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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 views • 1 month ago •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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Jensen Huang says quantum gives AI something it lacks: direct access to nature’s ground truth. Not to replace computing -- but to complete it. A QPU added to a GPU, added to a CPU. Quantum sees. AI understands.

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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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$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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It's not only about how long your context is, but how well you use it. Great to see Gemini 2.5 models dominating MRCR and other benchmarks on long context! See 2.5 Pro tackle a complex coding task by reasoning over an entire repo (>500k tokens). Performance and effective use of the (loooong) context windows are what really matter!

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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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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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Three layers keep DAC running: the Synchro Chain, the Master Chain, and the Shard Chain. Each handles a different job, and together they keep the network fast without trading away security. davide_costa walks through how all three connect. ↓

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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!

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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 @

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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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47,147 views • 1 year ago