers, enterprises, and OEMs are scrambling as Intel and AMD server CPUs are largely sold out for the year, with prices jumping 10–20% and lead times stretching from weeks to months (or longer for certain SKUs). What was once a GPU dominated story has flipped: the shift to explosive Agentic AI with its multi-step reasoning loops, tool calling, multi-agent orchestration, real-time data movement, and reinforcement learning, is dramatically tightening CPU:GPU ratios from the old training-era 1:4–8 all the way to 1:1 to 5:1 or even CPU-heavy configurations. CEOs across NVIDIA, AMD, Intel, Google, Meta, Microsoft, and public companies have been sounding the alarm on CNBC, Bloomberg, and earnings calls. CPUs are “cool again,” and in many agentic deployments they are becoming the new bottleneck alongside (or even ahead of) GPUs and custom ASICs.
In 2025, roughly 12-15m AI GPUs + AI ASICs GPUs shipped, and is expect to be 15-20m units by 2026, where it suggesting Training demand is not going away. The actual TAM is structural, multiplicative demand that has already forced AMD to double its long-term server CPU TAM forecast to >$120 billion by 2030 (>35% CAGR), with Dr. Lisa Su noting Q2 2026 server CPU sales expected to surge 70%+ year-over-year and demand “far exceeding expectations.”
At the same time, AMD’s secured 30–40% share of TSMC’s initial 2nm capacity (behind only Apple’s >50%) positions it to ramp Zen 6-based EPYC Venice exactly when this agentic wave hits hardest but even that aggressive five-fab 2nm expansion (with plans scaling toward 11 total advanced facilities) cannot instantly close the gap in the near-term. Supply constraints on wafers, advanced packaging, and power are compounding the squeeze, just as hyperscalers forward-buy and lock in long-term deals.
1. The actual potential TAM
Various sources and institutions are giving $50-$160-$200B CPUs TAM toward 2030, and i disagree, where supply is severely behind vs Demand by at least 2-3 years or even longer by some estimates.
The actual TAM will probably be 15-20m for FY2026. The typical average selling price from low to high end is $5,000 to $15,000, but due to rising memory, and different inflationary pressures on Semi, it would be more logical to think between $7,000-17,000.
A. CPU:GPU Ratio at 1:1
A basic calucation at mid range
=12,000 x 15-20m CPUs= $180-$240B TAM
B. CPU:GPU Ratio at 5:1
= $12,000 x 75m-100m CPUs= $900B-$1.2T TAM
Of course TSMC cannot even supply 20% of this massive inflection TAM in 2026. But do we think of Demand for TAM or Supply for TAM? Hence we are seeing massive 2nm Ramp from TSMC for $AMD.
IMO, conservatively, I would take down 15-20% on 1:1 or $135-$192B TAM for just 2026. Im not even talking about 2030.
We are just months into this, it is impossible to estimate Cagr atm, but this is 1-5 agents running tasks, I wrote a thread on 24/7 autonomous agents thread, where companies could use 50-250 agents to run tasks for them 24/7. It would require a different structural CPU:GPU to bring down the cost of token as well as handling the Orchestration bottleneck. GPUs would be useless and sit idle waiting for CPU due to highly CPU-intensive nature.
The cost per Million tokens must come down more rapidly for this 50-250 autonomous agents to work, otherwise the token cost would be too enormous.
Helios Rack is estimated to bring inference cost down to $0.0003-$0.0005/M tokens with 18 EPYC Venices along with 72 MI455x and other chips+ Components.
A heavier or CPUs dense rack would bring down inference cost further.
EPYC Verano(2027 gen 7 AI-optimized) is expected to drive inference costs meaningfully lower than the Venice baseline likely to the $0.00002–$0.00025 per million tokens range (or even sub-$0.00015 in highly optimized agentic/batch workloads).
Verano have higher core counts than Venice, LPDDR5X SOCAMM2 memory support, more AI optimized and Next-Gen rack density & efficiency.
2. $AMD secured at least 30-40% of TSMC 2nm capacity and Memory from Samsung through 2028-2030.
2 2nm fabs are entering ramping phase toward 60-65k wafers per months and 5 dedicated 2nm fabs entering mass production/ramp in 2026. Will link sub threads below if you are interest for full detail.
Apple is reported to secure 50%+ 2nm capacity for Iphone 18 and Mac chips and AMD secured at least 30-40% capacity while $NVDA $AVGO $ARM $AMZN $GOOGL and others are on 3nm.
This broader aggressive ramp from TSMC to target up to 11 fabs is to address $AMD massive growth ahead. Where $ARM is facing massive CPUs supply constraints as they have to compete with other Mega Cap players on 3nm allocation. And $INTC is also facing supply constraints for data center CPUs and PC per management with lead times extrended to longer than 12 weeks.
Dr. Su is aiming for higher than 50%+ Market share, and I believe it is achievable in 2026 or 2027 as AMD has the strongest CPUs offerings. Dr. Su did not want to take advantage of the shortage and she said during the Q1 earning call, AMD is prioritizing Units shipped while guiding margin to be inching 60%. If Jensen were in charge, I'm sure margin would be 70-75% in this kind of severe CPUs shortage condition. But that is not how Dr. Su operates for more than a decade. She wants most market share. So we will see it in revenue growth, but as TSMC ramps faster and faster, AMD Operating and FCF margin will massively improve vs prior decade. A significantly higher margin profile than before.
