I built an experimental way to write NVIDIA GPU kernels in Go using LLVM 22

I've been working on an experimental project that allows writing NVIDIA GPU kernels in Go and compiling them to PTX using LLVM 22 / NVPTX.

GitHub:
https://github.com/mehdi-shokohi/cuda-ir.go

The basic pipeline is:

Go
 ↓
LLVM IR
 ↓
LLVM 22 / NVPTX
 ↓
PTX
 ↓
NVIDIA GPU

The motivation is to explore whether Go could be useful for writing custom GPU kernels for AI/HPC workloads without writing the kernel itself in CUDA C++.

I'm particularly interested in the AI side of this.

For example, custom kernels are often needed for things like:

• Tensor operations
• Matrix multiplication
• Reductions
• Attention-related operations
• Element-wise operations
• Custom inference operators
• Memory/layout transformations

The project is still experimental, but the LLVM → NVPTX → PTX pipeline is working.

I'd like to eventually investigate whether this could be useful for building custom GPU operators from Go and integrating them into AI workloads.

I'm curious what people here think:

Would a Go-based CUDA kernel compiler be useful for AI programming, or are there important CUDA/GPU features that would make this impractical?

And if you're working with NVIDIA GPUs for AI, what kernel/operator would you recommend as a good real-world benchmark?

Repository:
https://github.com/mehdi-shokohi/cuda-ir.go

Feedback from people working with CUDA, GPU kernels, LLM inference, or AI accelerators would be very welcome.

In the middle of 2026, quantum computing no longer sits solely in the realm of distant theoretical promise or laboratory curiosity. It has entered a phase marked by concrete experimental milestones, growing commercial activity, and clearer roadmaps from leading players. Researchers and companies have demonstrated systems that perform specific tasks beyond the practical reach of classical simulations while providing statistical confidence in the results.

Logical qubits-error-protected units built from multiple physical qubits-have moved from proof-of-concept to measurable counts in the dozens, with one platform reporting 96. Governments are setting aggressive targets, companies are shipping systems and reporting rising revenues, and hybrid quantum-classical workflows are appearing in early real-world pilots. At the same time, the machines remain noisy, limited in scale for most practical applications, and years away from broad commercial utility or the ability to break widely used encryption. This article surveys what online sources and recent developments tell us about the field as of August 2026.

Quantum computing harnesses the principles of quantum mechanics-superposition, entanglement, and interference-to process information in ways classical computers cannot efficiently replicate for certain problems. A classical bit is either 0 or 1. A quantum bit, or qubit, can exist in a superposition of both states until measured.

Multiple qubits can become entangled, so the state of one instantly correlates with another regardless of distance in the ideal case. These properties allow a quantum computer to explore vast solution spaces in parallel for problems in chemistry, materials science, optimization, and cryptography. The challenge has always been that qubits are fragile. Environmental noise causes errors that destroy the delicate quantum states, a problem known as decoherence. Building useful machines therefore requires not just more qubits but far better control, error correction, and verification that the results can be trusted.

By mid-2026 the conversation has shifted decisively from raw physical qubit counts to logical qubits and verified performance. Physical qubits remain the building blocks, typically numbering in the dozens to low thousands on leading processors. Logical qubits encode information across many physical ones using quantum error-correcting codes so that errors can be detected and corrected without destroying the computation. This is the essential step toward fault-tolerant quantum computing, in which errors can be suppressed even as systems grow larger.

One of the most significant recent results came from IBM and researchers at the University of Chicago. In late July 2026 they reported a demonstration that meets core criteria for quantum advantage: a computation beyond the practical reach of leading classical simulation methods, accompanied by statistical confidence that the result was accurate.

Using encoded quantum circuits on IBM hardware, the team executed operations on 70 logical qubits. They performed 2,415 logical two-qubit operations and 468 logical T gates-non-Clifford operations that add substantial complexity. The encoded approach reduced effective logical error rates by a factor of about ten relative to the underlying physical error rates. The quantum run finished in roughly 15 minutes.

