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Qwen3.8-Flash-Next

qwen.ai

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Re: Qwen3.8-Flash-Next

#221

Post by villish »

hadlock wrote: post #216
criley2 wrote: post #86

Earlier quoted context omitted.

Those prices are just tokens? Since each model uses different amounts of tokens to do the same thing, it's a misleading price that often makes open-weights look more competitive than they are, since most open weights models use dramatically more tokens and time to complete tasks than many frontier models. In Artifical Analysis's cost per task, Luna(max) costs $0.05 per task, and Qwen 3.8 27B costs $0.25 per task, a 5…

the important thing is that Qwen 3.7 27B will run unlimited jobs on my consumer grade laptop at 60 tokens/second for free, forever, in about 1-2 years

Thats only important if running it locally is critical for privacy reasons or just as a hobby.

Time has a cost in business. If a model needs 30 million tokens to achieve a similar result as another that can do it in 10 million, that 60 tokens per second will take a long time.

Re: Qwen3.8-Flash-Next

#222

Post by simonw »

simonw wrote: post #173
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…

Tried again with a different quant, UD-Q2_K_XL: https://tools.simonwillison.net/markdown-svg-renderer#url=ht...

... and once more with UD-IQ4_XS

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

Re: Qwen3.8-Flash-Next

#223

Post by aaa_aaa »

mediaman wrote: post #202
aaa_aaa wrote: post #157

Earlier quoted context omitted.

Private companies cannot play that game too long. Profit from current state of AI is a mirage and sooner or later stuff will hit the fan.

Just look at the prices that inference providers charge for small models. The argument that these unit economics are negative is trivial to disprove. DeepInfra sells DS v4-flash at 0.08 in, $0.18 out. Gemma4 they sell for $0.07 in, $0.34 out. OpenAI's price for luna is $0.20 in, $1.20 out. Why would you assume OpenAI is somehow uniquely incompetent at making small, fast models? And that they're worse at serving it th…

Because Openai is just another player. Nothing really special for now. Their valuation is ridiculously overblown.

Re: Qwen3.8-Flash-Next

#224

Post by user43928 »

itishappy wrote: post #209
user43928 wrote: post #185

Earlier quoted context omitted.

The indication that OpenAI's inference is profitable is that 3rd party providers host large models for cheaper. Given that OpenAI is ahead in intelligence, it's also reasonably likely that they are at the frontier of efficiency too. Your "evidence" for OpenAI's inference not being profitable is apparently based on leaked financials supposedly showing growing losses for reasons entirely unknown. With their research, t…

> Given that OpenAI is ahead in intelligence, it's also reasonably likely that they are at the frontier of efficiency too. Frontier labs have no incentive to be at the frontier of efficiency. Claude still leads the pack in general intelligence yet has the worst efficiency by far.

They have an incentive to make their models efficient enough to serve demand and make a profit on it.

The incentive that is missing is passing on efficiency improvements as price savings to customers, when your model is still in demand because of its higher intelligence.

Re: Qwen3.8-Flash-Next

#225

Post by hadlock »

potus_kushner wrote: post #200

at first this looked like something one could run on CPU with 64GB RAM with a 2-3 bit quant, at possibly half the speed of 3.6 35B-A3B, however the 50B ngram sidecar makes it impossible. and oddly, unsloth's page lists the ngrams as 50GB even though they say it's in 4 bits. should be 25GB according to my math. anyway, the new ngram architecture makes it pretty much unusable for regular folks who cant afford more than…

I suspect we will see optimizations where the various vectors of the n-gram you actually use are hot in vram, the rest are warm in system memory and then cold storage on nvme. Same with MoE. If your workflow is particularly same-y then you're looking at cache miss below 5% with NTP/MTP turned on and the right harness. Agentic "openclaw" type stuff cache miss might be below 1% in the right local llm setups. There's been zero exploitation of n-gram stuff yet, it will be very interesting as things progress.

Re: Qwen3.8-Flash-Next

#226

Post by marcthor »

Casteil wrote: post #110
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.

Yep.. for 'general purpose' use I found qwen3.8:27b to be disappointing due to overthinking. It's brutal especially considering how slow it is compared to MoE variants. It often overthinks to the magnitude of ~10x the tokens vs a ~4x faster gemma4:26b-a3b. As a result, qwen3.8 will churn over a prompt often for 5-10 minutes while gemma4 regularly finishes the same prompt in under 20 seconds, while giving a consistent…

[flagged]

Re: Qwen3.8-Flash-Next

#227

Post by skeledrew »

quev wrote: post #43
rohansood15 wrote: post #27

Earlier quoted context omitted.

For world knowledge, you'd want it to find and reference the source material to be sure. At that point, it doesn't matter if the knowledge is embedded.

Keep in mind a web search might not include scanned books baked in the weights ;)

Are Chinese labs also acquiring and scanning books?

Re: Qwen3.8-Flash-Next

#229

Post by TonyStr »

a11r wrote: post #201
schopra909 wrote: post #107

Can someone explain the intuition behind the en-gram idea? I know DeepSeek published a paper about it a few months ago and the Gemma models have a lightweight version of it; but it hasn’t clicked for me yet

Quoting RGFusion from Reddit: LLMs run into an issue where the further you train a model, the more it overwrites facts with generalized concepts. You need the model to be able to do both. Intelligence arises from generalization, but without accurate information the model will hallucinate. The engram table allows for a low-computational method of fact-recall. You can think of it like a better form of RAG, where the da…

Very interesting. Is this compatible with MoE architectures? I'm not too familiar with how this works.

Re: Qwen3.8-Flash-Next

#230

Post by kzrdude »

TonyStr wrote: post #229
a11r wrote: post #201

Earlier quoted context omitted.

Quoting RGFusion from Reddit: LLMs run into an issue where the further you train a model, the more it overwrites facts with generalized concepts. You need the model to be able to do both. Intelligence arises from generalization, but without accurate information the model will hallucinate. The engram table allows for a low-computational method of fact-recall. You can think of it like a better form of RAG, where the da…

Very interesting. Is this compatible with MoE architectures? I'm not too familiar with how this works.

It is compatible, and Qwen3.8 Flash Next is MoE
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