3. How I came up with $1,200 withint 12-18 months?
At $1,200/ share, that would be around $2 Trillion MC.
I expect FY2027 revenue to be $124-$144B where data center revenue dominates overall revenue.
AI GPUs: I will stick to the lowest end so show u that I'm conservative at $18B for each GW vs $NVDA Rubin is $30B+ (most likely Helios Rack in the $20B+ due to memory price rising). We know deals with @OpenAI and @Meta are around 12GW and additional multi-customers at multi-GW scale were hinted and will be revealed as we get to July 22-23 2026 Advancing AI event. For now I will conservatively add a bit more to this model. (3-6GW Helios Rack Range)
EPYC Venice is reported to be in $15,000-$20,000. However large customers will likely to enjoy $10-$12k discount. I expect AMD to be able to ramp 7m EPYC Venice for entire 2026 and 3-4m of EPYC Verano(higher price than Venice). If we take an average selling price of $10,000 to be on the conservative side. Take down another 30% to be even more conservative on projection. I like to be conservative. That would be ~ 7m EPYC CPUs(Venice + Verano) for FY2027 or 583,000 units per month or 15,000 additional 2nm wafers per month which is completely reasonable for current TSMC Ramp, and I may be too conservative here.
EPYC Verano and MI500 series will also be on 2nm.
AI GPUs: 3GW x $18B= $54B
EPYC CPUs: $10k x 7m CPUs= $70B
= Data center revenue alone is $124B
Other segments= probably in the $20-$25B FY 2027.
FY2027 revenue = $124-$149B
At 7m EPYC CPUs for entire 2027, that would be more than 50% market share when we comp it to availability from supply side, not from total Demand. It is possible that TSMC could significantly ramp even more capacity in 2027, so we will see.
Metric Q1 2026 FY2027
Gross Margin 55-56% 60-62%
Operating Margin 25-26% 32-35%
Net Income Margin ~22% 26-30%
FCF Margin 25% 28-30%
At $124-$149B Revenue FY 2027
Net Income would be $32-$44B
EPS would be $20-$27 (GAAP)
Non-GAAP would be $25-$31
At $1,200 a share or $2T valuation that would be:
13.4-16x Price to Sales (P/S)
38-48 P/E
At this kind of growth of AI SuperCycle, I think it is very reasonable valuation.
If we use today at $406/share or $661B MC:
2027 P/S = 4.4x-5.3x
2027 P/E = 13x-16x
Is AMD today expensive or cheap to you?
Above is already a very conservative where I trimmed 20-30% of doable units. Meaning, there could be upside if TSMC is able to ramp meaningfully like they are planning.
Conclusion:
A $1,200 per share valuation IMO for AMD in FY2027 is not expensive at all; it is, in fact, conservative when viewed against the structural explosion in agentic AI demand we have mapped out.
With server CPU TAM potentially scaling into the $100–$200B+ range in just CPU:GPU 1:1 Ratio for just 2026. AMD positioned to capture 50%+ share thanks to its 2nm TSMC allocation advantage and full-stack leadership, the company could realistically deliver $124–149B in total revenue and $25–$31+ non-GAAP EPS. At those levels, $1,200 implies a 2027 P/E = 13x-16x. Entirely reasonable for a company that will have become the clear Inference Queen (and in many workloads the preferred) AI infrastructure provider, with operating margins expanding above 30% and tens of billions in high-margin rack-scale AI revenue.
Dr. Lisa Su was right presciently so about the Agentic AI inflection all the way back to her early 2022–2023 commentary on the coming shift from pure training to inference and orchestration-heavy workloads. While the broader market only fully woke up to this in 2026 when she doubled AMD’s long-term server CPU TAM forecast to >$120B by 2030 (with >35% CAGR), Dr. Su and her team have consistently positioned the company at the center of the CPU renaissance. The explosive demand we are seeing today, sold-out lines, rising ASPs, and hyperscalers forward-buying entire gigawatts of Helios-class systems is exactly the outcome she forecasted years ago.
Not Financial Advice! DYOR!
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Mike retweeted
Lisa Su is having a moment — and she's earned it.
AMD's stock crossed a $1 trillion market cap for the first time this week, and CEO Lisa Su rang the opening bell with first lady Melania Trump. A huge week for her and the company, with AMD's earnings up next on Nov. 3.
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Meta Makes A COMEBACK, Bond Yields SURGE Higher, Does The Market CARE? | Basis Points
piped.video/watch?v=-mXiaJRF…
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$AMD shareholders just realized $630/share is cheap af in one picture $META @Muse
2 virtual CPUs on one EPYC Turin host chip
1 EPYC Venice (9996- 256 cores / 512 threads)
256 cores ÷ 2 vCPUs per Muse = 128 Muse VMs per Venice chip
1 current Turin ≈ 64 Muse users.
1 Venice ≈ 128 Muse users. Twice as many
Bears cannot stop @AMD here.
Not Financial Advice! DYOR!
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Mike retweeted
$HIMS | BofA Securities 𝗺𝗮𝗶𝗻𝘁𝗮𝗶𝗻𝘀 𝗛𝗼𝗹𝗱 on 𝗛𝗶𝗺𝘀 & 𝗛𝗲𝗿𝘀 𝗛𝗲𝗮𝗹𝘁𝗵, maintains PT at $𝟯𝟮
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$AMD shareholders just realized, if @AMD were to trade at $INTC's valuation or Fwd P/E of 65-100x, AMD would trade at $1,400-$2,200 per share.
