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$AMD $TSM $WFC on AI Token cost 🧵
Not Financial Advice! DYOR
Wells Fargo's chief equity strategist Ohsung Kwon highlighted the "end of token-maxxing" as a key risk. As subsidies fade and enterprises (Walmart, Uber, etc.) become cost-sensitive, demand growth for AI services could slow. This will hit hyperscalers hard.
Now Most should know that, $GOOGL $AMZN $MSFT are mostly in-house chips or $NVDA chips and Agentic AI sent Inference costs by 50-1000x more tokens per task than simple chat queries.
$META is the new Hybrid Hyperscaler, and the first to adotp @AMD At large scale. $META and @OpenAI have cited AMD with superior Inference cost, precisely what Dr. Su bet on since 2022/2023.
The core issue in practice for many enterprises: total AI bills (especially for inference) are rising sharply despite per-token price drops and heavy use of Nvidia GPUs or in-house custom silicon. The "reality" on the ground often doesn't match vendor claims of dramatic TCO or $/M token savings, largely because of volume explosion from agentic/reasoning workflows, rather than hardware alone failing. AMD is frequently ignored for years but now positioned as delivering strongest real-world TCO advantages in owned or optimized inference setups, as Inference cost can collapse to $0.0003-$0.0005/ M tokens unlike rental rate effectively at $0.02-$0.25/M Tokens.
Ohsung Kwon core argument is high Token costs will kill demand, and may destroy the AI CapEx. However
in-house/Nvidia setups haven't fully delivered the promised token-cost relief at enterprise scale yet, contributing to bill shock. AMD's focus on accessible TCO (cheaper chips, high memory, open approaches) makes it a stronger "unlike" option for cost-sensitive inference, which is why Meta & @OpenAI (and soon all others) are scaling it AMD Racks aggressively
$NVDA, $MSFT $AMZN $GOOGL in-house chips rack-scale efficiency, Google on TPU perf/$, and AWS on Trainium 30-50% better price-performance) haven't fully translated into controlled OpEx for high-volume inference at places like OpenAI and Anthropic or to Enterprises.
Excellent raw throughput and ecosystem (CUDA), but premium pricing (~40k+/GPU), high power, and rack-scale (NVL72) still leads to high OpEx at extreme volumes. Blackwell delivers big gains (e.g., 35-100x in some agentic benchmarks), but not enough to offset token explosion for loss-making labs
Custom ASICs (TPU/Trillium, Trainium): Strong claims in cloud benchmarks (Google often best $/token for sustained inference; AWS 30-50% better price-perf), but locked to provider, optimization overhead, and less flexible for frontier model iteration. Anthropic's higher-than-expected costs on these show the gap
The exploding demand for Agentic AI (multi-step reasoning, tool use, branching workflows, reinforcement learning...) has dramatically shifted the CPU-to-GPU ratio in clusters, turning CPUs into a major bottleneck and driving up overall OpEx, even on high-end GPU/custom setups often cited at 1:1 Ratio. However, I believe the ratio or shortage is much worse than Media outlets or Hyperscalers reported. We can connect the dots from Enterprises complaining about Token Budget rised too much to this.
Under provisioned CPUs → low GPU utilization (often <10–20% in real serving), higher tail latency, more GPUs needed to compensate, and wasted power. Adding CPUs is much cheaper relative to GPUs, but shortages force over-provisioning or premium pricing. This directly inflates token costs for OpenAI/Anthropic-scale operations.
This explains part of why in-house/custom silicon (TPU, Trainium, Maia) and Nvidia-heavy setups haven't delivered the expected OpEx relief because the full stack isn't balanced.
The Helios rack (with next-gen MI450/MI455X Instinct GPUs, 6th Gen EPYC "Venice" CPUs, and Pensando Vulcano NICs) is explicitly engineered for agentic AI workloads, addressing the exact CPU/GPU imbalance and systemic inefficiencies that have driven high OpEx at OpenAI, Anthropic, and many enterprises.
