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Gemini 3.5 Flash

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Re: Gemini 3.5 Flash

#541

Post by stared »

easygenes wrote: post #466

For those who would like to know the total and active parameter count of this model: even though Google doesn't disclose the model technicals, we can infer them within relatively tight margins based on what we do know. We know they serve the model on TPU 8i, which we have plenty of hard specs for (so we know the key constraints: total memory and bandwidth and compute flops). We can also set a ceiling on the compute c…

A nice estimate! Since „you can compress knowledge, but not factual knowledge” https://x.com/bojie_li/status/2049314403208896521, it is likely we can actualy measure its size.

Re: Gemini 3.5 Flash

#542

Post by nl »

gertlabs wrote: post #492
easygenes wrote: post #466

For those who would like to know the total and active parameter count of this model: even though Google doesn't disclose the model technicals, we can infer them within relatively tight margins based on what we do know. We know they serve the model on TPU 8i, which we have plenty of hard specs for (so we know the key constraints: total memory and bandwidth and compute flops). We can also set a ceiling on the compute c…

We've been really impressed with the performance of ~30B parameter class models and how close they are to the frontier from ~6-12 months ago, which begs the question, are the frontier labs really serving 10T parameter models? Seems unlikely. If these Gemini 3.5 numbers are accurate, then I'd wager GPT 5.5 and Opus 4.7 are a lot smaller than people have speculated, too. It's not that frontier labs can't create a 5T+ p…

Elon says Opus is 5T (and I would expect he'd know)

> It's not that frontier labs can't create a 5T+ parameter model, but they don't have the data to optimize a model of that size.

The have plenty if data. They use very large amounts of verifiable synthetic data in (lots in coding and math) cover the gap.

Also the frontier labs are paying people to do tasks, tracking the trajectories and training on that. Most of the optimization is in RL based on these trajectories.

Re: Gemini 3.5 Flash

#544

Post by ohlookcake »

golfer wrote: post #91

Arena.ai: > Gemini 3.5 Flash’s pricing shifts the Pareto frontier in Text. 8 models from GoogleDeepMind dominate the Text Arena Pareto curve where only 4 labs are represented for top performance in their price tiers. https://x.com/arena/status/2056793180998361233

That graph seems odd. It looks like Gemini 3.5 Flash is not actually on the convex hull, and they forced the 'frontier' to bend inwards to include it

Re: Gemini 3.5 Flash

#546

Post by logicchains »

HDBaseT wrote: post #389
quirino wrote: post #289

Earlier quoted context omitted.

Yesterday, or the day before, Google lowered the AI Pro quota from 33x standard usage to 4x. From the talk on the Gemini subreddit it's severely lower than before. I'm likely canceling my AI Pro. The update also broke the app for me. Editing a message crashes the app every time. I'm on a Pixel lol

The crunch is real. - The model is appox 3.3x cost. - The model is realistically almost 5x cost due to token usage - Google has TPUs to run this on (yet the cost) - Google has a lot more security and backup cash compared to all other AI companies, likely even combined (yet the cost) We can continue moving the goal posts, but it seems we're at a bit of a wall. Costs are increasing, intelligence is improving, but the c…

It's an experience anyone who used Google BigQuery would be familiar with: start with an amazing engineering product, and keep continuously degrading the value users get out of a fixed dollar spend. It's like Google doesn't understand that lock-in doesn't work when customers can easily switch to Claude or GPT.

Re: Gemini 3.5 Flash

#547

Post by gertlabs »

nl wrote: post #542
gertlabs wrote: post #492

Earlier quoted context omitted.

We've been really impressed with the performance of ~30B parameter class models and how close they are to the frontier from ~6-12 months ago, which begs the question, are the frontier labs really serving 10T parameter models? Seems unlikely. If these Gemini 3.5 numbers are accurate, then I'd wager GPT 5.5 and Opus 4.7 are a lot smaller than people have speculated, too. It's not that frontier labs can't create a 5T+ p…

Elon says Opus is 5T (and I would expect he'd know) > It's not that frontier labs can't create a 5T+ parameter model, but they don't have the data to optimize a model of that size. The have plenty if data. They use very large amounts of verifiable synthetic data in (lots in coding and math) cover the gap. Also the frontier labs are paying people to do tasks, tracking the trajectories and training on that. Most of the…

This is what we do at gertlabs.com - the foundation labs are actually starving for better data. Having quality data is not the same as having a lot of data. Human curated data / RLHF cannot scale to a 5T model and synthetic data pipelines are very much a work in progress in the industry.

Some interesting notes:

- Training a small model with large model output resulted in LESS improvement than distilling a less smart model onto the same small architecture [0]. We are starting to hit intelligence density limits in small models (- good RL environments incidentally also make for good benchmarking

[0] https://arxiv.org/html/2502.12143v1

Re: Gemini 3.5 Flash

#548

Post by maipen »

nl wrote: post #542
gertlabs wrote: post #492

Earlier quoted context omitted.

We've been really impressed with the performance of ~30B parameter class models and how close they are to the frontier from ~6-12 months ago, which begs the question, are the frontier labs really serving 10T parameter models? Seems unlikely. If these Gemini 3.5 numbers are accurate, then I'd wager GPT 5.5 and Opus 4.7 are a lot smaller than people have speculated, too. It's not that frontier labs can't create a 5T+ p…

Elon says Opus is 5T (and I would expect he'd know) > It's not that frontier labs can't create a 5T+ parameter model, but they don't have the data to optimize a model of that size. The have plenty if data. They use very large amounts of verifiable synthetic data in (lots in coding and math) cover the gap. Also the frontier labs are paying people to do tasks, tracking the trajectories and training on that. Most of the…

I’m not sure if synthetic data is enough.

Xai paying cursor to train models with their data, tell us that having an agent tool like claude code is important for quality data acquisition. That’s why they recently shipped grok build

I think we will see insane SOTA models from xai in the next few months.

Re: Gemini 3.5 Flash

#549

Post by schneehertz »

yieldcrv wrote: post #49
aliljet wrote: post #33

Is there a good benchmark tracking hallucinations? The models are all incredibly good now, even the open ones, and my hope is that the rate of hallucinations is something that's falling off in concert with larger and larger context lengths.

if last year's models were the ones people got familiar with in late 2022, hallucinations would be an underrepresented rumor, there would be no articles about it because its so rare. overconfident lawyers wouldn't have messed up dockets in court with fake case law, in other domains that move faster, sources would be only partially outdated with agentic search and mcp servers filling in the gaps AI psychosis would be…

Victim of LLM hallucinations, poor guy
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