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Coder version with 30t/sec on a Ryzen 3600x with 48GB of RAM with a nvidia 3080.

This is not a very fast desktop. Memory speed is around 2000mhz only. My SSD is some of the worst SSD I've seen and 3080 had its days of glory.

I still have code, chromium, librewolf and many other programs running. I have video streams running while I also watch tv and many times youtube videos.

I use it with the browser that has a great dashboard and with hermes agent and that it really makes this amazing.Only change I made is to set thinking to low.

This is a coding model. Any other task, I still use Ornith 1.5 35B that throws 20t/sec and Laguna.XS-2.0.

by StumpChunkman8 hours ago|

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How much VRAM on your 3080? I've got an early 10gb model. I've been thinking of exploring local coding models, but everyone seems to use much better GPUs than I have access to. Yours is one of the first I've seen with maybe similar hardware on some level.

by roscas8 hours ago|

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Yes, 3080 with 10GB, forgot to mention that.

Mine is at the moment writting some cpp code for some SBOM tests.

I have loads of terminals open. Librewolf, Chromium and you know how this crap likes ram, I have also a vm with 4gb of ram running and doing stuff while I wait for the results but hey, while I wrote this the program is done. Wow! That was 29.x tokens per second most of the time.

Oh I will run some other tests with hermes now because hermes is amazing too.

by thatsabadlook9 hours ago|

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Why is this surprisingly well? It's 2.5x faster than anthropic models, you have data sovereignty, privacy,and that's a strong model. Sounds like a best case scenario to me

by hdjrudni6 hours ago|

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Not sure you understand the term 'surprisingly well'. It means 'better than expected'. I suspect they parent poster didn't actually expect to get >= 100 T/s.

by jacquesm1 hours ago|

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Speed is one thing, accuracy another. Have you benchmarked it against a reference? If so, what were the results? I tend to go for accuracy over speed because usually that means fewer round trips and fewer tokens wasted.

by proc09 hours ago|

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Do you know how it compares to Qwen 3.8 27B? I really want to compare the distilled ones with harness versus the full MoE versions.

by incognito1249 hours ago|

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Qwen 3.8 flash next is way better than 27B. It's so good I dont even use claude anymore

by mickeyp9 hours ago|

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I have not tried Flash Next yet; but 27B is a cracking, little model. It is the first small model that I, as someone with 30 years of experience, can finally say is good enough to hand off small and mid-sized tasks and expect a pretty good result.

It is also a competent tool caller when quantised to NVFP4 for use with ninfer; my own harness only reports the occasional hiccup and it is only because the model will sometimes emit tool calling tokens in its reasoning loop.

by snehesht9 hours ago|

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This is interesting, thanks. - https://github.com/Neroued/ninfer

by roscas5 hours ago|

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I prefer https://ornith.ai/ornith_1_5.html to Qwen 3.8 not only because it is much faster on my hardware but better responses.

But this Qwen 3.8 Flash next coder is amazing running with Strata.

by JokerDan7 hours ago|

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Is this true for 27b Q4_K_XL vs flash next IQ3_S? I thought under Q4 models start quickly degrading?

by gruturo6 hours ago|

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While this is generally true, it's _a little_ less true the larger the model is.

Also, quantization techniques have improved - the I in IQ3 stands for imatrix - Importance Matrix - it is a bit more surgical in what it cuts. The result is a model where the most important weights are even Q6 or above, the least important Q2 or even below, overall it takes the space of a Q3 but with better results.

by Tade03 hours ago|

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To add to the other comment, there's also Ridge quantisation - the majority of weights are indeed Q3_x, but the most sensitive layers are FP8.

by snehesht9 hours ago|

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Yeah I agree, I'm running it with Pi didn't notice much difference compared to lower tier models and the speed, of course.

by nicce9 hours ago|

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I am running 27B with Deepseek Harness these days and somehow just by using it, without any parameter changes, the model feels even more intelligent.

by DoctorOetker5 hours ago|

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do LLMs tend to be homesick when not used in the same harness they sat in during some training phase?

by Bnjoroge2 hours ago|

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iirc there was a sectionin Qwen’s paper where they talked anout how they post-trained flash or 3.8 to work just as well regardless of the harness or eval used. I think that used to be true but not sure if it is any longer

by a11r6 hours ago|

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We recently moved from 27B to Flash Next. The quality is superior for coding. Our workload is primarily well-defined coding tasks that need to be attempted a few times before the model gets it just right. FlashNext is also better at finding issues in generated code than Gemini 3.8 Flash.

by swozey2 hours ago|

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I'm on m1 max 64gb and went from qwen3.8-27B back to qwen3.6-a35b. Is flash next the move? I went from usable say 40tk/s qwen3.6 to unusable, like 11 with 3.8 and not impressed with the replies for the time sacrifice. pi (omp) and omlx but not with the recent 3.8 patch.

I've been waiting for a 35b of 3.8, I don't really know what the other versions are about. I'm on 5g so juggling 40gb of model files sucks. And honestly I'm sick of tweaking this stuff for no, very little, or break-it level improvements. Qwen3.6-a35b has been solid for work, just don't give it freedom to wipe your data.

by ENGNR5 minutes ago|

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Exact same scenario here

I’ve heard a quantised version of flash next can fit in ~50 gb of vram (which needs a system level flag set to go over 48gb)

But the m1 cpu is itself a bottleneck on prefill compared to say an m5, there’s no real getting around it. And the 400mb/s bandwidth starts to hurt without MOE

Hoping these model optimisations can see us through to 2028 because for everything other than LLMs this hardware is still over specced and working incredibly well

by thatsabadlook9 hours ago|

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Significantly better for both performance and real world use case. 3.8 27b is a good small model. This is a good model.

by geye12349 hours ago|

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I find 27B more accurate -- maybe because I'm running at FP8 instead of NVFP4? Flash Next starts making spelling mistakes when I get to 150K context or so. Also it sometimes ignores .md file instructions. Not sure if others have found that.

by thatsabadlook2 hours ago|

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Definitely not.

by PcChip8 hours ago|

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Spelling mistakes?

What inference engine are you using for flash next?

by geye12341 hours ago|

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I'm running Pennyroyal's Docker image (on Podman) which uses sglang. I have a single RTX 6000 Blackwell and 128GB RAM. I turned off disk caching. I'm running with a ~500K context, but have been limiting it to 256K in the client (pi).

It always detects its spelling mistakes, btw, but it worried me. It may turn 'rm -rf ' into 'rm -rf /' one day.

Almost certainly the problem is my config, not the image.

by anon3738398 hours ago|

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Yep, can confirm that is NOT normal. Are you using Nvidia’s NVFP4 quant? There are other NVFP4s floating around but they are not as good. The quality of the calibration data really matters.

Qwen Flash Next is just excellent, all the way to the very end of the native 262k context. (I haven’t tried YaRN scaling to 1M, so I don’t know about that.)

by notnullorvoid7 hours ago|

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Which quantization are you using to reach those numbers?