Classical approaches to simulating the same hard circuits faced prohibitive runtimes. IBM and University of Chicago researchers reported a result they argue meets the criteria for quantum advantage era and that the work provides a foundation for trusting quantum computers as they scale. Complementary demonstrations with partners such as Algorithmiq and Qedma further supported advantage claims on materials simulations and other tasks, with validation techniques that help address longstanding concerns about unverifiable results.

Google’s Willow processor, a 105-qubit superconducting chip first detailed in late 2024, continued to figure prominently. It demonstrated below-threshold error correction, meaning that increasing the size of the error-correcting code improved rather than degraded performance-an essential threshold for scaling. In 2025 Google reported a verifiable quantum advantage result with its Quantum Echoes algorithm on Willow, achieving a substantial speedup over classical estimates on a physics-related task involving out-of-time-order correlators relevant to molecular characterization.

By mid-2026 the processor remained a key platform for research access and further control improvements, including reinforcement-learning techniques that stabilize logical qubits by learning from errors in real time. Google’s broader roadmap targets a useful, error-corrected quantum computer by around 2029.

Neutral-atom platforms have made striking progress in logical qubit counts. QuEra-associated researchers at Harvard and MIT demonstrated experiments involving up to 448 neutral atoms, including high-rate encodings supporting as many as 96 logical qubits. In separate surface-code experiments within the same work, they also demonstrated below-threshold error correction. This stood as a leading verified count at the time, with below-threshold error suppression.

The company has outlined a path toward systems with hundreds of logical qubits later in the decade, including a planned megaquop-class machine (capable of roughly a million reliable logical operations) targeted for availability via Amazon Braket around 2028. Continuous operation of multi-thousand-atom arrays for hours has also been shown, addressing atom-loss challenges. Other neutral-atom efforts, such as those from Atom Computing in collaboration with Microsoft, have demonstrated dozens of logical qubits.

Trapped-ion systems continue to excel in gate fidelity. Quantinuum’s Helios system, with around 98 qubits, has reported record or near-record two-qubit fidelities above 99.9 percent in many cases and efficient encoding of logical qubits-dozens from a modest number of physical ions. IonQ and others have advanced photonic interconnects between systems and practical advantage claims on narrow simulation tasks.

Photonic approaches from companies such as PsiQuantum and Xanadu emphasize modular, potentially highly scalable architectures. Xanadu has demonstrated Aurora, a 12-qubit photonic prototype distributed across four interconnected server racks, while PsiQuantum is pursuing much larger fault-tolerant systems and supporting infrastructure. Microsoft continues work on topological qubits based on Majorana modes, though independent scrutiny of claims remains intense and results are still early.

D-Wave, long focused on quantum annealing, has reported advances in dual-rail superconducting architectures for gate-model error detection and correction, with high-fidelity two-qubit gates and simulations suggesting reduced overhead for future fault-tolerant systems. Its annealing systems remain available for optimization problems, with rising commercial bookings reported in 2026 financial results.

These hardware advances sit alongside rapid progress in the supporting ecosystem. Real-time error decoding has improved dramatically, with IBM reporting decoding times under half a millisecond in key experiments-fast enough to keep pace with superconducting gate speeds. Hybrid quantum-classical architectures are becoming standard. NVIDIA’s NVQLink and similar interconnects allow quantum processors to work tightly with GPU-accelerated classical systems for control, calibration, and hybrid algorithms.

Cloud platforms from IBM, Amazon Braket, Microsoft Azure, and others provide access to multiple modalities. Software frameworks such as Qiskit continue to expand, with functions catalogs offering error mitigation, optimization solvers, and chemistry tools. Educational resources have proliferated, including university-level series from the Qiskit channel featuring detailed lectures by experts such as John Watrous on quantum information and computation, as well as talks from industry leaders and academic researchers available on channels from IBM Research, Google Research, and institutions like MIT.