BREAKING $AMD hit new ATH today and broke my $620 Old Personal Price Target 🚀🚀🚀
Disclaimer: I do not know or see the future. Just pure fundamental researches.
Im still keeping my personal PT on @AMD to $800 by end of 2026. 15-25% GPU market share in 2027 and biggest Agentic AI winner with superior CPUs(EPYC Venice)
If there are anything material change such as
~Massive deals with $GOOGL and $AMZN
~More CPU exclusive multi-year deals
~Massive increase from TSMC Allocation
I reserve the right to change my personal Price Target!
Congratz to all AMD OG shareholders.
For bears and short sellers that bet against me.
May be move on to other tickers that dont have my name on it.
Not FInancial Advice! DYOR!
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Why Wall Street thinks $AMD is biggest Consumers Agentic AI winner 🧵|
Why Wall Street thinks $AMD is biggest Consumers Agentic AI winner 🧵|
$META @Muse $MSFT $AMZN $GOOGL @AnthropicAI @OpenAI
Not Financial Advice! DYOR!
Consumer agentic AI is not another GPU story. A personal agent like Muse is a worker with a computer: a private VM, a real browser, files, logins, background jobs, and the ability to keep working after the user closes the app. The model still needs accelerators. The new demand spike is the army of small, always on computers those agents live in. That work runs on CPUs, and the company best built for it right now is @AMD .
The product that wins consumers is the agent that can use the world people already use websites, checkout, email, documents, and a long tail of software that was never rewritten for a new chip. That world still speaks x86. AMD’s high core EPYC parts add the other thing this workload cares about: thread density. One Venice class socket can run 256 cores and 512 threads, so a rack can host more agent sandboxes without waiting for software to be ported. $ARM can be slightly cheaper when a hyperscaler owns the guest image and only needs Linux plus its own browser. It is not a better consumer computer.
That is why the cleanest Wall Street call is not “all CPUs win equally.” UBS’s framework is the honest one: standalone agentic racks look more like 60% x86 and 40% ARM, and AMD is the x86 vendor in position to take the extra demand. Bank of America just raised AMD to a $720 target on the same idea, agentic work turns the CPU from a supporting actor into a growth engine, and AMD covers both the dense sandbox chip and the fast host next to a GPU. Intel benefits because it is the other x86 name, but it is not the leader.
That is why hyperscalers and AI labs cannot just scale consumer agentic on a pure ARM bill of materials. They will keep buying Graviton, Cobalt, Axion, Vera, and Arm AGI for orchestration, efficiency, and GPU-adjacent host nodes they fully control. The moment the agent has to live in a real sandbox compile code, drive a browser against live sites, run mixed tools, spawn thousands of concurrent VMs, they need the x86 density and software stack EPYC already ships. Meta, OpenAI, Anthropic, and the clouds can mix architectures. They still have to add significant EPYC capacity (it is projected to be 50-60m units per year combined) if they want consumer agents that act like people on the open web, not only on a company built island.
That mix is why AMD is the biggest CPU winner because it has the product, the share gain flywheel, and a second engine. Server CPU revenue is already exploding, EPYC Venice is purpose built for agent concurrency, and Ryzen AI keeps AMD in the on device Windows PC where a consumer agent wants to touch local apps. On top of that, Instinct and Helios mean AMD is not betting the company on CPUs alone. When Meta, enterprises, and cloud buyers buy agent infrastructure, AMD can show up in the sandbox, the host node, and the accelerator tray.
Consumer agents multiply CPUs. The CPUs that can run the open consumer stack at massive concurrency are high thread x86. AMD is shipping that chip now, taking share, and pairing it with GPUs instead of praying the CPU cycle is the only cycle. If personal agents scale from a novelty to a default layer of the internet, AMD is the name that sits at the center of that buildout.
Dr. Lisa Su has been saying this out loud. On the earnings call she told analysts the old 1 to 4 or 1 to 8 CPU to GPU host ratio is moving toward 1-2 to 1, and “you can even imagine if you get lots and lots of agents that you could have more CPUs than GPUs.” At Advancing AI she went further: Venice “is actually designed for the agentic era,” and “EPYC is the only CPU portfolio that leads across all three” roles; GPU host, dense agent sandbox, and general purpose enterprise. Consumer agents multiply CPUs. The CPUs that can run the open consumer stack at massive concurrency are high thread EPYC. AMD is shipping that chip now.
Not Financial Advice! DYOR!
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Training the model is only the beginning.
Enterprise AI must continuously serve inference, orchestrate agents and connect with enterprise data and applications. See how @Oracle and AMD are building an integrated foundation for this new era:
bit.ly/4hmfgUx
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$AMD KeyBanc Conference in August 2026
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$AMD $META | @Muse is NOT a chatbot! 🧵
Muse is not a chatbot. It is a persistent personal agent: each user gets a dedicated cloud virtual machine with its own browser that can run in the background, connect to email, calendars, payments, and websites, and keep working after the app is closed. That architecture is far more compute and orchestration heavy than answering a prompt.
Muse’s bottleneck is not only “not enough GPUs.” Each user gets a Muse Secure VM: an isolated Linux box with its own Chromium browser, storage, memory, and enough CPU to compile code, run sub agents, and keep cron jobs going after the phone is locked. The model still runs on accelerators, but the work that makes Muse an agent driving the browser, filling forms, calling APIs, parsing pages, enforcing Sentinel policy, and coordinating tools lives on the CPU. When hundreds of thousands of those VMs stay warm at once, core count and VM density become the constraint, not token generation.
