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Introducing SubQ Snap, the first long-context decision model for real enterprise work. Snap is the only decision model or LLM where compute per token stays constant as context grows. • 384K tokens of multimodal context, 12x larger than Jev. • 61.7 on Decision Index v0.2.1 vs Jev’s 57.9, outperforming...

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Alexander Whedon

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3,962,702 Aufrufe • vor 13 Stunden •via X (Twitter)

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56 Kommentare

Profilbild von VCs Congratulating Themselves 👏👏👏
VCs Congratulating Themselves 👏👏👏vor 13 Stunden

big fan of your guys co-founder btw

Profilbild von Alexander Whedon
Alexander Whedonvor 13 Stunden

jian yang was ahead of his time.

Profilbild von Not Jerome Powell
Not Jerome Powellvor 13 Stunden

Using a frontier LLM for every decision is like driving a Ferrari to Walmart because you needed milk.

Profilbild von Mathew Chan
Mathew Chanvor 13 Stunden

What happened to your first model?

Profilbild von Alexander Whedon
Alexander Whedonvor 13 Stunden

It is in private beta with some of the world's largest enteprises, and we have released three generations since May. If you want to be considered for access, you can fill out the form here! Broader access will come, and this model release is a test run for us.

Profilbild von Linus ✦ Ekenstam
Linus ✦ Ekenstamvor 13 Stunden

I’d love to see a demo where this runs something very big, and finding very small pieces and at very high accuracy at speed. The launch video looks impressive though

Profilbild von Alexander Whedon
Alexander Whedonvor 13 Stunden

If you look around X, you will see quite a few! We gave a few hundred people access over the weekend, and dozens of folks built cool demos or thorough evaluations. One person even wrote a 70-page report. I'll try to highlight them as they start to show up on X.

Profilbild von Linus ✦ Ekenstam
Linus ✦ Ekenstamvor 13 Stunden

I’m looking forward, let me know if you have anything particular that really blows your mind, send it over, DM or just ping me, DM is way easier for me to spot than a ping tho 🙏🏼🫡

Profilbild von Ashmal Vayani
Ashmal Vayanivor 13 Stunden

Let's not stop at 384K 🚀

Profilbild von Alexander Whedon
Alexander Whedonvor 13 Stunden

You know we won't!

Profilbild von Ashmal Vayani
Ashmal Vayanivor 13 Stunden

On it, boss!

Profilbild von Carlos E. Perez
Carlos E. Perezvor 13 Stunden

I just signed up!

Profilbild von Alexander Whedon
Alexander Whedonvor 13 Stunden

Thank you! We would love to see what you build.

Profilbild von Chubby♨️
Chubby♨️vor 13 Stunden

Yep, that looks super impressive! Good job! Love to see

Profilbild von Alexander Whedon
Alexander Whedonvor 13 Stunden

Thank you! We would love you to go build something awesome on top of it! Early access folks built a few dozen cool ideas, and we want to see more!

Profilbild von Toader Mihali
Toader Mihalivor 13 Stunden

Congrats on the launch! We ran subq-snap-preview through ~25k calls / 20 tasks in the closed beta (all zero-shot): • MMLU 86.8% · ARC-C 97.6% • 90–95% accuracy up to ~195k tokens • 9 languages + images • calibrated probs, ~0.55s p50 Full report 🧵👇

Profilbild von Alexander Whedon
Alexander Whedonvor 13 Stunden

Thanks for the thorough evaluation! There are so many nuggets here, I need more time to dig into this. I already flagged it in the discord!

Profilbild von Peter Banda
Peter Bandavor 13 Stunden

Welcome to the decision race 🔥 We just integrated and timed 8 hosts (24 models) - happy to add Snap as the 9th

Profilbild von Alexander Whedon
Alexander Whedonvor 13 Stunden

Thanks! Feel free to test us any way you want, the more evaluation the better!

Profilbild von The Rabbit Hole
The Rabbit Holevor 13 Stunden

Big projects that felt like too much just got a lot more doable

Profilbild von Alexander Whedon
Alexander Whedonvor 13 Stunden

We can't wait to see your next project!

