The memory bandwith on M4 Max is 546 GB/s, M5 Max is 614GB/s, so not a huge jump.
The new tensor cores, sorry, "Neural Accelerator" only really help with prompt preprocessing aka prefill, and not with token generation. Token generation is memory bound.
Hopefully the Ultra version (if it exists) has a bigger jump in memory bandwidth and maximum RAM.
Related to this, how do you get your comments that you add in the review back into your agent (Claude Code, Cursor, Codex etc.)? Everybody talks about AI doing the code review, but I want a solution for the inverse - I review AI code and it should then go away and fix all the comments, and then update the PR.
I think the ability to actually run the code on the target helped a lot with understanding and optimizing for the specific micro architecture. Quite a few of the ideas turned out to not to be optimal and were discarded.
Also important to have a few test cases the agent can quickly check against, it will often generate wrong code, but if that is easily detectable the agent can fix it and continue quickly.
I used Claude Opus 4.5 inside Cursor to write RISC-V Vector/SIMD code. Specifically Depthwise Convolution and normal Convolution layers for a CNN.
I started out by letting it write a naive C version without intrinsic, and validated it against the PyTorch version.
Then I asked it (and two other models, Gemini 3.0 and GPT 5.1) to come up with some ideas on how to make it faster using SIMD vector instructions and write those down as markdown files.
Finally, I started the agent loop by giving Cursor those three markdown files, the naive C code and some more information on how to compile the code, and also an SSH command where it can upload the program and test it.
It then tested a few different variants, ran it on the target (RISC-V SBC, OrangePI RV2) to check if it improves runtime, and then continue from there. It did this 10 times, until it arrived at the final version.
The final code is very readable, and faster than any other library or compiler that I have found so far. I think the clear guardrails (output has to match exactly the reference output from PyTorch, performance must be better than before) makes this work very well.
I have the PCIe version of NanoKVM, and I am also happy with it.
The big advantage of the PCIe version is that it does not take up space on the desk and all the cables for ATX power control an inside the PC case.
Full-sized HDMI is nice, the only limitation here is 1080p resolution. 1440p or higher would allow mirroring the output on the main monitor to the NanoKVM, but this probably a weird use-case anyway.
The VPN product is very good, it's basically a thin wrapper around Mullvad, arguably the best VPN on the planet right now. At least from a privacy standpoint.
There are quite a few well funded companies now that use forks of VSCode: Google (IDX, now Firebase Studio), Cursor, Windsurf (especially if they are bought by OpenAI soon), GitLab etc.
An alternative marketplace already exists with open-vsx.org, but these companies need to fund the creation of alternatives for the proprietary extensions (C#, C++, Python, maybe others) that can be used by all forks.
Not fully confirmed yet, but that sounds really bad. It seems like it also hits low power models like the 13900T, which would imply this isn't just a voltage issue from auto overclocking.
The best way to swap desktops is to install the correct Ubuntu flavour from the start, i.e. Kubuntu, Xubuntu or Lubuntu to avoid those problems.
I am quite happy with Regolith Desktop [0] installed on top of standard Ubuntu. Gives you a nice tilling window manager based desktop without fiddling with config files for hours.
I wonder if they have a clear hardware separation between each of the API, ChatGPT, their lower-scale experiments and their large scale (e.g. GPT5) training hardware. Or is everything just a big blob of hardware, that dynamically gets allocated to jobs depending on demand?
Hardware demand is so high, having GPUs idling is a massive waste, but you also want to have separation between dev, test and prod environments, so not obvious what to do.
Consumer AMD chips to be precise. AMD is mostly focusing on their datacenter chips (MI300X), and I assume the support for AI workloads there is much better. They might even see their consumer chip undermining sales of datacenter chips.
NVIDIA got big because CUDA works on the most crappy notebook GPUs up to their most powerful chips, and AMD should do the same, but focusing their limited number of driver devs on the expensive enterprise hardware makes sense IMHO.
I wouldn't be surprised if the answer is "all of the above" and more. Not only is there limited availability of NVIDIA chips, it's also useful to tell Jensen that you actually could buy from somewhere else, even if you don't really want to.
At the end of the day, the bottleneck is TSMC, on which all GPUs are produced right now, even the ones from Intel.
The recent blog post from Google announcing TurboQuant does not change anything regarding RAM planning for the big labs.
TurboQuant itself is already a year old! So even smaller labs have probably seen and implemented it.