That is why agentic systems flip the old AI rack math. Chatbots could run with roughly one CPU per four to eight GPUs. Agents loop: a short inference burst, then tool execution, then another burst. Studies of those pipelines find CPU side tool processing can account for 50–90% of end to end latency; the GPU sits idle waiting for the host to finish. Industry estimates now put agent serving closer to a 4-8:1 CPU to GPU ratio, and in some deployments more CPUs than GPUs. Packing more isolated agent sandboxes per rack is a high core, high thread CPU problem.
Meta already standardized on @AMD , NOT $INTC for that layer. It has deployed millions of EPYC CPUs across generations (Milan through Turin) and is a lead customer for 6th-gen EPYC “Venice,” co-engineered with Instinct GPUs and Helios racks. AMD has been explicit that as systems become agentic, CPUs take on orchestration, tool execution, code running, and data movement around the model. The newest EPYC 9006 line even includes SKUs aimed at “high-density agent sandbox execution” up to 256 cores / 512 threads so more persistent VMs fit in the same power envelope. That is the hardware profile Muse needs if usage stays 10x above test cohorts.
Adding more AMD CPUs therefore attacks the actual failure modes: control plane lock timeouts, degraded search, VMs that stall after minutes, and a fleet that cannot keep a browser plus Sentinel instance alive for every active user. GPUs still matter for Muse Spark inference, but they do not render pages, schedule sub agents, or isolate a Debian sandbox. Until Meta can stand up far more of those CPU backed Secure VMs, Muse will keep looking capacity constrained well before it reaches a million daily users.
Not Financial Advice! DYOR!
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$AMD Goldman Sachs Highlights
$AMD| $META Muse is pushing 4-8 CPU : 1 GPU ratio 🧵
1. “Make CPU Great Again”
@Muse is the clearest example. Meta does not just run a chatbot on shared inference clusters. Each user gets an isolated Linux VM with its own browser, storage, CPU, and memory so the agent can browse, fill forms, compile code, run cron jobs, and keep state after the app closes. Community reports put a typical instance around 2 vCPU and 8 GB RAM, plus storage. Multiply that by millions of users on a generous free tier and the bill is racks of general purpose compute, not just more H100s or MI300X .The “Make CPU Great Again” captured a symptom, not the actual constraint. Agentic products like Muse and Instinct are not mainly a story about chips beating GPUs. They force hyperscalers, AI labs, and enterprises to treat persistent per agent computers as a first class line item in CapEx .
In February 2026 AMD and Meta signed a multi-year deal for up to 6 GW of Instinct GPUs, with first gigawatt shipments in 2H 2026. That first wave is not GPUs alone. It is a custom MI450 class Instinct GPU plus 6th gen EPYC “Venice” CPUs on Helios racks that @AMD and Meta co-designed through the Open Compute Project. Meta is also a lead customer for the next EPYC generation, “Verano.” AMD has already shipped Meta millions of EPYC CPUs and earlier Instinct MI300/MI350 parts. @finkd framed the deal as compute for “personal superintelligence” and as diversification away from a single accelerator vendor.
The 6GW is the dominating headline, but the actual partnership is co-design, co-engineer and co-optimize together. Meaning @Meta will be buying tens of millions of EPYC from AMD in the coming years, because scaling consumers Agentic will require massive ten million Muse class users is tens of millions of small CPU boxes, plus a much smaller pool of shared inference GPUs for the tokens those boxes request. Consumer agent fleets push that ratio toward 4-8 CPU : 1 GPU or past it on the sandbox tier, because the “computer per person” layer does not batch the way tokens do. Free tiers make it worse: Meta can give away 100 million tokens a week only if the VM behind those tokens is cheap enough to keep allocated.
Muse’s product architecture is a dedicated Linux VM per user: browser, filesystem, Sentinel process, cron jobs, code compilation, sub-agents. That box is a CPU, memory, and storage problem. Inference of Muse Spark still wants GPUs. Keeping millions of isolated agent computers alive wants dense server CPUs. Dr. Lisa Su has been telling us that story explicitly since 2022: EPYC SKUs for agent sandboxes, AI host nodes, and general-purpose tool execution, with Venice going to 256 cores / 512 threads. Dr. Lisa Su has already said server CPU demand “far exceeded” forecasts because of agentic workloads, and AMD raised its server CPU market outlook from $120 billion to $220B+ industry target by 2030 and she aims for 50% market share of that.
Enterprises follow the same logic at smaller scale. An OpenClaw or Instinct like agent on-prem is a 4–8 vCPU Linux box with a browser. A fleet of those is an EPYC purchase, not an B200 or Rubin purchase. AMD’s marketing and product split (sandbox density vs host node I/O vs general purpose) is aimed at that exact mix.
2. Why AMD?
x86 compatibility for agent sandboxes. Muse, OpenClaw, and enterprise agents run Linux, Chromium, compilers, and random third party tools. Custom Arm CPUs win on cloud native efficiency. They lose some of that advantage when the workload is “give this agent a real computer.” EPYC’s core density and PCIe/memory bandwidth are the merchant answer to packing more VMs per rack.
Meta is both design partner and volume customer. Helios sits on Meta’s Open Rack Wide spec. Muse’s scale out is therefore not a generic CPU RFP. It is incremental demand on a stack Meta already standardized with AMD. If Muse VMs land on the same generation of EPYC that hosts Instinct nodes, AMD sells twice per watt of Meta campus: accelerator dollars and sandbox dollars.