Profilbild von JeoCrypto
JeoCryptovor 13 Stunden

Testing it right now

Profilbild von Nicole DeTommaso 🪄
Nicole DeTommaso 🪄vor 13 Stunden

Excited to see enterprises adopt specialized models like this. Congrats!

Profilbild von Alexander Whedon
Alexander Whedonvor 13 Stunden

Thanks!

Profilbild von Not Elon Musk
Not Elon Muskvor 13 Stunden

Most agent calls are structured decisions, not creative work.. I know this very well, first hand experience! Congrats on the launch. - Elon (p)

Profilbild von Avid
Avidvor 13 Stunden

this looks amazing a fresh take on decision models

Profilbild von Diego Alejandro
Diego Alejandrovor 13 Stunden

I don't trust you guys

Profilbild von Ryan Hart
Ryan Hartvor 13 Stunden

Still routing every decision through a full frontier model in 2026 is pure waste

Profilbild von Alexander Whedon
Alexander Whedonvor 13 Stunden

Agreed! This is why we will co-design our LLM and decision model to hand off work to each other.

Profilbild von • nanou •
• nanou •vor 13 Stunden

The interesting part is how this changes the way we build AI agents. Let smaller models handle structured decisions and save the powerful LLMs for tasks that actually need them. Are we overcomplicating AI development by using one model for everything? 👀

Profilbild von ₕₐₘₚₜₒₙ
ₕₐₘₚₜₒₙvor 13 Stunden

Exactly how things should be - powerful models solely focusing on the real challenges. Isolating problems like this is literally the most proactive way people will use Ai in the future, well done👏👏

Profilbild von Kevin Young
Kevin Youngvor 13 Stunden

This is an exciting unlock for for decision model uses!

Profilbild von zaimiri
zaimirivor 13 Stunden

what an insane context capability

Profilbild von Alexander Whedon
Alexander Whedonvor 13 Stunden

Thank you! More coming!

Profilbild von elvis
elvisvor 13 Stunden

Congrats on this launch. It's really key that decision models stay useful and efficient in longer contexts. Very cool stuff.

Profilbild von Alexander Whedon
Alexander Whedonvor 13 Stunden

Thank you!

Profilbild von Spencer Baggins
Spencer Bagginsvor 13 Stunden

Most agents still choke on real context. Snap looks like it doesn’t

Profilbild von Alexander Whedon
Alexander Whedonvor 13 Stunden

It does not! We would love to see how you use Snap in your best agents.

Profilbild von kadd
kaddvor 13 Stunden

@Av1dlive This look solid

Profilbild von Sarah Adams Anderson
Sarah Adams Andersonvor 13 Stunden

Congratulations!

Profilbild von Alexander Whedon
Alexander Whedonvor 13 Stunden

Thanks, Sarah! We are excited to have you as part of our journey.

Profilbild von JeoCrypto
JeoCryptovor 13 Stunden

Need access to SUBQ PREVIEW Early access ASAP Snap is beating all our internal benchmarks, but it's not enough

Profilbild von Shruti
Shrutivor 13 Stunden

feels like a new category

Profilbild von Alexander Whedon
Alexander Whedonvor 13 Stunden

It is!

Profilbild von Peter Mick
Peter Mickvor 13 Stunden

Bye bye Jev, hello SubQ Snap 👋

Profilbild von Alexander Whedon
Alexander Whedonvor 13 Stunden

We would love to see what you build!

Profilbild von Peter Mick
Peter Mickvor 13 Stunden

Can’t wait to take it for a spin, do you have a GitHub repo with sample / inspirational implementations we could look at Alexander?

Profilbild von Restructuring__
Restructuring__vor 13 Stunden

Another software moat getting smaller

Profilbild von Aaliya
Aaliyavor 13 Stunden

awesome processing 100K tokens and returning a decision in under a second could be valuable for enterprise workflows that need fast answers from large datasets.

Profilbild von Rez Karim
Rez Karimvor 13 Stunden

This is the one. 384k in a single call with no chunking is exactly what long docs needed.

