Is this a derived from flash next? That's the impression I get from reading over everything. Using Strata + Qwen 3.8 Flash Next now and I am beyond impressed. It's basically as capable as 5.6 Luna in my hands so far. If this is an upgrade I can't wait to try it out.
I assume so - Qwen/Qwen3.8-Flash-Next is marked as the "Base Model" on the huggingface page and states it may be subject to "Qwen's separate authorization or attribution requirements"
And I assume the metrics comparing qwen are using a quantized version - from the blog they compare memory use against qwen's "standard 4-bit deployment". Which might explain the improved performance over that base model they show - it might lose less from quantization.
It's interesting. It does seem to be based on QFN, but they've modified the architecture. 10B active versus QFN's 6B, plus also QFN is 180B parameters, not 145B. But it has the same 51B n-gram table.
Also, why ship both a 145B and a 120B size? That's basically the same size class.
3.8F/N is amazing. Truly useful, runnable local AI on machines that are not ridiculously expensive. (I’m running it on 2x 3090 and 128gb of ram, on the quantum roughly equivalent to 5-K-M. fast, but you can run it on much less
This looks like it would actually work on my 64mb M1 MBP. That would be fantastic.
Interesting, if 3rd party testing and benchmarking hold the published values this is very exciting. I am a bit skeptical given the model size, but I hope it turns out to be true!
I'm really impressed with the Blender examples. It's amazing how far "tool use" has come, when it now means using a tool as complicated as Blender to perform work that would take a human days to complete. I'm a little skeptical the actual models are workable, in the sense that a human could come in after and manually refine and apply better textures, etc. Historically, agent-made designs are messy as hell, and by the time you clean them up, you haven't saved any time. But, that's begun to get better; the best models are now very good at making clean shape-based SVGs, for example, that are no messier than what a human would make in Inkscape or Illustrator. But, I'll definitely give it a try.
Do they say how they interact with Blender? Launched within a python interpreter everything in Blender is accessible as a method call. I hope they didn’t do anything more complex than that.
TerminalBench scores often correspond to real world performance for coding agents that rely heavily on bash.
This score is roughly on par with 5.6 Terra max, which in my direct experience is perfectly capable of driving a cloud agent harness that can do pretty complex knowledge work.
Thy have shipped this as NVFP4 so its Nvidia blackwell only, presumably their inference engine doesn't support anything outside CUDA 13 specified either. Will be interesting to see if this gains some other quants and other inference engines pick it up and we get some standard GGUF files, its a bit of weird release at the moment rather than GGUF file downloads the variants link on HF is leading to a directory of stuff which contains the model spread across a variety of files.
It is strange that they also benchmarked an M2 Ultra, but they are specifically not releasing a Mac runtime today. Maybe that is still under development.
> Sharing matrices substantially reduces the weights that must be stored and moved while keeping the expert count unchanged. Each group of 64 experts shares two large matrices, while each expert retains a small set of parameters that captures its differences.
145B parameters with only peak 29GB memory usage - really impressive, truly something you can actually run on a decent spec MBP
Harness improvements like context pruning, malformed tool call healing also very helpful to boost smaller local models. Doing alot of work in this area lately.
Qwen 27B in Terminal Bench 2.1 73->85 for instance.
dyyd3333 | 3 hours ago
mortalapeman | 3 hours ago
kimixa | 2 hours ago
And I assume the metrics comparing qwen are using a quantized version - from the blog they compare memory use against qwen's "standard 4-bit deployment". Which might explain the improved performance over that base model they show - it might lose less from quantization.
https://huggingface.co/Edge0/Edge0-145B-NVFP4
anon373839 | 2 hours ago
Also, why ship both a 145B and a 120B size? That's basically the same size class.
[OP] sroussey | 8 minutes ago
K0balt | 2 hours ago
This looks like it would actually work on my 64mb M1 MBP. That would be fantastic.
Zetaphor | 3 hours ago
hedora | 2 hours ago
fesoliveira | 3 hours ago
SwellJoe | 3 hours ago
jazzyjackson | an hour ago
kennywinker | 23 minutes ago
rybosome | 3 hours ago
This score is roughly on par with 5.6 Terra max, which in my direct experience is perfectly capable of driving a cloud agent harness that can do pretty complex knowledge work.
Definitely worth an evaluation!
paulhart | 2 hours ago
PaulKeeble | 2 hours ago
coder543 | an hour ago
It is strange that they also benchmarked an M2 Ultra, but they are specifically not releasing a Mac runtime today. Maybe that is still under development.
suthakamal | 2 hours ago
1612elphi | 2 hours ago
mikeshi42 | 2 hours ago
145B parameters with only peak 29GB memory usage - really impressive, truly something you can actually run on a decent spec MBP
devmor | 2 hours ago
Damn, guess I can't run this on my desktop. /s
quicklywilliam | an hour ago
nightdeployboy | 29 minutes ago
dpweb | 15 minutes ago
[OP] sroussey | 11 minutes ago
Centigonal | 13 minutes ago