The most expensive part of the product is keeping that machine isolated and busy: page loads, form fills, compiles, API calls, file I/O. Muse Spark inference is a burst on a shared GPU cluster. The VM is reserved CPU capacity. That is why Meta renting “a massive number of virtual machines” is a CPU shortage story.
The same pattern shows up in the rest of the consumer stack. OpenClaw with a browser wants roughly 4 vCPU and 8 GB, not a GPU, unless you host the model locally. Instinct is waitlisted on compute while it hands every user a cloud machine they can text and call. Tool heavy agent traces in the literature put 50–90% of latency on CPU side tool processing; some vendor testing says seven of eight stages in a realistic agent pipeline run on the host, not the accelerator. GPU utilization drops while the agent waits on Chromium, a compiler, or an API. Adding more GPU does not fix that. Adding more cores/threads, DRAM, and VM density does.
Density follows from concurrency, not from model size. One consumer does not need a dedicated GPU. They need a private address space that can stay up for hours. Ten million Muse class users is tens of millions of small CPU boxes, plus a much smaller pool of shared inference GPUs for the tokens those boxes request.
Conclusion:
The conclusion is that Dr. Lisa Su kept funding the unfashionable layer. In 2022 the market was already pivoting to GPUs. She still said, “We’ve said the datacenter represents our largest growth opportunity and the number one strategic priority for our company.” On Genoa she added, “It’s the highest performance datacenter processor, it’s the most efficient, and we’re delivering significantly better performance-per-watt than our competition.” Meta was already putting third gen EPYC into Open Compute servers. That was the bet: keep winning the general purpose socket even while Instinct chased NVIDIA.
Agentic consumer products paid that bet off. A Muse or Instinct user is not a batched token stream. They are a reserved Linux computer with a browser, tools, sandbox, and Sentinel that only sometimes wakes a GPU. In March 2026 Su said, “We’re seeing a significant CPU demand, frankly, as a result of the inference demand picking up,” and then, “the CPU portion of the business has actually far exceeded my expectations in terms of demand.” She told the same audience that top customers were saying CPU compute sitting alongside AI “was perhaps something that was under forecasted.” AMD then raised the server CPU TAM and split EPYC into GPU host nodes, high frequency head nodes, and dense agent sandbox parts. Venice is the sandbox chip: 256 cores, 512 threads, first PCIe Gen 6 on a CPU, tuned for agents per watt, per dollar, and per rack.
Versus NVIDIA Vera, EPYC 9996(Venice) is 1.2x per core and more than 2x platform SPECrate integer, and roughly 3.3 to 3.4x throughput in a modeled 100 kW rack. Versus Intel Xeon 6980P, the same part is in the 1.8x to 3x range on the enterprise and HPC suites AMD cites. Versus Arm AGI, AMD has a large per core gap. The direction matches the product: more cores in an x86 box that already runs Chromium, compilers, and enterprise tools without a platform change. That is what a consumer agent VM needs.
Dr. Su was early on not abandoning the socket that agents would have to live on years after. NVIDIA still owns more GPU dollars. Intel and custom Arm still take a large share of general cloud cores. AMD is the merchant vendor that kept the best high core x86 CPU line through the GPU years, then sold Meta both that CPU and the Instinct GPU in the same Helios rack. Consumer agentic being CPU dense is why that 2022 decision now looks like winning strategy for AMD long term shareholders
Not Financial Advice !DYOR!
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$AMD| $META Muse is pushing 4-8 CPU : 1 GPU ratio 🧵
1. “Make CPU Great Again”
@Muse is the clearest example. Meta does not just run a chatbot on shared inference clusters. Each user gets an isolated Linux VM with its own browser, storage, CPU, and memory so the agent can browse, fill forms, compile code, run cron jobs, and keep state after the app closes. Community reports put a typical instance around 2 vCPU and 8 GB RAM, plus storage. Multiply that by millions of users on a generous free tier and the bill is racks of general purpose compute, not just more H100s or MI300X .The “Make CPU Great Again” captured a symptom, not the actual constraint. Agentic products like Muse and Instinct are not mainly a story about chips beating GPUs. They force hyperscalers, AI labs, and enterprises to treat persistent per agent computers as a first class line item in CapEx .
In February 2026 AMD and Meta signed a multi-year deal for up to 6 GW of Instinct GPUs, with first gigawatt shipments in 2H 2026. That first wave is not GPUs alone. It is a custom MI450 class Instinct GPU plus 6th gen EPYC “Venice” CPUs on Helios racks that @AMD and Meta co-designed through the Open Compute Project. Meta is also a lead customer for the next EPYC generation, “Verano.” AMD has already shipped Meta millions of EPYC CPUs and earlier Instinct MI300/MI350 parts. @finkd framed the deal as compute for “personal superintelligence” and as diversification away from a single accelerator vendor.
The 6GW is the dominating headline, but the actual partnership is co-design, co-engineer and co-optimize together. Meaning @Meta will be buying tens of millions of EPYC from AMD in the coming years, because scaling consumers Agentic will require massive ten million Muse class users is tens of millions of small CPU boxes, plus a much smaller pool of shared inference GPUs for the tokens those boxes request. Consumer agent fleets push that ratio toward 4-8 CPU : 1 GPU or past it on the sandbox tier, because the “computer per person” layer does not batch the way tokens do. Free tiers make it worse: Meta can give away 100 million tokens a week only if the VM behind those tokens is cheap enough to keep allocated.