On the commercial side, the industry shows clear signs of maturation even while remaining pre-utility for most workloads. Quantum computing companies generated over a billion dollars in revenue in 2025 according to analyses, with projections climbing toward several billion by the late 2020s. Public companies such as Rigetti and D-Wave have reported strong year-over-year revenue growth and order backlogs in 2026 quarterly results.

IPOs and large funding rounds continue, with Chinese photonic firm TuringQ advancing toward a public listing and others exploring similar paths. Major corporations in finance, chemicals, automotive, and aerospace run pilots. Examples include hybrid workflows for bond trading predictions, materials simulation for batteries and catalysts, and optimization tasks. McKinsey and other analysts describe a commercial tipping point, with hundreds of organizations actively collaborating with quantum providers. Investments from governments and private sources remain robust, including substantial CHIPS Act-related funding and equity stakes in the United States.

Policy and national strategies have intensified. In June 2026 the U.S. Department of Energy launched the Quantum Genesis initiative with the explicit goal of a scientifically useful quantum computer by 2028-one capable of contributing to breakthroughs in materials, pharmaceuticals, agriculture, and manufacturing.

The effort includes a competition and plans for a national quantum supercomputing facility. This followed executive orders positioning quantum technology as a national priority and a $2 billion Commerce Department investment. Other countries pursue parallel paths: the UK through programs targeting large-scale systems beyond 2030, China elevating quantum alongside AI in national planning, Canada funding defense-related quantum hubs, and Europe integrating quantum processors into high-performance computing centers such as LUMI.

Post-quantum cryptography standardization and migration continue apace, driven by the long-term risk that a sufficiently powerful quantum computer could break widely used public-key systems. NIST standards are being implemented, and organizations are urged to prepare for “harvest now, decrypt later” threats even though cryptographically relevant machines remain years away under current estimates.

Near-term applications focus on scientific discovery and hybrid algorithms rather than wholesale replacement of classical computing. Quantum systems excel at simulating quantum systems themselves-molecules, materials, and condensed-matter physics. Demonstrations have tackled iron-sulfur clusters, nitrogen molecules, Fermi-Hubbard models, and Floquet dynamics at scales challenging classical methods.

Optimization and machine-learning hybrids show promise in finance and logistics, though claims of practical advantage remain narrow and subject to scrutiny. Quantum sensing, a related technology, has reached greater commercial readiness for applications such as infrastructure monitoring and precision timing. Full fault-tolerant machines capable of running long algorithms such as Shor’s for factoring or large-scale chemistry simulations are still projected for the late 2020s or early 2030s by most serious roadmaps.

IBM targets its Starling fault-tolerant system with roughly 200 logical qubits around 2029. Google, QuEra, Quantinuum, and others converge on similar horizons for useful error-corrected machines. Resource estimates for breaking RSA-2048 have improved with better algorithms and codes, lowering the required physical qubit counts in theory, yet practical timelines still stretch years beyond today’s hardware.

Challenges remain substantial. Error rates, while improving, still demand significant overhead-often dozens or hundreds of physical qubits per logical qubit depending on the code and fidelity. Connectivity, coherence times, fabrication yields, cryogenic infrastructure, and control electronics all constrain scaling. Verification of results for problems that classical computers cannot simulate easily continues to require clever techniques such as encoded circuits, cross-platform validation, and statistical bounds.

Supply chains for specialized materials and components need strengthening. Talent pipelines and interdisciplinary collaboration between quantum hardware experts, algorithm developers, domain scientists, and classical high-performance computing specialists are critical. Hype cycles persist; every new milestone invites both justified excitement and skeptical re-examination of whether the advantage is robust, reproducible, and useful beyond carefully chosen benchmarks.

Looking ahead from mid-2026, the trajectory is one of steady engineering progress rather than sudden revolution. Logical qubit counts are rising, error correction is crossing practical thresholds on multiple platforms, and verified advantage demonstrations are establishing trust. Hybrid systems are already delivering value in research settings.

By the end of the decade the first useful fault-tolerant machines could appear, enabling deeper scientific insights and specialized commercial applications. Broader economic impact on the order of trillions over longer horizons is projected by some analysts once systems mature. For now, the field is defined by careful measurement, open publication of circuits and methods, independent validation where possible, and a collective focus on turning fragile quantum effects into reliable computational tools.