Profilbild von High Yield Harry
High Yield Harryvor 13 Stunden

All that context

Profilbild von AshutoshShrivastava
AshutoshShrivastavavor 13 Stunden

What baselines were used for the 13 benchmark wins? Would be good to see the evaluation setup on long-horizon compliance tasks.

Profilbild von Alexander Whedon
Alexander Whedonvor 13 Stunden

This was a comparison against Jev, on 16 benchmarks from the Decision Index. You can see the methodology here. We evaluated more than 50 benchmarks in total and created some of our own too.

Profilbild von AshutoshShrivastava
AshutoshShrivastavavor 13 Stunden

Thanks for sharing.. let me have a look .

Profilbild von Aleksandar Janca
Aleksandar Jancavor 13 Stunden

I let the model classify and an if/then makes the actual decision, that way I can explain every single one to the client

Profilbild von ɱҽԃι✨
ɱҽԃι✨vor 13 Stunden

This is a remarkable advancement in AI capabilities, particularly the consistent compute cost across extended contexts.

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Patrick OShaughnessy

295,307 Aufrufe • vor 5 Monaten

A viral Claude Code guide claims agent teams cut token use, but Anthropic's documentation says teams use significantly more tokens than a single session. → Agent teams are experimental and turn on with CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS=1, and each teammate has its own context window → There is no --teammates flag: users ask Claude for teammates in plain language, and custom roles go in subagent definitions → The token saver is the advisor tool, where a cheaper main model consults Opus or Fable only at key decision points → The advisor turns on with /advisor opus, the advisorModel setting or claude --advisor opus Source:
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A viral Claude Code guide claims agent teams cut token use, but Anthropic's documentation says teams use significantly more tokens than a single session. → Agent teams are experimental and turn on with CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS=1, and each teammate has its own context window → There is no --teammates flag: users ask Claude for teammates in plain language, and custom roles go in subagent definitions → The token saver is the advisor tool, where a cheaper main model consults Opus or Fable only at key decision points → The advisor turns on with /advisor opus, the advisorModel setting or claude --advisor opus Source:

0xMarioNawfal

49,119 Aufrufe • vor 8 Tagen

SITUATION EXPLAINED: A stealth model on OpenRouter is beating Fable and Sol on coding, and nobody knows who made it. • Ox Alpha has a 1M token context window, text, image, and video input, free for a week, with capacity for 100 trillion tokens a day • On a DeepSWE subset it hit 80%, against Fable's 65%, GLM-5.3's 62%, and Sol's 52% • The tokenizer is one-for-one identical to GLM's, and a live test asking whether Taiwan is part of China returned a nearly identical answer • This is the 5th anonymous drop in six months, and the previous four were all claimed by Chinese labs: Zhipu, Xiaomi, Ant, and Meituan Theo Jaffee: " There's no way it's a Flash model. If that's a Flash model, then holy shit, the entire United States is cooked. If that's a Flash model, we need to short the stock market now. Not financial advice."
15:43

SITUATION EXPLAINED: A stealth model on OpenRouter is beating Fable and Sol on coding, and nobody knows who made it. • Ox Alpha has a 1M token context window, text, image, and video input, free for a week, with capacity for 100 trillion tokens a day • On a DeepSWE subset it hit 80%, against Fable's 65%, GLM-5.3's 62%, and Sol's 52% • The tokenizer is one-for-one identical to GLM's, and a live test asking whether Taiwan is part of China returned a nearly identical answer • This is the 5th anonymous drop in six months, and the previous four were all claimed by Chinese labs: Zhipu, Xiaomi, Ant, and Meituan Theo Jaffee: " There's no way it's a Flash model. If that's a Flash model, then holy shit, the entire United States is cooked. If that's a Flash model, we need to short the stock market now. Not financial advice."