Muse’s product architecture is a dedicated Linux VM per user: browser, filesystem, Sentinel process, cron jobs, code compilation, sub-agents. That box is a CPU, memory, and storage problem. Inference of Muse Spark still wants GPUs. Keeping millions of isolated agent computers alive wants dense server CPUs. Dr. Lisa Su has been telling us that story explicitly since 2022: EPYC SKUs for agent sandboxes, AI host nodes, and general-purpose tool execution, with Venice going to 256 cores / 512 threads. Dr. Lisa Su has already said server CPU demand “far exceeded” forecasts because of agentic workloads, and AMD raised its server CPU market outlook from $120 billion to $220B+ industry target by 2030 and she aims for 50% market share of that.
Enterprises follow the same logic at smaller scale. An OpenClaw or Instinct like agent on-prem is a 4–8 vCPU Linux box with a browser. A fleet of those is an EPYC purchase, not an B200 or Rubin purchase. AMD’s marketing and product split (sandbox density vs host node I/O vs general purpose) is aimed at that exact mix.
2. Why AMD?
x86 compatibility for agent sandboxes. Muse, OpenClaw, and enterprise agents run Linux, Chromium, compilers, and random third party tools. Custom Arm CPUs win on cloud native efficiency. They lose some of that advantage when the workload is “give this agent a real computer.” EPYC’s core density and PCIe/memory bandwidth are the merchant answer to packing more VMs per rack.
Meta is both design partner and volume customer. Helios sits on Meta’s Open Rack Wide spec. Muse’s scale out is therefore not a generic CPU RFP. It is incremental demand on a stack Meta already standardized with AMD. If Muse VMs land on the same generation of EPYC that hosts Instinct nodes, AMD sells twice per watt of Meta campus: accelerator dollars and sandbox dollars.
The most expensive part of the product is keeping that machine isolated and busy: page loads, form fills, compiles, API calls, file I/O. Muse Spark inference is a burst on a shared GPU cluster. The VM is reserved CPU capacity. That is why Meta renting “a massive number of virtual machines” is a CPU shortage story.
The same pattern shows up in the rest of the consumer stack. OpenClaw with a browser wants roughly 4 vCPU and 8 GB, not a GPU, unless you host the model locally. Instinct is waitlisted on compute while it hands every user a cloud machine they can text and call. Tool heavy agent traces in the literature put 50–90% of latency on CPU side tool processing; some vendor testing says seven of eight stages in a realistic agent pipeline run on the host, not the accelerator. GPU utilization drops while the agent waits on Chromium, a compiler, or an API. Adding more GPU does not fix that. Adding more cores/threads, DRAM, and VM density does.
Density follows from concurrency, not from model size. One consumer does not need a dedicated GPU. They need a private address space that can stay up for hours. Ten million Muse class users is tens of millions of small CPU boxes, plus a much smaller pool of shared inference GPUs for the tokens those boxes request.
Conclusion:
The conclusion is that Dr. Lisa Su kept funding the unfashionable layer. In 2022 the market was already pivoting to GPUs. She still said, “We’ve said the datacenter represents our largest growth opportunity and the number one strategic priority for our company.” On Genoa she added, “It’s the highest performance datacenter processor, it’s the most efficient, and we’re delivering significantly better performance-per-watt than our competition.” Meta was already putting third gen EPYC into Open Compute servers. That was the bet: keep winning the general purpose socket even while Instinct chased NVIDIA.
Agentic consumer products paid that bet off. A Muse or Instinct user is not a batched token stream. They are a reserved Linux computer with a browser, tools, sandbox, and Sentinel that only sometimes wakes a GPU. In March 2026 Su said, “We’re seeing a significant CPU demand, frankly, as a result of the inference demand picking up,” and then, “the CPU portion of the business has actually far exceeded my expectations in terms of demand.” She told the same audience that top customers were saying CPU compute sitting alongside AI “was perhaps something that was under forecasted.” AMD then raised the server CPU TAM and split EPYC into GPU host nodes, high frequency head nodes, and dense agent sandbox parts. Venice is the sandbox chip: 256 cores, 512 threads, first PCIe Gen 6 on a CPU, tuned for agents per watt, per dollar, and per rack.
Versus NVIDIA Vera, EPYC 9996(Venice) is 1.2x per core and more than 2x platform SPECrate integer, and roughly 3.3 to 3.4x throughput in a modeled 100 kW rack. Versus Intel Xeon 6980P, the same part is in the 1.8x to 3x range on the enterprise and HPC suites AMD cites. Versus Arm AGI, AMD has a large per core gap. The direction matches the product: more cores in an x86 box that already runs Chromium, compilers, and enterprise tools without a platform change. That is what a consumer agent VM needs.
Dr. Su was early on not abandoning the socket that agents would have to live on years after. NVIDIA still owns more GPU dollars. Intel and custom Arm still take a large share of general cloud cores. AMD is the merchant vendor that kept the best high core x86 CPU line through the GPU years, then sold Meta both that CPU and the Instinct GPU in the same Helios rack. Consumer agentic being CPU dense is why that 2022 decision now looks like winning strategy for AMD long term shareholders
Not Financial Advice !DYOR!
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Why Wall Street thinks $AMD is biggest Consumers Agentic AI winner 🧵|
$META @Muse $MSFT $AMZN $GOOGL @AnthropicAI @OpenAI
Not Financial Advice! DYOR!