This setup aims to deliver higher tokens per dollar, better GPU utilization, and lower overall rack TCO compared to unbalanced or premium-priced alternatives directly attacking the bill shock for Enterprsies.
Why $AMD is the only system that can boost Token Consumption again?
As costs per token stabilize or drop further via better balance + hardware efficiency like AMD Helios Rack and Agentic AI Rack, enterprises that paused or throttled "token-maxxing" will ramp usage again. Agentic AI becomes more economically viable when the full stack (CPU + GPU + networking) doesn't waste resources.
The difference is massive between $0.0003-$0.0005/M tokens vs $0.02-$0.25/M Tokens. That would mean token consumption to 10-50x, hence we won't see Enterprises slowing down token consumption. @OpenAI of course has the advantage here, but @Citi cited Anthropic to sign a deal with $AMD soon, so we will find out soon at Advancing AI event in July.
$META and Hypersaclers are also scaling AMD EPYC or Agentic AI Rack with $AMD, that should also help lowering token costs.
Conclusion:
AMD’s Helios rack powered by next-gen MI450/MI455X Instinct GPUs, high-core EPYC Venice CPUs, and full-stack integration directly tackles the systemic issues that have kept inference costs stubbornly high despite vendor claims from Nvidia and custom silicon players. By delivering superior rack-level balance, massive HBM4 memory advantages, lower TCO, and optimized handling of agentic workflows (with proper CPU provisioning for orchestration, retries, and tool calls), Helios is positioned to normalize enterprise AI bills where in-house and Nvidia-heavy setups have fallen short so far.
This cost relief will trigger a virtuous cycle: as OpEx stabilizes and tokens-per-dollar improve, enterprises are likely to resume (and expand) “token-maxxing” and agentic deployments driving higher overall consumption, infrastructure demand, and sustainable AI growth across the ecosystem.
Dr. Lisa Su said:
“In the past the CPU to GPU ratio was primarily just as a host node in like a one to four or one to eight configuration, now changing and getting closer to a one-to-one configuration or, even, you can even imagine if you get lots and lots of agents that you could have more CPUs than GPUs.”
She has also called Helios “truly a game changer,” noting it is “architected every part of the rack as a unified system... purpose built for the most demanding AI workloads.”
The biggest winner in Agentic AI already won when we saw this many enterprises capping token budget or trying to justify the token bills vs economic gains. Only AMD could fix this, because Dr. Su made this bet ~4 years ago, that at some point, Inference will be 90-95% of all AI compute long term.
Not Financial Advice! DYOR
$AMD $TSM $WFC on AI Token cost 🧵
Not Financial Advice! DYOR
Wells Fargo's chief equity strategist Ohsung Kwon highlighted the "end of token-maxxing" as a key risk. As subsidies fade and enterprises (Walmart, Uber, etc.) become cost-sensitive, demand growth for AI services could slow. This will hit hyperscalers hard.
Now Most should know that, $GOOGL $AMZN $MSFT are mostly in-house chips or $NVDA chips and Agentic AI sent Inference costs by 50-1000x more tokens per task than simple chat queries.
$META is the new Hybrid Hyperscaler, and the first to adotp @AMD At large scale. $META and @OpenAI have cited AMD with superior Inference cost, precisely what Dr. Su bet on since 2022/2023.
The core issue in practice for many enterprises: total AI bills (especially for inference) are rising sharply despite per-token price drops and heavy use of Nvidia GPUs or in-house custom silicon. The "reality" on the ground often doesn't match vendor claims of dramatic TCO or $/M token savings, largely because of volume explosion from agentic/reasoning workflows, rather than hardware alone failing. AMD is frequently ignored for years but now positioned as delivering strongest real-world TCO advantages in owned or optimized inference setups, as Inference cost can collapse to $0.0003-$0.0005/ M tokens unlike rental rate effectively at $0.02-$0.25/M Tokens.