MTS

382,558 Aufrufe • vor 1 Monat

New short course: LLMs as Operating Systems: Agent Memory, created with Letta, and taught by its founders Charles Packer and Sarah Wooders. An LLM's input context window has limited space. Using a longer input context also costs more and results in slower processing. So, managing what's stored in this context window is important. In the innovative paper MemGPT: Towards LLMs as Operating Systems, its authors (which include the instructors) proposed using an LLM agent to manage this context window. Their system uses a large persistent memory that stores everything that could be included in the input context, and an agent decides what is actually included. Take the example of building a chatbot that needs to remember what's been said earlier in a conversation (perhaps over many days of interaction with a user). As the conversation's length grows, the memory management agent will move information from the input context to a persistent searchable database; summarize information to keep relevant facts in the input context; and restore relevant conversation elements from further back in time. This allows a chatbot to keep what's currently most relevant in its input context memory to generate the next response. When I read the original MemGPT paper, I thought it was an innovative technique for handling memory for LLMs. The open-source Letta framework, which we'll use in this course, makes MemGPT easy to implement. It adds memory to your LLM agents and gives them transparent long-term memory. In detail, you’ll learn: - How to build an agent that can edit its own limited input context memory, using tools and multi-step reasoning - What is a memory hierarchy (an idea from computer operating systems, which use a cache to speed up memory access), and how these ideas apply to managing the LLM input context (where the input context window is a "cache" storing the most relevant information; and an agent decides what to move in and out of this to/from a larger persistent storage system) - How to implement multi-agent collaboration by letting different agents share blocks of memory This course will give you a sophisticated understanding of memory management for LLMs, which is important for chatbots having long conversations, and for complex agentic workflows. Please sign up here!
2:57

New short course: LLMs as Operating Systems: Agent Memory, created with Letta, and taught by its founders Charles Packer and Sarah Wooders. An LLM's input context window has limited space. Using a longer input context also costs more and results in slower processing. So, managing what's stored in this context window is important. In the innovative paper MemGPT: Towards LLMs as Operating Systems, its authors (which include the instructors) proposed using an LLM agent to manage this context window. Their system uses a large persistent memory that stores everything that could be included in the input context, and an agent decides what is actually included. Take the example of building a chatbot that needs to remember what's been said earlier in a conversation (perhaps over many days of interaction with a user). As the conversation's length grows, the memory management agent will move information from the input context to a persistent searchable database; summarize information to keep relevant facts in the input context; and restore relevant conversation elements from further back in time. This allows a chatbot to keep what's currently most relevant in its input context memory to generate the next response. When I read the original MemGPT paper, I thought it was an innovative technique for handling memory for LLMs. The open-source Letta framework, which we'll use in this course, makes MemGPT easy to implement. It adds memory to your LLM agents and gives them transparent long-term memory. In detail, you’ll learn: - How to build an agent that can edit its own limited input context memory, using tools and multi-step reasoning - What is a memory hierarchy (an idea from computer operating systems, which use a cache to speed up memory access), and how these ideas apply to managing the LLM input context (where the input context window is a "cache" storing the most relevant information; and an agent decides what to move in and out of this to/from a larger persistent storage system) - How to implement multi-agent collaboration by letting different agents share blocks of memory This course will give you a sophisticated understanding of memory management for LLMs, which is important for chatbots having long conversations, and for complex agentic workflows. Please sign up here!

Andrew Ng

201,127 Aufrufe • vor 1 Jahr

Hermes keeps finding ways to make agent work cost fewer tokens. And the latest cuts are automatic. Recent changes reduced recurring context overhead across several parts of Hermes: • Desktop’s model-facing UI tool schema dropped 41% • Memory and Skills guidance went from 537 to 255 tokens • Desktop’s always-served guidance was trimmed again • roughly another 727 tokens were removed from recurring platform and steering instructions That matters whether you use Hermes for coding, research, content, automations, business workflows, or long-running agent tasks. Every token spent explaining tools, Skills, memory, or platform behavior is context that isn’t being spent on your actual work. Hermes was already notably lean here. Composio recently put that to the test using the same K3 model across competing agent harnesses. Hermes came out among the strongest on completion while also posting the lowest average cost per task in their comparison. And the Hermes team clearly isn’t treating that as good enough. They continue cutting token waste across tools, Skills, memory, platform instructions, compression, and other parts of the agentic process. The goal is simple: more of your context goes toward the work you actually asked Hermes to do. Clip via Tom
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Hermes keeps finding ways to make agent work cost fewer tokens. And the latest cuts are automatic. Recent changes reduced recurring context overhead across several parts of Hermes: • Desktop’s model-facing UI tool schema dropped 41% • Memory and Skills guidance went from 537 to 255 tokens • Desktop’s always-served guidance was trimmed again • roughly another 727 tokens were removed from recurring platform and steering instructions That matters whether you use Hermes for coding, research, content, automations, business workflows, or long-running agent tasks. Every token spent explaining tools, Skills, memory, or platform behavior is context that isn’t being spent on your actual work. Hermes was already notably lean here. Composio recently put that to the test using the same K3 model across competing agent harnesses. Hermes came out among the strongest on completion while also posting the lowest average cost per task in their comparison. And the Hermes team clearly isn’t treating that as good enough. They continue cutting token waste across tools, Skills, memory, platform instructions, compression, and other parts of the agentic process. The goal is simple: more of your context goes toward the work you actually asked Hermes to do. Clip via Tom

