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$INFQ GPU + CPU + QPU Frontier models are hitting saturation on tokens (data volume) and context windows (memory). $INFQ CTO walks through how they and $NVDA are working together to add a Quantum Processing Unit to the chain as the next inflection point.

YeahDave

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57,181 görüntüleme • 2 ay önce •via X (Twitter)

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"Quantum is here, and it's time to make sure we are capitalized to address the opportunity that's ahead of us," Infleqtion CEO Matt Kinsella says on going public on the New York Stock Exchange under the ticker $INFQ.

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In 2025, I told you to buy quantum and we made millions together. In 2026, I told you to buy quantum AGAIN. This time, you'll retire within the next 4 years. Here are the BEST quantum stocks to buy right now: 1. IonQ $IONQ 2. International Business Machines $IBM 3. Infleqtion $INFQ 4. Rigetti Computing $RGTI 5. D-Wave Quantum $QBTS 6. Xanadu $XNDU 7. Quantum Computing $QUBT 8. Arqit Quantum $ARQQ 9. Horizon Quantum $HQ All my buy and sell signals in Discord @

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The models we use every day are brilliant strangers. They forget your organization the moment a chat ends, then relearn it on the next query. Engram fixes that. It learns your world once and reuses that memory, matching frontier systems on 1-10% of the tokens. Microsoft, Notion, and Harvey are already testing it within their organizations. Congratulations to the team, and hear directly from Dan Biderman (CEO and co-founder) and Sabri Eyuboglu (CTO and co-founder) with "Leigh Marie" Braswell ⬇️

The models we use every day are brilliant strangers. They forget your organization the moment a chat ends, then relearn it on the next query. Engram fixes that. It learns your world once and reuses that memory, matching frontier systems on 1-10% of the tokens. Microsoft, Notion, and Harvey are already testing it within their organizations. Congratulations to the team, and hear directly from Dan Biderman (CEO and co-founder) and Sabri Eyuboglu (CTO and co-founder) with "Leigh Marie" Braswell ⬇️

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$NVDA is entering the consumer PC chip market with RTX Spark, an $ARM based CPU/GPU/AI chip for Windows laptops and mini-PCs. The flagship version includes 20 CPU cores, 6,144 GPU cores, and up to 128GB of unified LPDDR5X memory, aimed at local AI agents, creators, developers, and gaming. Nvidia says 30+ laptops and 10+ desktops are already in development with partners including Microsoft, Dell, HP, Asus, Lenovo, MSI, Acer, and Gigabyte.

$NVDA is entering the consumer PC chip market with RTX Spark, an $ARM based CPU/GPU/AI chip for Windows laptops and mini-PCs. The flagship version includes 20 CPU cores, 6,144 GPU cores, and up to 128GB of unified LPDDR5X memory, aimed at local AI agents, creators, developers, and gaming. Nvidia says 30+ laptops and 10+ desktops are already in development with partners including Microsoft, Dell, HP, Asus, Lenovo, MSI, Acer, and Gigabyte.

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I had to test it myself to believe this unreal inference speed. 3,000 tokens/s for 1 user on standard datacenter GPUs. They leveraged a hidden efficiency gap in how GPUs generate tokens. Kog just achieved 3,000 tokens/s on 8× AMD MI300X GPUs and 2,100 on 8× NVIDIA H200 (FP16, no speculative decoding). Their tech preview is on a 2B model, and they show how their techniques will scale to large frontier MoE models at similar speeds. That's a huge number because normal low-batch GPU decoding for 2B to 8B models is usually closer to 100 to 300 tokens/s per request, so Kog is claiming something like a 10X to 30X jump in the speed one user actually feels. Their trick: they are getting the speed by treating LLM decoding as a memory streaming problem, not mainly a math problem. For 1 user at batch size 1, the GPU is not doing big, efficient matrix-matrix work like in training or large-batch serving; it is repeatedly pulling the model’s active weights from high-bandwidth memory for each new token, so speed depends on how smoothly those weights keep flowing. Normal inference stacks keep breaking that flow. They run many separate GPU programs for different parts of the model, move intermediate results through memory, wait at synchronization points, talk back to the CPU for scheduling or sampling, and then repeat this token after token. Kog’s answer is to co-design 3 things that are usually tuned separately: the runtime, the low-level GPU code, and the model architecture. The biggest engineering move is the monokernel, where the whole decode pass runs as 1 persistent GPU-resident program, including sampling, so the system does not keep stopping for kernel launches, CPU scheduling, and intermediate memory round trips. They also rebuilt synchronization, because their own measurements say grid sync was eating around 35% of token-generation time; instead of making every compute unit wait at a broad barrier, each unit waits only for the exact data it needs. On AMD MI300X, they also map memory access around the chiplet layout, because memory latency changes depending on which die makes the request. Then their Laneformer model uses Delayed Tensor Parallelism, which lets cross-GPU communication happen in the background instead of blocking every layer.

