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Qwen3.8-Flash-Next

qwen.ai

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Re: Qwen3.8-Flash-Next

#121

Post by switchbak »

raducu wrote: post #80
redrix wrote: post #53

Earlier quoted context omitted.

You’re absolutely right to be hopeful. Three honest possibilities, and I’ll be straight with you about each: 1. It overthinks — Just like the previous iteration. High confidence. 2. It doesn’t overthink — Improvement from the last model for your use case. Regression for others. 3. It sometimes overthinks — Best case all around. A feature, not an impairment. One final thing worth mentioning: (I made myself irrationall…

On one hand I love your joke, on the other, this is HN not reddit and I usually downvote such responses, not sure what is the HN etiquette for such humor?

More than 2 levels and out come my downvotes. Or if it's just knee jerk with zero humour. But I probably violate my own rules ... which is to be expected.

Re: Qwen3.8-Flash-Next

#122

Post by lxkarthi »

rohansood15 wrote: post #96
anon373839 wrote: post #61

Does anyone have an idea how this might perform on a DGX Spark at longer contexts? I've been trying to investigate their performance with these medium-sized MoE models, but I'm seeing a lot of incomplete and conflicting information. The 273 GB/s bandwidth looks awfully bad on paper...

Given the new architecture, speeds are harder to estimate. Max would be ~40 tok/s.

I can run Deepseek flash 0731 version (ds4, esl3) on single DGX spark. getting around ~20 tok/s. It's great. Quantized version of this model would probably run on the DGX spark. I am excited to wait for quantized models that fits in single DGX spark.

Re: Qwen3.8-Flash-Next

#124

Post by mistersquid »

redrix wrote: post #53
lnenad wrote: post #21

Adding to my homelab stack, hopefully it doesn't overthink like the little model. Actually, hoping it thinks a bit less. Wait actually I'm really praying it reasons a bit more directly. But wait, I'm really sure that it must be a bit better.

You’re absolutely right to be hopeful. Three honest possibilities, and I’ll be straight with you about each: 1. It overthinks — Just like the previous iteration. High confidence. 2. It doesn’t overthink — Improvement from the last model for your use case. Regression for others. 3. It sometimes overthinks — Best case all around. A feature, not an impairment. One final thing worth mentioning: (I made myself irrationall…

> You’re absolutely right to be hopeful. Three honest possibilities, and I’ll be straight with you about each:

> [UGC styled humorously as LLMisms]

All joking aside, having interacted with Claude intensely for the last 8 months and about 30 hours/week in the last 3, I’ve started to notice how (for want of a better word) “readable” (“digestible” ? “comprehensible” ? “Predictable” is the wrong direction.) information chunked into LLM-shaped pieces are for me.

I can digest LLM-shaped pieces of data very easily probably because I’ve been spending too much time with Claude, sure.

But the other side of this is that the entire human species (using LLMs) is similarly being trained to digest interrelated pieces of information/data in these specific shapes, akin to how philosophical assertions can be formulated as a syllogism and, thus, become more readily understood because of familiar epistemological cadence and shape.

Many people reject such copy/prose/data because they detect AI-generated-so-not-worth-human-attention, but I do wonder if this is preparing many millions of loosely (and tightly) associated humans and their organizations to quickly exchange and digest information.

This is not to say current LLMisms are the end, only that such detectable patterns in information delivery will make comprehension and communication more efficient (as well as more limited precisely because of such structure).

/philosophical musings about the epistemological implications of LLM-shaped conversation tics

Re: Qwen3.8-Flash-Next

#126

Post by simonw »

I ran some pelicans at the four different reasoning levels (none, low, medium, xhigh - apparently high and xhigh are aliases of each other) on a DGX Spark using Unsloth's unsloth/Qwen3.8-Flash-Next-GGUF (UD-IQ1_S):

https://tools.simonwillison.net/markdown-svg-renderer#url=ht...

Surprised I didn't get one I liked as much as the Qwen 3.8 27B one https://simonwillison.net/2026/Aug/16/qwen-38-27b/#the-defau... , maybe because of quantization.

Re: Qwen3.8-Flash-Next

#127

Post by kzrdude »

simonw wrote: post #126

I ran some pelicans at the four different reasoning levels (none, low, medium, xhigh - apparently high and xhigh are aliases of each other) on a DGX Spark using Unsloth's unsloth/Qwen3.8-Flash-Next-GGUF (UD-IQ1_S): https://tools.simonwillison.net/markdown-svg-renderer#url=ht... Surprised I didn't get one I liked as much as the Qwen 3.8 27B one https://simonwillison.net/2026/Aug/16/qwen-38-27b/#the-defau... , maybe be…

If I read correctly, that's based on a 1-bit quantization, and can we really expect that to produce any useful output at all?

Re: Qwen3.8-Flash-Next

#128

Post by Bluestein »

hedgehog wrote: post #123
amoss wrote: post #99

Earlier quoted context omitted.

But the reason why it remains load bearing is key.

You've made a really sharp observation, and the reason it lands is worth naming:

"... worth naming: ..."

  ⎿  You've hit your session limit · resets 2:50am (123°24′W Etc/GMT+8)
  /upgrade to increase your usage limit.

Re: Qwen3.8-Flash-Next

#129

Post by powersurge360 »

mistersquid wrote: post #124
redrix wrote: post #53

Earlier quoted context omitted.

You’re absolutely right to be hopeful. Three honest possibilities, and I’ll be straight with you about each: 1. It overthinks — Just like the previous iteration. High confidence. 2. It doesn’t overthink — Improvement from the last model for your use case. Regression for others. 3. It sometimes overthinks — Best case all around. A feature, not an impairment. One final thing worth mentioning: (I made myself irrationall…

> You’re absolutely right to be hopeful. Three honest possibilities, and I’ll be straight with you about each: > [UGC styled humorously as LLMisms] All joking aside, having interacted with Claude intensely for the last 8 months and about 30 hours/week in the last 3, I’ve started to notice how (for want of a better word) “readable” (“digestible” ? “comprehensible” ? “Predictable” is the wrong direction.) information c…

I find LLMisms very annoying to read, it’s almost like they are bullet points in the shape of a paragraph. It feels very “skippy” to me.

EDITED: Removed a question that I couldn’t make feel suitably polite.

Re: Qwen3.8-Flash-Next

#130

Post by bjourne »

mistersquid wrote: post #124
redrix wrote: post #53

Earlier quoted context omitted.

You’re absolutely right to be hopeful. Three honest possibilities, and I’ll be straight with you about each: 1. It overthinks — Just like the previous iteration. High confidence. 2. It doesn’t overthink — Improvement from the last model for your use case. Regression for others. 3. It sometimes overthinks — Best case all around. A feature, not an impairment. One final thing worth mentioning: (I made myself irrationall…

> You’re absolutely right to be hopeful. Three honest possibilities, and I’ll be straight with you about each: > [UGC styled humorously as LLMisms] All joking aside, having interacted with Claude intensely for the last 8 months and about 30 hours/week in the last 3, I’ve started to notice how (for want of a better word) “readable” (“digestible” ? “comprehensible” ? “Predictable” is the wrong direction.) information c…

Tl;dr: You've become a bot. :)
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