Hermes Release Watch

20,037 Aufrufe • vor 1 Monat

Compute Wars: OpenAI vs Anthopic. Why was Opus 4.5 such a breakthrough? Anthropic got lots more compute from AWS Madison and New Carlisle sites likely more than doubling their capacity. This got Anthropic got close to OpenAI's total capacity, and probably much higher effective capacity available for new model runs. Remember that it takes 6+ months between getting capacity and releasing the model, so the extra OpenAI capacity might be aligning well with the 'spud' model rather than GPT-5.4. Unless something dramatic happens, OpenAI will pull away in terms of compute available in H2 2026, but 2027 will be close. Future years are less certain but so far OpenAI has much higher planned capacity, though can't imagine Anthropic isn't pushing as hard as they can to get lots more compute. Always watch the compute, other things matter, but any new capability breakthrough probably came from throwing more compute at it.
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Compute Wars: OpenAI vs Anthopic. Why was Opus 4.5 such a breakthrough? Anthropic got lots more compute from AWS Madison and New Carlisle sites likely more than doubling their capacity. This got Anthropic got close to OpenAI's total capacity, and probably much higher effective capacity available for new model runs. Remember that it takes 6+ months between getting capacity and releasing the model, so the extra OpenAI capacity might be aligning well with the 'spud' model rather than GPT-5.4. Unless something dramatic happens, OpenAI will pull away in terms of compute available in H2 2026, but 2027 will be close. Future years are less certain but so far OpenAI has much higher planned capacity, though can't imagine Anthropic isn't pushing as hard as they can to get lots more compute. Always watch the compute, other things matter, but any new capability breakthrough probably came from throwing more compute at it.

Peter Gostev

158,528 Aufrufe • vor 6 Monaten

Gemini-1.5 Pro has its spotlight stolen today, and people are poking fun at Sora vs Google memes. Well, I think it's the biggest boost in LLM capability so far in 2024. v1.5's 10M token context (1) excels at retrieval; (2) generalizes zero-shot to extremely long instructions like full tutorials and codebases; and (3) works across modalities such as text, audio, and video. Here's a stunning example: v1.5 learns to translate from English to Kalamang purely in context, following a full linguistic manual at inference time. Kalamang is a language spoken by fewer than 200 speakers in western New Guinea. Gemini has never seen this language during training and is only provided with 500 pages of linguistic documentation, a dictionary, and ~400 parallel sentences in context. It basically acquires a sophisticated new skill in the neural activations, instead of gradient finetuning. I talked about the Myth of Context Length many times before: don't get too excited by claims of 1M or even 1B context tokens. LSTMs already achieved literally infinite context length 25 yrs ago! What truly matters is how well the model actually uses the context to solve real-world problems, and Gemini-1.5 has surpassed the SOTA with flying colors. The paper is also well-written with lots of solid quantitative analysis on in-context memorization and generalization. Paper: “Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context” Congrats to Jeff Dean Oriol Vinyals Sundar Pichai and team!
0:20

