ColonelPhantom

Isn't Anthropic doing the same, with Opus 5.5 being out while Fable/Mythos is still on 5.1?

Or HOGS? HTMX-OS-Go-Sqlite. While having "OS" in there is kind of redundant, it does make for a nice and general acronym.

Which model class requires an 80 GB VRAM GPU? From my perspective, popular models seem to be either in the ~30B range (Qwen3.6, Gemma 4), while the larger models (MiniMax, MiMo, StepFun, Deepseek) are in the multiple hundreds of billions parameters, for which 80 GB is simply too small.

You can just about reach the lower end of the latter category with a 128GB machine like a DGX Spark, Framework Desktop, or M5 Max, though those are usually not super fast. For the former category, you can easily run them fast with something like a 3090 or 5090, hell, probably even a 5060 Ti.

> the most likely experts

Is that how MoEs work? I though that an important constraint for MoEs is that experts need to be uniformly used to make sure they can be used effectively. If there is a 'common subset' that, if anything, sounds like a symptom of undertraining (i.e. the same trick will not work as well for Deepseek V4.1).

Also, even if your MoE hitrate is 90%, you still spend half your time waiting for the SSD, giving similar total speed to a 27B model!

Finally, it looks like Deepseek V4 is pretty much only runnable with antirez's ds4, and SSD streaming only works with Metal; but I would like to try what you say with llama.cpp which uses mmap to also potentially do SSD streaming. (I can maybe try the large Qwen3.5 MoEs?)

> as context length increases

What kind of context length do you consider reasonable, though? From what I know, all models (even frontier ones) start degrading once you pass a few hundred thousand tokens. So realistically, limiting context size might even improve quality, especially if you use token-efficient harnesses.

> Or you could fit the dense portion of a much more capable model and still take advantage of that hardware.

Your point about consumer hardware was that it would be "borderline unusable" when running Qwen 3.6 27B. However, you need much less hardware to run a 27B than DSv4 Flash. In addition, you can do the same 'trick' with low-end GPUs and small MoEs: my desktop with 32 GB DDR4-3200 and an RTX 2070 8GB can run the ~30B class MoEs at 20-30 tokens per second and similar speeds to my laptop.

Deepseek V4 Flash still has 13B active params though? That is about half as many as Qwen3.6-27B (and much more than Qwen3.6-35B-A3B). Given that RAM (even on a base M4 or 'regular' Intel/AMD system) is like an order of magnitude faster than an SSD, even Qwen 27B running from RAM will be much faster than any Deepseek V4 model with SSD offloading. And the MoE will be much faster still.

Qwen 27B is also small enough to completely fit in a high-end consumer or mid-end pro GPU, like an RTX 5090 or Radeon PRO R9700. I found results claiming 30 tokens per second generation for 27B(-Q4_K_XL) on an R9700. I doubt you get more than 5 tokens per second doing SSD MoE streaming.

Even for relatively short contexts, I honestly already find the ~30B class MoE models to be only borderline acceptable in terms of speed on my laptop (Ryzen 7 7840U, 64 GB LPDDR5-6400), though I use Gemma 4 26B-A4B more than Qwen3.6 35B-A3B.

Carp is memory safe via linear types + references, similar to Rust, so I would not describe it as C-like but rather Rust-like.

But what _is_ a "Text User Interface"? Google Images just returns what is being discussed here: "GUIs" that run in some kind of text mode. And to me, that's also what a TUI is.

A more textually oriented environment (like a normal Unix shell) is, in my experience, usually referred to as a CLI: Command Line Interface.

I did find an interesting hybrid in the Pi coding agent: it seems to leverage the normal terminal scrollback, while still enhancing it with things like transient input fields and status lines, so that it can display those without cluttering scrollback.

You mentioned Strix Halo, which also has off-die memory. Strix Halo does have a real advantage from its wider memory bus (four channels for 256 bit instead of 128 bit), but Strix Point is equivalent-ish to Intel's platforms like Panther Lake or Arrow Lake in terms of memory setup.

In fact, Intel also had Lunar Lake, which had on-package memory. However, it was still limited to 128-bit dual-channel, so there weren't really many performance benefits; it did however help with power efficiency.

Hilariously, those AMD chips are way behind the Intels in terms of memory.

First off, I believe that Intel has its memory far more "unified". AMD typically has a stricter VRAM/RAM 'tradeoff' setting that does not exist on Intel in the same way to my knowledge. (See how on Strix Halo systems, there is a thing about "allocating" 96 GB to the GPU, which seems to be needed sometimes but prevents the CPU from accessing that memory.)

Secondly, the Panther Lake board has LPDDR5X LPCAMM2 memory at 7467 MT/s, while the AMD boards are stuck with DDR5 SODIMMs at a meagre 5600 MT/s. In other words, the Intel board gets a third more memory bandwidth!

Nvidia Turing (RTX 20) definitely marked a major shift IMO.

- It was the first card to enable real-time ray-traced effects. - Mesh shaders are a significant overhaul of the geometry pipeline that's only recently getting real traction. - Its tensor cores enabled a new generation of AI-driven upscaling/antialiasing. DLSS 2, FSR 4 and XeSS are all some variation of "TAA + neural networks", and these all rely on specialized matrix hardware to get optimal performance.

Obviously all of these features are supported across all vendors. Intel Arc Alchemist has all of these features as well, and AMD got RT and mesh shader support with RDNA2 along with slowly building up to tensor cores with RDNA3/4. But Turing clearly debuted these feature which have majorly changed the landscape of realtime 3D graphics.

838 seems to be the real INT8 TOPS number for the 5090; going from 800 to 3400 takes an x2 speedup for sparsity (so skipping ops) and another x2 speedup for FP4 over INT8.

So it's closer to half the speed than a tenth. Intel also seems to be positioning this card against the RTX PRO 4000 Blackwell, not the 5090, and that one gets more like 300 INT8 TOPS. It also has less memory but at a slightly higher bandwidth. The 5090 is much faster and IIRC priced similarly to the PRO 4000, but is also decidedly a consumer product which, especially for Nvidia, comes with limitations (e.g. no server-friendly form factor cards available, and there are or used to be driver license restrictions that prevented using a consumer card in a data center setup).

Aren't Intel Xeon Rapids and Intel Xeon Forest just different target markets? Rapids has fewer but faster cores in general, and more special-purpose accelerators (e.g. AMX, QAT), while Forest is focused on maximum compute density (just pack in as many fast-enough cores as you can).

IIRC Granite Rapids is also not _that_ old, and either current or a single generation behind. (Has its successor landed yet? IIRC GNR is the same generation as Sierra Forest).

Very cool! I am wondering one thing: how fast is it? Much of the "secret sauce" of the Voodoo is its high speed: a first-gen Verite or (God forbid) any ViRGE takes many more cycles for common operations like, say, Z-buffered pixels.

I'm guessing this isn't fully cycle-accurate, but is it at least somewhat "IPC-accurate"? I'm guessing yes? But much of that was also derived from Voodoo's (for the time) crazy high memory bandwidth AFAIK.

GPT-OSS is tailored to be extremely memory efficient. Not only is it natively using the 4.25 bit per token MXFP4 format, but it also uses sliding window attention for half of its layers. It also doesn't have that many layers, only 36 for the 120B version and 24 for the 120B version. (The 120B is also much much sparser than the 20B.)

I found a Reddit comment claiming only 36 KiB per token. With that, half a million tokens fits in 18 GB, which is less than one GPU. And three GPUs fit the parameters with room to spare (64 out of 72 GB).