I had to test it myself to believe this unreal inference speed. 3,000 tokens/s for 1 user on standard datacenter GPUs. They leveraged a hidden efficiency gap in how GPUs generate tokens. Kog just achieved 3,000 tokens/s on 8× AMD MI300X GPUs and 2,100 on 8× NVIDIA H200 (FP16, no speculative decoding). Their tech preview is on a 2B model, and they show how their techniques will scale to large frontier MoE models at similar speeds. That's a huge number because normal low-batch GPU decoding for 2B to 8B models is usually closer to 100 to 300 tokens/s per request, so Kog is claiming something like a 10X to 30X jump in the speed one user actually feels. Their trick: they are getting the speed by treating LLM decoding as a memory streaming problem, not mainly a math problem. For 1 user at batch size 1, the GPU is not doing big, efficient matrix-matrix work like in training or large-batch serving; it is repeatedly pulling the model’s active weights from high-bandwidth memory for each new token, so speed depends on how smoothly those weights keep flowing. Normal inference stacks keep breaking that flow. They run many separate GPU programs for different parts of the model, move intermediate results through memory, wait at synchronization points, talk back to the CPU for scheduling or sampling, and then repeat this token after token. Kog’s answer is to co-design 3 things that are usually tuned separately: the runtime, the low-level GPU code, and the model architecture. The biggest engineering move is the monokernel, where the whole decode pass runs as 1 persistent GPU-resident program, including sampling, so the system does not keep stopping for kernel launches, CPU scheduling, and intermediate memory round trips. They also rebuilt synchronization, because their own measurements say grid sync was eating around 35% of token-generation time; instead of making every compute unit wait at a broad barrier, each unit waits only for the exact data it needs. On AMD MI300X, they also map memory access around the chiplet layout, because memory latency changes depending on which die makes the request. Then their Laneformer model uses Delayed Tensor Parallelism, which lets cross-GPU communication happen in the background instead of blocking every layer.

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Interesting that in three of my recent conversations with Krishna at Anthropic, Dylan Patel, and Gavin Baker, each said that frontier tokens are capturing the majority of the economic value. Gavin: "An overwhelming amount of the economic returns to AI at the model layer have been at the frontier. That's surprising to me, and I think it's been surprising to a lot of people. This is one of the most important questions to be answered, and you need to have a hypothesis on it as an investor." Krishna: "We think the returns to frontier intelligence are extremely high. Customers invest really heavily in more tokens with the newer models. The ones at the frontier clearly are capturing this economic value, driving meaningful ROI for customers. The returns to frontier intelligence are not slowing down." Dylan: "No one gives a crap about GPT-4 class models. They want the frontier because the frontier lets them create the economically valuable things."

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PowerInfer - a high-speed inference engine for deploying LLMs locally. Just came across this super interesting project on speeding up inference. It's not MoE but it's a simple approach that exploits the high locality in LLM inference to design a GPU-CPU hybrid inference engine. Hot-activated neurons are preloaded onto the GPU for fast access, while cold-activated neurons (the majority) are computed on the CPU. This approach significantly reduces GPU memory demands and CPU-GPU data transfer. It achieves an average token generation rate of 13.20 tokens/s, with a peak of 29.08 tokens/s, across various LLMs on a single NVIDIA RTX 4090 GPU. It's on only 18% lower than that achieved by a top-tier server-grade A100 GPU. It also significantly outperforms llama.cpp by up to 11.69x while retaining model accuracy. There is a lot more innovation around inference that's coming fast. Really encouraged by the study on sparse computation to enhance the computational efficiency of LLMs. It's now possible to use PowerInfer with Llama 2 and Faclon 40B. Mistral-7B support is coming soon!

PowerInfer - a high-speed inference engine for deploying LLMs locally. Just came across this super interesting project on speeding up inference. It's not MoE but it's a simple approach that exploits the high locality in LLM inference to design a GPU-CPU hybrid inference engine. Hot-activated neurons are preloaded onto the GPU for fast access, while cold-activated neurons (the majority) are computed on the CPU. This approach significantly reduces GPU memory demands and CPU-GPU data transfer. It achieves an average token generation rate of 13.20 tokens/s, with a peak of 29.08 tokens/s, across various LLMs on a single NVIDIA RTX 4090 GPU. It's on only 18% lower than that achieved by a top-tier server-grade A100 GPU. It also significantly outperforms llama.cpp by up to 11.69x while retaining model accuracy. There is a lot more innovation around inference that's coming fast. Really encouraged by the study on sparse computation to enhance the computational efficiency of LLMs. It's now possible to use PowerInfer with Llama 2 and Faclon 40B. Mistral-7B support is coming soon!