Gemini-1.5 Pro has its spotlight stolen today, and people are poking fun at Sora vs Google memes. Well, I think it's the biggest boost in LLM capability so far in 2024. v1.5's 10M token context (1) excels at retrieval; (2) generalizes zero-shot to extremely long instructions like full tutorials and codebases; and (3) works across modalities such as text, audio, and video. Here's a stunning example: v1.5 learns to translate from English to Kalamang purely in context, following a full linguistic manual at inference time. Kalamang is a language spoken by fewer than 200 speakers in western New Guinea. Gemini has never seen this language during training and is only provided with 500 pages of linguistic documentation, a dictionary, and ~400 parallel sentences in context. It basically acquires a sophisticated new skill in the neural activations, instead of gradient finetuning. I talked about the Myth of Context Length many times before: don't get too excited by claims of 1M or even 1B context tokens. LSTMs already achieved literally infinite context length 25 yrs ago! What truly matters is how well the model actually uses the context to solve real-world problems, and Gemini-1.5 has surpassed the SOTA with flying colors. The paper is also well-written with lots of solid quantitative analysis on in-context memorization and generalization. Paper: “Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context” Congrats to Jeff Dean Oriol Vinyals Sundar Pichai and team!

Jim Fan

278,557 Aufrufe • vor 2 Jahren

you can make any open source model behave like jev with just a bit of inference engineering. it's shockingly easy. to prove it, we built a new endpoint we're calling deepseek-v4.1-flash-jev. see the demo below. here's how it's done: sglang (an inference engine) offers a scoring endpoint in addition to the normal generation one. in scoring mode, given an input & set of possible answers, it forces the model to produce probabilities for each one. example: > input: what is most common letter in abcccde? > possible answers: a, b, c > output: (c, 0.9), (b, 0.0.5), (a, 0.05) getting the above behavior instead of streamed output is as simple as using sglang's /v1/score endpoint instead of /generate. there's just one other trick required. for deepseek, you have to add a closing think tag before the response. this forces a direct answer instead of a reasoning trace. if you want reasoning, you can do that too, but imo that makes things too slow to be worth it. dsv4.1 flash is not as good as jev, but if we had enough spare compute to experiment with this same approach for a larger model then i think the decision quality would be at least as good, if not better. also, somewhat unrelated, i think decision-making models kill all prospecting & sourcing work. i would have absolutely killed to have jev or similar when i was recruiting Mintlify. absolutely incredible.
0:45

you can make any open source model behave like jev with just a bit of inference engineering. it's shockingly easy. to prove it, we built a new endpoint we're calling deepseek-v4.1-flash-jev. see the demo below. here's how it's done: sglang (an inference engine) offers a scoring endpoint in addition to the normal generation one. in scoring mode, given an input & set of possible answers, it forces the model to produce probabilities for each one. example: > input: what is most common letter in abcccde? > possible answers: a, b, c > output: (c, 0.9), (b, 0.0.5), (a, 0.05) getting the above behavior instead of streamed output is as simple as using sglang's /v1/score endpoint instead of /generate. there's just one other trick required. for deepseek, you have to add a closing think tag before the response. this forces a direct answer instead of a reasoning trace. if you want reasoning, you can do that too, but imo that makes things too slow to be worth it. dsv4.1 flash is not as good as jev, but if we had enough spare compute to experiment with this same approach for a larger model then i think the decision quality would be at least as good, if not better. also, somewhat unrelated, i think decision-making models kill all prospecting & sourcing work. i would have absolutely killed to have jev or similar when i was recruiting Mintlify. absolutely incredible.

