Open-model news

Alibaba's Damo Academy and Zhejiang University's First Affiliated Hospital published RADAR in Science on 17 September: one model that reads a contrast-enhanced abdominal CT scan and reports 146 imaging findings across 18 anatomical structures, trained on more than 400,000 examinations — and in the study's reader tests, its assistance raised the detection sensitivity of 26 radiologists by about a tenth. The paper's own conclusion is the headline: generalist AI can now match human experts in routine and complicated radiology alike.

The code is Apache 2.0 and the weights are downloadable; the licence on the weights is non-commercial, so treat this as the direction of travel — research and evaluation today, and a licence worth watching for a commercial successor.

For a household as much as for a hospital, the scan is the paper that must not go to a cloud API — and an open model can now read it at expert level on a machine its owner controls.

PrismML rebuilt Qwen3.8-27B — a model this site runs — in ternary weights at 1.76 bits per weight, and the result is a 27B-class model in a file of about six gigabytes, running on a laptop or a single graphics card. The maker's own benchmarks put it at 98% of the full-precision model's average — reasoning, coding, vision and tool calling intact — in a file more than nine times smaller; it still reads images, holds a 262k-token window, and ran at 143 tokens a second on an RTX 5090.

It is Apache 2.0, and it went to the top of Hugging Face's trending list: 1.9 million downloads in its first four days. One catch before you try it: the files need PrismML's own build of llama.cpp today, and stock runtimes cannot open them yet.

The floor for a serious model just dropped — a household or a small business that was told a 27B needed the big machine can run one on the machine it already has.

Through September NVIDIA published official NVFP4 builds of Qwen3.8-Flash-Next and GLM-5.3-Flash (2 September), Qwen3.8-27B (4 September), GLM-5.3 (14 September) and DeepSeek-V4.1-Flash (16 September).

A four-bit build is about a third of the memory the full-precision weights need — the difference between a datacentre card and the one in the machine you already own — and it is the configuration this site quotes.

Take the vendor's own build over a community conversion: the calibration, the recipe and the licence travel with it.

One of the two models this site standardises on is now downloadable and free for commercial use: 763.2B parameters on disk — a 552B backbone plus 196B of Engram memory, with about 15B of vision encoder and auxiliary tensors DeepSeek does not itemise — activating 8B per token on the way in and 16B on the way out, so it runs far lighter than its size suggests. The cache is the real change: 890 bytes per token globally, about a quarter of the memory the previous generation needed, which is what makes a one-million-token window affordable on owned hardware.

It reads images, which the model it replaces did not.

DeepSeek is retiring V4-Pro, routing its traffic here until V4.1-Pro launches — and no V4.1-Pro is released yet.

The hub where nearly every open weight is published is changing hands.

NVIDIA's commitments are on the record: the platform stays open, NVIDIA hardware will not be required to build on or deploy through it, and multi-cloud, multi-accelerator support continues.

Nothing changes for a business running models today — but the shelf everything sits on now belongs to the vendor selling the hardware, and those promises are what to hold it to.

125B on the label, 176B once the separate n-gram embedding table is counted, and 180B packaged with the 4B MTP head — the model card's own numbers, with only 6B working per token — so it is far faster than its size suggests, and it reads text, images and video. Native context is 262k; the 1M figure needs manual configuration.

Read the licence before planning on it: the Qwen Community Licence 1.0 requires a separate agreement from Qwen for anyone running a "Model as a Service or AI Work Assistant business", with no revenue or user threshold. Internal use is explicitly exempt.

Our everyday models are Apache 2.0 and unaffected.

Z.AI shipped GLM-5.3 Flash on the same date as Qwen's release.

Two open releases in one day is now ordinary.

The question worth asking of each is not which scores higher but which one fits a machine you can afford to keep running.

A 22-billion-parameter open model that generates picture and audio together rather than separately, six to twenty seconds, up to 4K.

Worth knowing about and worth being honest about: video is still the most expensive thing to run locally, it needs the larger machine, and for most small businesses it is not a job at all.

The runtime behind most single-machine setups was in the b10600s by 28 August, released as sequential build tags rather than versions.

Good news for support of new models.

Also a reason to pin a build and test before moving, rather than tracking the tip on a machine somebody depends on.

A 32 GB DDR5-6000 kit is around $392, against $110–140 in late 2025. Reported 64 GB prices differ by source — roughly $850 to $1,118 — and we quote the range rather than pick one.

The cause is memory makers moving wafer capacity to HBM for AI datacentres, where a gigabyte costs about three times the wafer of ordinary DDR5.

This changes the price of every machine we quote, and it may last into 2027.

The best picture quality per gigabyte you can self-host, and MIT means no revenue threshold and no restriction on selling what it makes.

Eight billion parameters, so it fits the everyday machine rather than needing the large one.

Apache 2.0, 4.1 billion parameters, about 3 GB of memory. Nine languages, and it clones a voice from three to five seconds of audio.

Human listeners preferred it to ElevenLabs Flash v2.5 in 68 per cent of blind comparisons.

A European lab, a permissive licence, and it runs on your own machine.

Apache 2.0, 80 billion parameters but only 3 billion working per token, so it runs far lighter than its size suggests.

A 262,000-token window, which is enough to hold a real codebase.

Around 70 per cent on SWE-bench — the practical choice if you want a coding agent that never sends your source anywhere.

Apache 2.0 on the code and the weights, which is rare here. It writes clearer sung lyrics than anything else measured, including the paid services, and places second overall in listening tests behind Suno.

The trade is speed: it generates at about real time, so a four-minute track takes about four minutes.

Around 4.8 GB has to stay in memory once the audio codec is counted.

Fast enough to change the job — a four-minute track with vocals in under ten seconds on a mid-range card, under 4 GB for the smaller version. We are not stocking it.

The code is MIT but the weight files carry no stated licence at all, which is not the same as permissive, and several guides report the project as Apache 2.0 when it is not.

Until that is fixed you cannot safely sell what it makes.

Used RTX 4090 cards are trading around $2,100, up from $1,200–1,400 earlier in the year: production ended in 2024 and demand keeps absorbing what is left.

If you were planning to save money with a used card, price a new one before you decide.