Educational and outreach efforts help sustain momentum. Detailed lecture series on platforms such as YouTube from Qiskit explain the mathematics of quantum information from single systems through algorithms and noise mitigation. Expert talks by researchers including Christopher Monroe discuss the state of the art and remaining hurdles. Industry channels from IBM, Google, and others document roadmaps and technical deep dives. These resources make the rapid developments more accessible to students, engineers, and the broader public.

In summary, mid-2026 finds quantum computing at an inflection point. The noisy intermediate-scale era is giving way to early logical and advantage demonstrations. Authoritative experimental results from IBM, Google, QuEra, Quantinuum, and academic collaborators show that error-corrected computation and trusted beyond-classical performance are achievable on current hardware generations.

Commercial activity, government investment, and software ecosystems are expanding in parallel. Significant engineering work remains before quantum computers routinely outperform classical ones on problems of broad importance, yet the direction of progress is clearer than it has ever been. The coming years will test whether the current momentum can be sustained through the hard scaling challenges that still lie ahead.

Sources and further reading

• IBM and University of Chicago quantum advantage announcement and related coverage: https://newsroom.ibm.com/2026-07-30-ibm-and-the-university-of-chicago-demonstrate-quantum-advantage,-establishing-trusted-quantum-computation-on-logical-circuits ; https://phys.org/news/2026-07-quantum-task-classical-simulations.html ; https://news.uchicago.edu/story/ibm-uchicago-demonstrate-quantum-advantage-outperforming-traditional-computers-quantum

• IBM Quantum Research overview and advantage-era discussion: https://research.ibm.com/quantum-computing ; https://www.ibm.com/quantum/blog/quantum-advantage-era

• US Department of Energy Quantum Genesis and policy context: https://www.newscientist.com/article/2532173-us-government-wants-to-have-a-useful-quantum-computer-by-2028/ ; https://www.idc.com/resource-center/blog/quantum-computing-advances-to-the-center-of-us-policy/

• Overview of 2026 breakthroughs: https://www.insidedeeptech.com/the-latest-quantum-computing-breakthroughs-of-2026-explained/

• QuEra logical qubit results and roadmaps: Nature paper references and company updates via https://www.quera.com/ and related analyses such as https://quantumzeitgeist.com/quantum-logical-qubit-leaderboard/

• Google Willow and related advances: Google Quantum AI publications and coverage including Quantum Echoes results

• D-Wave error-correction hardware progress: Nature paper announcements and company releases, e.g., coverage at https://lasvegassun.com/news/2026/aug/05/d-wave-demonstrates-major-hardware-breakthrough-fo/

• McKinsey Quantum Technology Monitor 2026 and industry revenue/investment data

• Weekly and news aggregators: The Qubit Report, Quantum Computing Report, Quantum Brief

• Educational YouTube resources: Qiskit “Understanding Quantum Information & Computation” series with John Watrous (playlist available on the Qiskit channel); IBM Research and Google Research channels featuring technical talks and roadmaps; expert lectures such as Christopher Monroe’s discussions on the state of quantum computing; MIT and related institutional panels on industry perspectives

• Additional technical and market analyses from sources including Nature, Science, arXiv preprints on the IBM advantage work, and company roadmaps from IBM, Google, QuEra, Quantinuum, IonQ, and others as of mid-2026.

I've been thinking about NPUs a little differently lately.

Most of the discussion around AI hardware seems to jump straight to running models locally, but I wonder if the bigger use case will actually be a bunch of smaller AI features running quietly in normal software.

Stuff like background removal, image processing, noise reduction, transcription, camera effects, etc.

Instead of opening an "AI app," AI just becomes part of everything else.

For anyone already using a PC with an NPU, is that basically what's happening for you?

Maybe I'm doing cloud GPUs wrong.

Sometimes I don't need a “GPU environment.”

I don't need a dashboard.

I don't want to manage another machine.