Ohsung Kwon core argument is high Token costs will kill demand, and may destroy the AI CapEx. However
in-house/Nvidia setups haven't fully delivered the promised token-cost relief at enterprise scale yet, contributing to bill shock. AMD's focus on accessible TCO (cheaper chips, high memory, open approaches) makes it a stronger "unlike" option for cost-sensitive inference, which is why Meta & @OpenAI (and soon all others) are scaling it AMD Racks aggressively
$NVDA, $MSFT $AMZN $GOOGL in-house chips rack-scale efficiency, Google on TPU perf/$, and AWS on Trainium 30-50% better price-performance) haven't fully translated into controlled OpEx for high-volume inference at places like OpenAI and Anthropic or to Enterprises.
Excellent raw throughput and ecosystem (CUDA), but premium pricing (~40k+/GPU), high power, and rack-scale (NVL72) still leads to high OpEx at extreme volumes. Blackwell delivers big gains (e.g., 35-100x in some agentic benchmarks), but not enough to offset token explosion for loss-making labs
Custom ASICs (TPU/Trillium, Trainium): Strong claims in cloud benchmarks (Google often best $/token for sustained inference; AWS 30-50% better price-perf), but locked to provider, optimization overhead, and less flexible for frontier model iteration. Anthropic's higher-than-expected costs on these show the gap
The exploding demand for Agentic AI (multi-step reasoning, tool use, branching workflows, reinforcement learning...) has dramatically shifted the CPU-to-GPU ratio in clusters, turning CPUs into a major bottleneck and driving up overall OpEx, even on high-end GPU/custom setups often cited at 1:1 Ratio. However, I believe the ratio or shortage is much worse than Media outlets or Hyperscalers reported. We can connect the dots from Enterprises complaining about Token Budget rised too much to this.
Under provisioned CPUs → low GPU utilization (often <10–20% in real serving), higher tail latency, more GPUs needed to compensate, and wasted power. Adding CPUs is much cheaper relative to GPUs, but shortages force over-provisioning or premium pricing. This directly inflates token costs for OpenAI/Anthropic-scale operations.
This explains part of why in-house/custom silicon (TPU, Trainium, Maia) and Nvidia-heavy setups haven't delivered the expected OpEx relief because the full stack isn't balanced.
The Helios rack (with next-gen MI450/MI455X Instinct GPUs, 6th Gen EPYC "Venice" CPUs, and Pensando Vulcano NICs) is explicitly engineered for agentic AI workloads, addressing the exact CPU/GPU imbalance and systemic inefficiencies that have driven high OpEx at OpenAI, Anthropic, and many enterprises.
This setup aims to deliver higher tokens per dollar, better GPU utilization, and lower overall rack TCO compared to unbalanced or premium-priced alternatives directly attacking the bill shock for Enterprsies.
Why $AMD is the only system that can boost Token Consumption again?
As costs per token stabilize or drop further via better balance + hardware efficiency like AMD Helios Rack and Agentic AI Rack, enterprises that paused or throttled "token-maxxing" will ramp usage again. Agentic AI becomes more economically viable when the full stack (CPU + GPU + networking) doesn't waste resources.
The difference is massive between $0.0003-$0.0005/M tokens vs $0.02-$0.25/M Tokens. That would mean token consumption to 10-50x, hence we won't see Enterprises slowing down token consumption. @OpenAI of course has the advantage here, but @Citi cited Anthropic to sign a deal with $AMD soon, so we will find out soon at Advancing AI event in July.
$META and Hypersaclers are also scaling AMD EPYC or Agentic AI Rack with $AMD, that should also help lowering token costs.