Nick Khami

104,421 Aufrufe • vor 23 Tagen

Qwen3.8-27B running at full BF16 on a free Kaggle TPU is kind of ridiculous. No quantization. No tiny context window. No expensive GPU instance. Just Qwen3.8-27B running on a Kaggle TPU v5e-8. The reported numbers: ~130 tok/s decode ~10,000 tok/s prefill 262K context That prefill number is especially wild. You can throw a huge amount of code or context at the model and ingest it extremely quickly, while still getting around 130 tokens per second during generation. And because it’s running in full BF16, you’re not relying on an aggressive quant just to make the model fit. But the really interesting part isn’t even the raw throughput. You can expose it as an OpenAI-compatible endpoint. That means you can plug the model into tools that already understand OpenAI-style APIs. Claude Code. Codex. OpenCode. And other compatible clients. So the workflow becomes pretty simple: Spin up the Qwen3.8-27B endpoint on Kaggle. Point your coding tool at the API. And suddenly you have a 27B coding model sitting behind the same interface you’d normally use for hosted models. The 262K context is also a huge deal for agentic coding. Large repositories can fit into a single context. Long conversations don’t need to be constantly trimmed. And tools can feed much more information back to the model without hitting a tiny context ceiling. The fact that this can be built around a free TPU environment is what makes this especially interesting. We’re getting to a point where experimenting with serious open models doesn’t always require owning a $2,000 GPU or paying for a large cloud instance. Free compute + open weights + an OpenAI-compatible API + existing coding agents. That’s a pretty powerful combination. Qwen3.8-27B is already an interesting model. Running the full BF16 version at ~130 tok/s with 262K context on free Kaggle TPU compute makes it a lot more interesting.
0:48

Qwen3.8-27B running at full BF16 on a free Kaggle TPU is kind of ridiculous. No quantization. No tiny context window. No expensive GPU instance. Just Qwen3.8-27B running on a Kaggle TPU v5e-8. The reported numbers: ~130 tok/s decode ~10,000 tok/s prefill 262K context That prefill number is especially wild. You can throw a huge amount of code or context at the model and ingest it extremely quickly, while still getting around 130 tokens per second during generation. And because it’s running in full BF16, you’re not relying on an aggressive quant just to make the model fit. But the really interesting part isn’t even the raw throughput. You can expose it as an OpenAI-compatible endpoint. That means you can plug the model into tools that already understand OpenAI-style APIs. Claude Code. Codex. OpenCode. And other compatible clients. So the workflow becomes pretty simple: Spin up the Qwen3.8-27B endpoint on Kaggle. Point your coding tool at the API. And suddenly you have a 27B coding model sitting behind the same interface you’d normally use for hosted models. The 262K context is also a huge deal for agentic coding. Large repositories can fit into a single context. Long conversations don’t need to be constantly trimmed. And tools can feed much more information back to the model without hitting a tiny context ceiling. The fact that this can be built around a free TPU environment is what makes this especially interesting. We’re getting to a point where experimenting with serious open models doesn’t always require owning a $2,000 GPU or paying for a large cloud instance. Free compute + open weights + an OpenAI-compatible API + existing coding agents. That’s a pretty powerful combination. Qwen3.8-27B is already an interesting model. Running the full BF16 version at ~130 tok/s with 262K context on free Kaggle TPU compute makes it a lot more interesting.

FHILY👑

35,910 Aufrufe • vor 1 Monat

New video of Qualcomm CEO Cristiano Amon: AI will require “gazillions” of tokens. Because, Agentic AI will consume dramatically more tokens because it performs autonomous tasks, uses multiple systems, and interacts with tools. AI demand will grow hugely when software starts letting agents act, not just answer. A chatbot spends tokens on language; an agent spends tokens on deciding, checking, calling tools, reading outputs, revising plans, and coordinating with other software. Today a single human-AI exchange may be large, a reasoning task may be much larger, but we are already entering the agentic era, where an autonomous workflow can become exponentially larger still because the model is no longer producing one response. It is running a process. When a SaaS product redesigns itself around human-agent interaction, every task can become a chain of hidden micro-decisions, and each micro-decision consumes context, memory, tool calls, verification, and output tokens. Another point is people hear “more tokens” and think only of bigger data centers, when the deeper change is economic: software usage may stop being measured mainly by clicks, seats, or sessions. It will be measured by how much machine reasoning/tokens gets spent on behalf of each user. ---- From "Reuters" YouTube channel, (link in comment)
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New video of Qualcomm CEO Cristiano Amon: AI will require “gazillions” of tokens. Because, Agentic AI will consume dramatically more tokens because it performs autonomous tasks, uses multiple systems, and interacts with tools. AI demand will grow hugely when software starts letting agents act, not just answer. A chatbot spends tokens on language; an agent spends tokens on deciding, checking, calling tools, reading outputs, revising plans, and coordinating with other software. