Qwen/Qwen3.8-2.4T-A95B

Also of interest: DeepSeek V4-Pro-0813 (1.6T-A49B) benchmark scores have apparently just been announced on the DeepSeek WeChat channel and they're sitting about Fable 5 level.[1]

[1] https://www.reddit.com/r/LocalLLaMA/comments/1vmi0fg/deepsee...

Supposedly this is a Kimi k3 rival. Bit of a chonker, especially since they only released bf16 and fp8. So at launch this will be harder to serve than k3. No QAT on q4 means that someone with deep pockets (nvda?) will have to quant it, with plenty of calibration data. Should bring it ~1.3TB, so around k3 size.

License pretty similar to k3 with some caveats. Free to use for internal or <50M$ revenue / year. Limitations above that threshold for serving the model or services targeting coding / productivity agents.

Benchmarks are looking good, trading blows w/ opus4.8 and sol, generally 10-20p under fable. But that's neither here nor there w/ qwen, their benchmark to real world usage correlation has been iffy in the past.

The local model 3.8-27B announced for Friday, same time so ~48 hours from now. That'll be a bit more exciting for a lot more people, since 3.6 was quite good for local inference, and their 3.7-max -> 3.8-max shows a lot of improvement.

https://unsloth.ai/docs/models/qwen3.8

The 1bit quant model is at an astonishing 397GB with 95B active per MOE. This literally puts Opus 4.5 performance level into a machine a normal person could buy, and still gets usable tokens/second.

The full lossless model BF16 is clocking at 4.9TB. The model card claims the model to be between Opus 4.8 and Fable 5. Again that's astonishing as getting a machine with 7TB RAM (with context + KV cache) is still within the realm of medium size companies.

Bad things: The open source version has its vision capability removed, and the context capped at 250k . I expect someone to bolt a Kimi 2.6 vision tower to it to restore the vision capability (at less performance of course). For context, I played around with extending the context to 600k for Qwen 3.5 397b, and the context remained stable up to around 480k. It'd be interesting to see if the same can be done to Q3.8 .

Also no out of the box DSpark/DFlash support. MTP is present so we should at least get some boost in TP speed.

> In particular, Qwen3.8-Max is the official version based on Qwen3.8-2.4T-A95B with more features, such as vision input & non-thinking support, 1M context length by default, official built-in tools, etc.

That is unfortunate, that the open weight model doesn't have vision support or the 1M context length...

I've been wanting to run open weight models lately to give them a shot with OpenCode. However, I get the impression that models like Qwen and Kimi k3 are impossible to run locally? I have a RTX 5090 and 64 GB of RAM but the models seem to be much larger than that. What's the route to start using these models? Bedrock?

More curious about how qwen3.8-27B performs. That's the size that I can run locally.

Not seeing the upside versus K3 here, especially with the intentional capability loss.

Read the room, Qwen. It's not a good time to hobble your releases.

"QwenSVGBench" elo 1713, pelicanmaxxxing confirmed?

Do we know if AA and DeepSWE benchmarks are on bf16 or fp8 quantisations?

Is this the largest ever open weight model release by parameter count? I think it is.

The card looks almost too good to be true

A ~5TB model.

the a little disappointing part is this is released in BF16. so i suppose no QAT was implemented.