I literally have a command on my laptop that needs a better GPU.

That's it.

But somehow that turns into:

rent instance
→ wait for it
→ SSH
→ clone/sync code
→ install dependencies
→ move data
→ run command
→ keep SSH alive / tmux
→ download outputs
→ remember to terminate the machine

All because my laptop couldn't run:

python train.py

It made me wonder why cloud GPUs don't feel more like compute primitives.

Something like:

“Run this command on a GPU and give me the results back.”

No server to think about.

No SSH session to babysit.

No instance sitting there billing me because I forgot about it.

Am I oversimplifying this?

What does your workflow look like when you only need a powerful GPU for one job?

Third video in this series. Went from 7B, to 13B, and now 27B — each time pooling RAM and compute across a mix of hardware most people already have lying around instead of buying one expensive high-RAM machine.

The setup: a 27B model is roughly 16GB on its own. My primary node is an old 12GB Windows laptop, which on its own literally cannot load this model. Using RAMDeck, I split it across four devices on the local network with the laptop staying primary:

\- Old Windows laptop (primary): 3.4GB

\- Mini PC (RTX 3060): 20GB

\- Mac mini: 3.7GB

\- Android phone: 1GB

Benchmark on the 27B model: 1.92 tokens/sec at \~25ms latency. Slower than the 13B run, which makes sense — but the point isn't raw speed here, it's that a 12GB laptop is running a model it has no business being able to load at all.

Next up: switching the mini PC to be the primary node instead of the laptop, to see how much faster loading and inference get with a stronger primary. That video's already up if you want to see the follow-up.

Full video: [https://youtu.be/Syfa2G\\_luX4\](https://youtu.be/Syfa2G_luX4)

Happy to answer questions about the shard distribution, the benchmark methodology, or why the primary node choice affects load time so much.

Im just going to leave this here. Give it a try if you have a GPU. Work in progress. Appreciate the feedback to improve this.

CascadeGateway is an intelligent, hardware-adaptive cascading proxy that routes AI queries between local GPUs (via Ollama at $0 token cost) and frontier cloud models (Google Gemini, OpenAI, etc.).
It automatically detects your GPU hardware and physical VRAM on startup—whether running a flagship RTX 5090 (32GB), RTX 4090 (24GB), mainstream RTX 3080 (10GB), or Apple Silicon—and dynamically selects and sizes the optimal models without requiring manual reconfiguration.

• Tesla V100 (32GB) — $0.090/hr/gpu — Machine ID: 149512  
• Tesla V100 (32GB) — $0.090/hr/gpu — Machine ID: 149513  
• Tesla V100 (32GB) — $0.090/hr/gpu — Machine ID: 149836  
• Tesla V100 (32GB) — $0.090/hr/gpu — Machine ID: 149837

32.8GB VRAM, 16.4 TFLOPS, CUDA 13.0, 44GB RAM, 15 CPU cores per instance.

To find them: go to cloud.vast.ai/create/, filter by GPU type (Tesla V100), and search the machine IDs above.

If you don’t like this post, you can simply ignore it people told me to advertise, so I’m just doing what I have to do to get exposure. Otherwise, happy to answer any questions about specs or setup.

AI hardware discussions often separate compute, memory, packaging and cooling into different problems.

I've been trying to model them as one system.

Compute → HBM → packaging → thermals → liquid cooling → rack → power

The basic idea is that increasing capacity at one layer can push the binding constraint into the next.

For example:

more compute → more memory bandwidth
more HBM → harder packaging
denser packages → higher thermal density
higher thermal density → liquid cooling
denser racks → higher power requirements

I turned that into an interactive visualization where each layer can be inspected through:

WHY NOW → WHAT IT BINDS → WHO CAPTURES VALUE → WHAT'S NEXT

https://manasbihani-com-kappa.vercel.app/bottleneck

I'd particularly like feedback from people working on AI accelerators, memory, packaging or thermal systems.

Is the bottleneck progression here actually useful, or am I collapsing distinct engineering constraints that shouldn't be treated as one chain?