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J.P. Morgan JUST said that quantum computing is, "a core focus" for them and they are, "investing BILLIONS." If you want to become a multi-billionaire this year, you need to go all-in on quantum right now - there's no waiting. Here's the best quantum stocks to buy: 1. IonQ $IONQ 2. D-Wave Quantum $QBTS 3. Rigetti Computing $RGTI 4. Quantum Computing $QUBT 5. Infleqtion $INFQ 6. Xanadu $XNDU 7. Arqit Quantum $ARQQ 8. Horizon Quantum $HQ 9. X-Energy (honorable mention) $XE 10. ETF's include: $QTUM, $QNTM, and $CHPX All my buy and sell signals in Discord @

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Microsoft and Atom Computing are coming together to ultimately build the world’s most powerful quantum machine. Learn more on how we are advancing the future of quantum computing: #AzureQuantum #MSIgnite

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Bank of America just said that, “quantum could be the biggest revolution for humanity since discovering fire.” If you want to become a billionaire, go all-in on quantum stocks. These are the best ones: 1. IonQ $IONQ 2. Rigetti Computing $RGTI 3. D-Wave Quantum $QBTS 4. Quantum Comptuing $QUBT 5. Xanadu Quantum $XNDU 6. Infleqtion $INFQ 7. Arqit Quantum $ARQQ 8. Horizon Quantum $HQ 9. SEALSQ $LAES All my buy and sell signals in Discord @

Bank of America just said that, “quantum could be the biggest revolution for humanity since discovering fire.” If you want to become a billionaire, go all-in on quantum stocks. These are the best ones: 1. IonQ $IONQ 2. Rigetti Computing $RGTI 3. D-Wave Quantum $QBTS 4. Quantum Comptuing $QUBT 5. Xanadu Quantum $XNDU 6. Infleqtion $INFQ 7. Arqit Quantum $ARQQ 8. Horizon Quantum $HQ 9. SEALSQ $LAES All my buy and sell signals in Discord @

StockWhale

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Palantir CEO Alex Karp on what customers actually want, the real business of frontier labs, and the importance of open source models: “What the technical customers want is control over their compute, their models, their data stack, and their alpha. They want to know they own the means of production, and it's not being transferred to someone else.” "Who owns the data? Are the prompts secure? Is this being transferred to you?" "If it was so valuable, and I can make you a billion dollars, wouldn't I say I'll make you a billion dollars and I want 30%? Why are they charging for tokens if it's so valuable?"

Palantir CEO Alex Karp on what customers actually want, the real business of frontier labs, and the importance of open source models: “What the technical customers want is control over their compute, their models, their data stack, and their alpha. They want to know they own the means of production, and it's not being transferred to someone else.” "Who owns the data? Are the prompts secure? Is this being transferred to you?" "If it was so valuable, and I can make you a billion dollars, wouldn't I say I'll make you a billion dollars and I want 30%? Why are they charging for tokens if it's so valuable?"

Palantir

4,461,436 görüntüleme • 16 gün önce

My 10x stock idea from GTC isn't photonics?! But it does involve lasers. Say hello to $INFQ. It's a newly IPO'd quantum stock generating tens of millions in revenue in space + defense applications with very unique technology. Infleqtion went public last month but it's trading 40% below its IPO price with a sub $2B market cap. $INFQ trades at roughly 70x trailing sales on $29M in revenue. Compare that to $RGTI at $6B market cap on just $7M in revenue, that's 860x sales. I chatted with $INFQ's Chief Administrative Officer, Julie McGee, to dig in further but here's the TLDR. Most quantum companies need to cool their chips to near absolute zero temps just to operate. Infleqtion uses "neutral atom" technology that traps individual atoms inside a glass cell using lasers and runs them at room temperature. It takes the power of a few hairdryers. No giant refrigerators. Way cheaper and way easier to scale. And unlike most quantum names they're actually shipping products NOW. Quantum clocks for GPS-denied navigation, RF sensors, inertial navigation systems. Selling to NASA, the DoD, and the UK government. Their quantum clock is being qualified by SpaceX for satellite systems. Quantum brings a whole new level of precision that works on the ground, in the sky, and underwater. Their technology can enable submarines to navigate without ever linking up to a satellite. GPS jamming is also becoming a huge problem on the battlefield, showing up in Ukraine and Iran. Quantum timing is inherently unjammable and unspoofable. They also had a dedicated spot inside the Nvidia booth at GTC. $INFQ partnered with Nvidia to demo the first commercial materials science application running on logical qubits and are working with Nvidia's NVQLink to scale quantum-classical hybrid computing. What's next: 30 logical qubits targeted this year, one of the most important milestones in the race to fault tolerant quantum computing by 2028. Plus a new NASA contract to measure Earth's gravity from space. Infleqtion combines an attractive valuation with extremely unique technology (they're the only neutral atom quantum company publicly listed). Could easily see this re-rating fast, I just think the IPO timing was poor with Iran. Could be adding this as a lottery ticket to my Asymmetrical Bets portfolio soon... This post was not sponsored or influenced in any way by $INFQ. All thoughts are my own, NFA / DYOR.