Consumer agentic AI is not another GPU story. A personal agent like Muse is a worker with a computer: a private VM, a real browser, files, logins, background jobs, and the ability to keep working after the user closes the app. The model still needs accelerators. The new demand spike is the army of small, always on computers those agents live in. That work runs on CPUs, and the company best built for it right now is @AMD .
The product that wins consumers is the agent that can use the world people already use websites, checkout, email, documents, and a long tail of software that was never rewritten for a new chip. That world still speaks x86. AMD’s high core EPYC parts add the other thing this workload cares about: thread density. One Venice class socket can run 256 cores and 512 threads, so a rack can host more agent sandboxes without waiting for software to be ported. $ARM can be slightly cheaper when a hyperscaler owns the guest image and only needs Linux plus its own browser. It is not a better consumer computer.
That is why the cleanest Wall Street call is not “all CPUs win equally.” UBS’s framework is the honest one: standalone agentic racks look more like 60% x86 and 40% ARM, and AMD is the x86 vendor in position to take the extra demand. Bank of America just raised AMD to a $720 target on the same idea, agentic work turns the CPU from a supporting actor into a growth engine, and AMD covers both the dense sandbox chip and the fast host next to a GPU. Intel benefits because it is the other x86 name, but it is not the leader.
That is why hyperscalers and AI labs cannot just scale consumer agentic on a pure ARM bill of materials. They will keep buying Graviton, Cobalt, Axion, Vera, and Arm AGI for orchestration, efficiency, and GPU-adjacent host nodes they fully control. The moment the agent has to live in a real sandbox compile code, drive a browser against live sites, run mixed tools, spawn thousands of concurrent VMs, they need the x86 density and software stack EPYC already ships. Meta, OpenAI, Anthropic, and the clouds can mix architectures. They still have to add significant EPYC capacity (it is projected to be 50-60m units per year combined) if they want consumer agents that act like people on the open web, not only on a company built island.
That mix is why AMD is the biggest CPU winner because it has the product, the share gain flywheel, and a second engine. Server CPU revenue is already exploding, EPYC Venice is purpose built for agent concurrency, and Ryzen AI keeps AMD in the on device Windows PC where a consumer agent wants to touch local apps. On top of that, Instinct and Helios mean AMD is not betting the company on CPUs alone. When Meta, enterprises, and cloud buyers buy agent infrastructure, AMD can show up in the sandbox, the host node, and the accelerator tray.
Consumer agents multiply CPUs. The CPUs that can run the open consumer stack at massive concurrency are high thread x86. AMD is shipping that chip now, taking share, and pairing it with GPUs instead of praying the CPU cycle is the only cycle. If personal agents scale from a novelty to a default layer of the internet, AMD is the name that sits at the center of that buildout.
Dr. Lisa Su has been saying this out loud. On the earnings call she told analysts the old 1 to 4 or 1 to 8 CPU to GPU host ratio is moving toward 1-2 to 1, and “you can even imagine if you get lots and lots of agents that you could have more CPUs than GPUs.” At Advancing AI she went further: Venice “is actually designed for the agentic era,” and “EPYC is the only CPU portfolio that leads across all three” roles; GPU host, dense agent sandbox, and general purpose enterprise. Consumer agents multiply CPUs. The CPUs that can run the open consumer stack at massive concurrency are high thread EPYC. AMD is shipping that chip now.
Not Financial Advice! DYOR!
$AMD shareholders just realized there will be intense comeptition with $META @Muse from $AMZN $GOOGL $MSFT $AAPL @AnthropicAI @OpenAI to perform Agentic Tasks for consumers(B2C).
And @AMD has the best x86 CPUs to truly scale Consumers Agentic. The biggest Agentic AI use case at massive scale to generate Hyper Revenue growth for decades to come.
This is a massive AMD CPU shortage that most will likely see a bidding war. Meaning AMD will have
~Pricing power
~Margin Expansion
~Years of TAM & Market Share Capture and Expansion
~Even faster Revenue Growth
~ Much higher multi-year CAGR than what was projected just weeks ago
This is going to be fun!
AMD shareholders gonna do VERY WELL in the next 3-5 years!
Not Financial Advice! DYOR!
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$AMD my old PT $620 by 2026 from 2025
$AMD is a $620 stock by 2026 🧵
Advanced Micro Devices (AMD) is solidifying its position as a key player in the AI revolution, challenging Nvidia in data center accelerators while maintaining strength in CPUs and diversified segments. As of September 27, 2025, the stock trades around $159, but this undervalues AMD's trajectory in a market where AI chip demand is forecasted to reach nearly $500 billion by 2028.
With Q2 2025 revenue hitting a record $7.7 billion (up 32% year-over-year) and net income of $872 million (54 cents per share), driven by Data Center growth to $3.24 billion, AMD's pipeline—including the MI350 and MI355 GPUs—positions it for substantial market share gains. In this analysis, I'll detail the growth drivers, then apply multiple valuation methods to support a $620 target by 2026, implying over 280% upside.
1. AMD's Unmatched Growth Potential: Riding the AI Wave
AMD's shift toward AI infrastructure is yielding results, with the Data Center segment now over 40% of revenue and growing rapidly. The Instinct MI300X GPUs have fueled adoption by hyperscalers like Microsoft, Meta, and Oracle, while the newly launched MI350 and MI355 series—built on the 4th Gen CDNA architecture—deliver up to 35x better inference performance over prior generations. The MI355X, with 288GB of HBM3e memory, offers leadership in AI and HPC workloads, and benchmarks show 1.2x-1.3x advantages in models like Llama 3.1. AMD currently holds about 5% of the data center AI GPU market, trailing Nvidia's 95%, but its focus on cost-efficient inference could double that share as enterprises prioritize power and total cost of ownership.