Conclusion:
AMD’s Helios rack powered by next-gen MI450/MI455X Instinct GPUs, high-core EPYC Venice CPUs, and full-stack integration directly tackles the systemic issues that have kept inference costs stubbornly high despite vendor claims from Nvidia and custom silicon players. By delivering superior rack-level balance, massive HBM4 memory advantages, lower TCO, and optimized handling of agentic workflows (with proper CPU provisioning for orchestration, retries, and tool calls), Helios is positioned to normalize enterprise AI bills where in-house and Nvidia-heavy setups have fallen short so far.
This cost relief will trigger a virtuous cycle: as OpEx stabilizes and tokens-per-dollar improve, enterprises are likely to resume (and expand) “token-maxxing” and agentic deployments driving higher overall consumption, infrastructure demand, and sustainable AI growth across the ecosystem.
Dr. Lisa Su said:
“In the past the CPU to GPU ratio was primarily just as a host node in like a one to four or one to eight configuration, now changing and getting closer to a one-to-one configuration or, even, you can even imagine if you get lots and lots of agents that you could have more CPUs than GPUs.”
She has also called Helios “truly a game changer,” noting it is “architected every part of the rack as a unified system... purpose built for the most demanding AI workloads.”
The biggest winner in Agentic AI already won when we saw this many enterprises capping token budget or trying to justify the token bills vs economic gains. Only AMD could fix this, because Dr. Su made this bet ~4 years ago, that at some point, Inference will be 90-95% of all AI compute long term.
Not Financial Advice! DYOR
The ultimate local AI developer platform is here.
AMD Ryzen AI Halo presale is now live!
Built for #AMDevs, Ryzen AI Halo delivers the performance, memory, and AI capabilities needed to build, test, and run agentic AI, generative AI, LLM applications, and more locally.
Reserve yours now: https://t.co/qurlrK06mX
5 Pieces by Hans Zimmer \\ Iconic Soundtracks \\ Relaxing Piano [20min] https://t.co/rJQLBurxdL via @YouTube
#Piano
#Music
#Masterpiece
#PumpTheJuice
#PumpTheMusic
ARE YOU TAKING
OZEMPIC
OR WEGOVY
DID THEY TELL YOU
WHAT’S INSIDE IT?
$AMD gaining even more advanced packaging allocation from $TSM is massive catalyst, where analysts and investors dont talk enough about.
Now @theinformation is saying @Google $AVGO going some millions units toward $INTC. I was right on this months ago, where analysts refused to accept it.
@AMD had to come out and said they are getting much more supply than investors/analysts appreciated at BofA Conference Call(Full link below).
The truth is, Analysts can continue to be sexist and their time is running out. We are already in Q3 and significant supply is getting ramped/favored by TSMC from this quarter toward the next 5 years.
12 2nm/1.4nm Fabs are equivalent of 300,000-420,000 WPM capacity, and this is expected 2027/2028. And yes each quarter, AMD will gain more and more allocation. In term of Wafers Count, AMD is on track to be largest TSMC customer and be contributing significantly to TSMC revenue growth in the coming quarters and years. Dr. C. C. Wei is very bullish on Agentic AI and 2nm HPC ($AMD) on shareholder meeting on June 4th 2026. When the world's largest advanced chip is bullish on AMD, and Analysts still this delusional? Do u think TSMC just made a random decision to give AMD much higher allocation for fun or they see Agentic AI demand is higher than $500B TAM by 2030?
Why $AMD is still the least owned among all Funds? What will be AMD valuation when it generates more revenue, net income and growing faster than $AVGO in FY2027?
How did we get here? Dr. Su is sticking to 60%-62% Margin long term, like $TSM, because she wants more market share and sustainability, unlike Memory companies greeding toward 80%+. And yes she could charge more, but she wants to help Hyperscalers and AI natives to get more computes to grow. In this supply shortage environment, customers will remember who is helping them instead of charging as much as possible.
So many exciting quarters/years ahead for AMD shareholders. I can't wait to shit on sexist Analysts that been trashing AMD for nearly a decade. Don't forget AMD was given $50-$100 price target last year when I consistently putting our DDs.
It is what it is.
Not Financial Advice! DYOR!
@HolySmokas “Recent #analyst actions include *Barclays raising its $AMD ↗️ price forecast to $665, *TD Cowen increasing its forecast to $600 and *Mizuho lifting its forecast to $615.
.@AMD ↗️
#inSuWeTRUST 👩🏻💼🥇
#TogetherWeAdvance_ai 🚀🛰️
$AMD gaining even more advanced packaging allocation from $TSM is massive catalyst, where analysts and investors dont talk enough about.
Now @theinformation is saying @Google $AVGO going some millions units toward $INTC. I was right on this months ago, where analysts refused to accept it.
@AMD had to come out and said they are getting much more supply than investors/analysts appreciated at BofA Conference Call(Full link below).
The truth is, Analysts can continue to be sexist and their time is running out. We are already in Q3 and significant supply is getting ramped/favored by TSMC from this quarter toward the next 5 years.
12 2nm/1.4nm Fabs are equivalent of 300,000-420,000 WPM capacity, and this is expected 2027/2028. And yes each quarter, AMD will gain more and more allocation. In term of Wafers Count, AMD is on track to be largest TSMC customer and be contributing significantly to TSMC revenue growth in the coming quarters and years. Dr. C. C. Wei is very bullish on Agentic AI and 2nm HPC ($AMD) on shareholder meeting on June 4th 2026. When the world's largest advanced chip is bullish on AMD, and Analysts still this delusional? Do u think TSMC just made a random decision to give AMD much higher allocation for fun or they see Agentic AI demand is higher than $500B TAM by 2030?
Why $AMD is still the least owned among all Funds? What will be AMD valuation when it generates more revenue, net income and growing faster than $AVGO in FY2027?
How did we get here? Dr. Su is sticking to 60%-62% Margin long term, like $TSM, because she wants more market share and sustainability, unlike Memory companies greeding toward 80%+. And yes she could charge more, but she wants to help Hyperscalers and AI natives to get more computes to grow. In this supply shortage environment, customers will remember who is helping them instead of charging as much as possible.
So many exciting quarters/years ahead for AMD shareholders. I can't wait to shit on sexist Analysts that been trashing AMD for nearly a decade. Don't forget AMD was given $50-$100 price target last year when I consistently putting our DDs.
It is what it is.
Not Financial Advice! DYOR!
@LongGameEquity Ask @grok if #nuclear #energy is best to power #ai #datacenters “EFFECTIVELY”?
@elonmusk perspective 👇🏽
https://t.co/bLEApw4uVO
BREAKING NEWS: NVIDIA HAS JUST OPEN SOURCED THEIR RUBIN NVSWITCH TRAY BoM & DIAGRAM & IT INCLUDES AMD EYPC 3151 EMBEDDED CPU. Since there is 9 NVSwitch Trays Per VR200 Rack, that is 9 small AMD embedded CPUs per NVIDIA rack.
NVIDIA has open sourced this in their "NVIDIA/nvbmc-docs" public github repo which has an CC 4.0 open source license!
@HokiesAudio @BillRoth2020 @HokiesFB @HokiesMBB @HokiesWBB @HokiesBaseball @MikeBurnopVT @zach_w_mackey @EvanKHughes ⛳️……………………………………🏌🏼♂️
https://t.co/RbP6jClamK
Nick Saban's quotes to Congress in #NIL testimony will make you 🤣LOL - USA TODAY
#Fan
#Follow
#Fanbase
#FanActivator
#FollowTheMoney
https://t.co/rivzeZW89K
Nick Saban's quotes to Congress in #NIL testimony will make you 🤣LOL - USA TODAY
#Fan
#Follow
#Fanbase
#FanActivator
#FollowTheMoney
https://t.co/rivzeZW89K
$AMD's heading to $5T MC LT| Lowest $/M tokens 🧵
The real reason why Institutions are FOMOing into AMD while other Semi stocks are underperforming ($NVDA $AVGO)
Not Financial Advice! DYOR!
Under Dr. Lisa Su’s leadership, @AMD has transformed from a distant challenger into a formidable force in AI infrastructure, delivering the industry’s most compelling TCO story for high-volume inference. Her clear vision open ecosystems, aggressive annual roadmaps, rack-scale innovation, and relentless focus on tokens-per-dollar has positioned AMD’s Helios racks as the go-to solution for hyperscalers and AI natives struggling with exploding token costs, collapsing the cost down to $0.0003-$0.0005/M tokens. I will link various threads on this analysis to supply chain and wafer ratio if you are interested in understanding the full picture.
In the last 3-4 months, explosive Agentic AI demand significantly increased Inference demand for Agentic AI models with 5-10 agents. If you are a listener of CNBC or Bloomberg, u should know enterprises and companies are complaining abt cost of token, and how it starts to spike up way too much to make sense. The fact that most data center today are run by $NVDA Chips, where the cost is way too high for Training or Inference.
1. Token cost
Here are some quick comp, so u understand why $META @OpenAI @AnthropicAI $MSFT $AMZN Softbank $GOOGL and many more small to medium AI Natives are buying AMD CPUs and GPUs as much as they want, or pretty much AMD chips are sold out for the next 3-5 years.
Inference (Cost per Million Tokens)
~$NVDA B200 / HGX: ~$0.02–$0.08 on optimized workloads (FP4/MXFP4, speculative decoding). Significant improvement over Hopper but still premium-priced. GB200 NVL72 rack-scale: $0.05–$0.25+
~$AMD Helios Racks: $0.0003-$0.0005 per M tokens, dramatically lower than NVIDIA equivalents in owned infra. MI355X node-level: Up to 40% more tokens per dollar vs. competing solutions ( B200), driven by higher memory capacity (up to 288GB+ HBM), strong bandwidth, and lower acquisition costs.
Training
~$NVDA Rubin Rack is estimated $0.7-$1.2/M Tokens
~$AMD Helios Rack is estimated $0.65-$1.0/M Tokens
2. Why Hyperscalers and AI Natives Are Choosing AMD
Token consumption (especially Agentic) is outpacing even NVIDIA’s efficiency gains, making diversification mandatory for economic viability. Massive deals reflect this reality like $META, @OpenAI, $MSFT, Softbank, $AMZN, Oracle, LumaAI, G42...
Dr. Lisa Su’s Vision in Action: Since taking the helm, Su has driven AMD’s turnaround with disciplined execution, annual GPU cadence (MI300 → MI350 → MI400), full-stack software (ROCm 7), open ecosystems (UALink, OCP designs), and customer-centric rack-scale solutions like Helios. Her emphasis on “tokens per dollar” and TCO has turned AMD into the pragmatic choice for sustainable AI scaling.
Power/Energy Efficiency:
~Helios Rack-level is estimated at 120kW-140kW with 50% more HBM4 where Inference and Training cost matter
~Rubin Rack-Level is estimated at 160kW-230kw
AMD Helios shines in owned TCO, memory density, and energy flexibility at hyperscale.
Cost to build 1GW data center
1GW Helios Rack full build is estimated $30-$35B
1GW Rubin Rack full build is estimated $45-$55B
3. Superior CPUs to pair with GPUs on massive scale 5-10-20GW
Agentic AI. autonomous, multi-step workflows with orchestration, tool use, parallel agents, data movement, and enterprise integration has dramatically increased the importance of strong host CPUs alongside GPUs. This shifts the CPU-to-GPU ratio higher and makes balanced systems critical toward 1:1 to 5:1 as enterprises testing more than 5-10 agents.
AMD EPYC Venice excels
~Leadership core density (up to 256 Zen 6 cores per socket) for running many agents in parallel, orchestration layers, and high-throughput control-plane tasks.
~Superior performance-per-core and power efficiency ( up to 2.1x higher perf/core and 2.26x better SPECpower vs. NVIDIA Grace in benchmarks).
~Tight integration in Helios: One Venice CPU + multiple MI450 GPUs per node, enabling efficient data feeding to GPUs ("zero-copy"), parallel execution, and full rack utilization for complex agentic loops.
Hyperscalers (Meta, Microsoft, Amazon, Google, Softbank) and AI natives (OpenAI, Anthropic...) are adopting high-core EPYC at scale specifically for these agentic demands, as CPUs now handle a larger share of non-model work (orchestration, policy enforcement, tool calls). This complements AMD’s lower-cost GPUs for overall TCO wins.
Conclusion:
NVIDIA’s Vera Rubin cannot compete with a 2 years old EPYC Turin, but AMD under Dr. Lisa Su has engineered the lowest cost-per-million-tokens, highly competitive energy-efficient solutions, and superior CPU orchestration for agentic AI at scale with Helios. Dr. Su has championed this shift since at least 2023, foreseeing the rise of agentic workflows that demand far more orchestration, parallel agents, and balanced compute well before the industry fully embraced it. Her long-term vision of AI moving from simple prompts to always-on, multi-agent systems has driven AMD’s investments in high-core EPYC CPUs and integrated rack-scale solutions, perfectly positioning the company for today’s realities.
Hyperscalers and AI natives effectively have no choice but to buy more AMD system for Agentic AI as leadership in economical, power-aware, high-volume internal + agentic use. However, due to supply constraints where Supply is far behind Demand, this makes multi-vendor reality along with in-house chips drive faster industry progress, lower overall costs, and better sustainability.
Not Financial Advice! DYOR!
Video source: Microsoft Build 2026
https://t.co/AYcvtohfE5
@joe_spears7 ~heads up, misprint.
This info is incorrect- 2 different high schools 👇🏽
12. Seton Lasalle Catholic High School (Mount Lebanon)
Which Pennsylvania high schools are best for athletes? According to one study, these are the top 25:
https://t.co/LWmWYkic5e
BREAKING $AMD is ready to ship Ryzen AI Halo 🆕
in June 2026!!!! 🚀🚀🚀
AKA Personal/Local AI system.
Ryzen AI Max+ 395 (Strix Halo APU) with 128 GB of unified LPDDR5X memory, 40 RDNA 3.5 GPU compute units, and a full ROCm software stack preloaded for both Windows and Linux.
AMD Halo is $2,000-$3,000 vs
$NVDA Spark $4,699
The main purpose of the AMD Ryzen AI Halo is to serve as a compact, developer focused workstation for running intensive AI workloads locally, specifically building, testing, prototyping, fine-tuning, and deploying generative AI models (like LLMs) and Agentic AI applications without relying on the cloud.
Run models with up to 200 billion parameters on your desk using 128 GB of unified memory (shared across CPU, GPU, and NPU). This eliminates cloud costs, latency, data privacy concerns, and internet dependency.
~This is for AI developers, researchers, engineers, and creators.
~Teams or individuals wanting faster iteration, data privacy, and cost savings compared to cloud APIs.
~Users experimenting with open-source models, fine-tuning, inference, and production-like local deployment
Source: AMD CES 2026
@1000xStocks Thank you, Jeremy!
#TogetherWeAdvance_ai
#inSuWeTRUST 👩🏻💼🥇
$AMD ↗️
@HolySmokas @PumpTheJuice
@HolySmokas *~PLEASE Post Specified video /shorts on your $AMD ↗️ stock projections! on @youtube &/or @facebook #StockAnalysts
Congratulations to our Chair and CEO @LisaSu on being recognized among @FortuneMagazine's Most Powerful Women.
Her leadership continues to guide AMD and the semiconductor industry through one of the most transformative periods in computing, while inspiring the next generation of technology leaders.
Proud moment for Team AMD. 💪
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