My 10x stock idea from GTC isn't photonics?! But it does involve lasers. Say hello to $INFQ. It's a newly IPO'd quantum stock generating tens of millions in revenue in space + defense applications with very unique technology. Infleqtion went public last month but it's trading 40% below its IPO price with a sub $2B market cap. $INFQ trades at roughly 70x trailing sales on $29M in revenue. Compare that to $RGTI at $6B market cap on just $7M in revenue, that's 860x sales. I chatted with $INFQ's Chief Administrative Officer, Julie McGee, to dig in further but here's the TLDR. Most quantum companies need to cool their chips to near absolute zero temps just to operate. Infleqtion uses "neutral atom" technology that traps individual atoms inside a glass cell using lasers and runs them at room temperature. It takes the power of a few hairdryers. No giant refrigerators. Way cheaper and way easier to scale. And unlike most quantum names they're actually shipping products NOW. Quantum clocks for GPS-denied navigation, RF sensors, inertial navigation systems. Selling to NASA, the DoD, and the UK government. Their quantum clock is being qualified by SpaceX for satellite systems. Quantum brings a whole new level of precision that works on the ground, in the sky, and underwater. Their technology can enable submarines to navigate without ever linking up to a satellite. GPS jamming is also becoming a huge problem on the battlefield, showing up in Ukraine and Iran. Quantum timing is inherently unjammable and unspoofable. They also had a dedicated spot inside the Nvidia booth at GTC. $INFQ partnered with Nvidia to demo the first commercial materials science application running on logical qubits and are working with Nvidia's NVQLink to scale quantum-classical hybrid computing. What's next: 30 logical qubits targeted this year, one of the most important milestones in the race to fault tolerant quantum computing by 2028. Plus a new NASA contract to measure Earth's gravity from space. Infleqtion combines an attractive valuation with extremely unique technology (they're the only neutral atom quantum company publicly listed). Could easily see this re-rating fast, I just think the IPO timing was poor with Iran. Could be adding this as a lottery ticket to my Asymmetrical Bets portfolio soon... This post was not sponsored or influenced in any way by $INFQ. All thoughts are my own, NFA / DYOR.

Michael Sikand

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Nvidia announces the new RTX Spark, a new platform powered by the NX1 CPU, and shows off Spark laptops running 007 First Light and Forza 6. The CPU has 20 ARM based cores and a Blackwell RTX GPU with 6144 CUDA Cores. This is the same core count as a 5070, but with 128GB of unified LPDDR5X RAM memory sitting in the same package as the CPU and GPU. The entire Nvidia software stack is available, particularly CUDA, vital for AI. Nvidia's new laptops will likely be ideal for running local LLMs be cause the unified memory means you can load models up to 120-180B parameters (quantized). These laptops are expected to ship later this year and could become strong competitors to high-end MacBooks and even Mac Studios for local AI workloads, thanks to CUDA support and unified memory. Price is unannounced.

Nvidia announces the new RTX Spark, a new platform powered by the NX1 CPU, and shows off Spark laptops running 007 First Light and Forza 6. The CPU has 20 ARM based cores and a Blackwell RTX GPU with 6144 CUDA Cores. This is the same core count as a 5070, but with 128GB of unified LPDDR5X RAM memory sitting in the same package as the CPU and GPU. The entire Nvidia software stack is available, particularly CUDA, vital for AI. Nvidia's new laptops will likely be ideal for running local LLMs be cause the unified memory means you can load models up to 120-180B parameters (quantized). These laptops are expected to ship later this year and could become strong competitors to high-end MacBooks and even Mac Studios for local AI workloads, thanks to CUDA support and unified memory. Price is unannounced.

Grummz

30,573 görüntüleme • 1 ay önce

"All the frontier models are coming on-prem. That's a huge change, folks." — Michael Dell 🇺🇸, CEO at Dell Technologies Technologies OpenAI GPT-5.5. Reflection. Palantir. SpaceX AI. Open source. All landing where the valuable data already lives. For the first time, they don't have to choose between frontier capability and data sovereignty.

"All the frontier models are coming on-prem. That's a huge change, folks." — Michael Dell 🇺🇸, CEO at Dell Technologies Technologies OpenAI GPT-5.5. Reflection. Palantir. SpaceX AI. Open source. All landing where the valuable data already lives. For the first time, they don't have to choose between frontier capability and data sovereignty.

Six Five Media

184,401 görüntüleme • 1 ay önce