2. Valuation
What we know from HSBC report, MI355 is selling in the $25k-$66k, much higher than prior consensus($10-$15k) due to high demand and performance to value from @AMD chips.
We will use the base case of $25k, much cheaper than $NVDA Chips. Note that MI450 will be priced higher than MI355x(estimate to be $30k-$40k). Demand is 10 to 1 Supply, so $AMD and $NVDA have pricing power.
Etimated conservative FY 25 revenue: $34-$36B
Estimated conservative FY 26 revenue: $55B-$62B
Forward P/E
Base Case: 2026 EPS ~$9 (bullish from consensus $3.9-4.9). At 70x forward P/E (above Nvidia's 39.5x
but justified by 225% EPS growth), yields $630/share.
This is with supply contraint from $TSM. Entire $AMD MI355x supply would not be enough to supply $META alone. So this number could change if TSMC manages to allocate more supply for AMD.
EV/EBITDA
Base Case: 2026 EBITDA ~$24.75 billion (45% of $55 billion revenue, $NVDA is abt 61%).
At 40x (Nvidia's 41.5x), EV $990 billion.
Subtract ~$3 billion net debt:
Equity $987 billion / 1.62 billion shares
= ~$610/share.
Conclusion: AMD's AI accelerators, acquisitions like ZT Systems, and execution make it a high-conviction growth story, undervalued amid $500B+ AI opportunities. MI450 rack solution with ROCm 8 is on track and expected to close the GAP with competition.
$620 by end of 2026 is achievable with base case.
AMD's transformation from a PC chip underdog to an AI powerhouse is accelerating. The company's Data Center segment, now over 50% of revenue, grew 80% year-over-year in Q1 2025, driven by EPYC CPUs and Instinct MI300X GPUs. Dr. @LisaSu has emphasized that AMD's latest MI350 chips outperform Nvidia counterparts in key AI benchmarks, while the MI355 series—shipping since June 2025—delivers 35x gains over prior generations. This isn't hype: AMD's AI accelerators are already powering hyperscalers like @Microsoft Azure, @Meta , and @Oracle , @xai with partnerships expanding to include custom AI solutions.
Alright, that is it.
This is Not Financial Advice!
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$AMD personal $800 PT by end of 2026
$AMD| I'm raising my personal PT on @AMD to $800 by end of 2026 🤔⤴️📶☑️
Not Financial Advice! DYOR!
This is due to too many massive deals signed from H1 2027. And Congratz to all AMD long term shareholders, and especially the hardwork from AMD team! If you were paying attention in 2024 and 2025, u know these deals would come in 2026 and 2027.
At $800 PT year end, that would be trading at
22-25x FY2027 P/E(updated projection), which I believe would be very reasonable IMO at this kind of growth and potential. Market may shoot up AMD far higher than my PT, due to FOMO and years of the least owned among Funds. Institutional FOMO is a different beast vs Retails, so I wont speculate on that.
Q4 2026 and Q1 2027 are likely to be biggest jump YoY growth of 3 digits. Stock will be re-rated violently as the numbers to get better and better after Q1 2027.
What do we know so far in 2027 on Helios Rack:
~OpenAI & Meta want 4GW (BofA 2026 Conference)
~Anthropic wants 1GW+
~ $MSFT wants probably as much as Anthropic
~TensorWave wants 1.5GW, this is most likely dependent on if they can sign up 2GW capacity
~5C 1.5GW but did not disclose for 2027, so i will use conservative 0.5GW
~Amazon is also expected to be a customer, wont speculate on GW for now
~LumaAI/HUMAIN wants 6.6GW, or roughly 0.5GW-1GW in 2027
~Softbank France 5GW, but most likely starting in 2028= not 2027
~ $DELL $HPE $SMCI ... = probably 0.5-1GW
~ SEA, SA and Europe are likely to be in the 0.5GW combined
This is why Dr. Su went to Taiwan to secure more Advanced Packaging, the biggest bottleneck. Will be interesting to monitor $TSM supply chain ramp. Currently TSMC 2nm is on track to meet 140k WPM by end of 2026 and 220-240k WPM by end of 2027, TSMC is also investing $100B in the US for 4-5 more fabs and more CapEx in Taiwan as well.
I'm excited about Agentic AI Rack, specifically EPYC Venice, we saw the massive teaser from $HPE $AMD Venice 81,920 core per rack. Morgan Stanley estimated 6.75m Venice units to be sold in 2027.
In $HPE Rack, that is abt 320 Venice CPUs
In $AMD rack, roughly 140-150 Venice CPUs
Because AMD is optimized for TCO, while $HPE is optimized for Maximum number of Agents per rack.
So MS 6.75m Venice = ~46,551 EPYC Venice Racks.
I believe this is a conservative estimate. I will update my personal FY2027 PT later as we get more data on Q4 2026 ER.
Superior TCO leads to accelerated adoption, and this is where we are at with $AMD . I expect more customers to pop up on small-large contracts. All AI labs will need to own AMD racks to lower Training Cost and have the lowest Inference cost.
Not Financial Advice! DYOR!
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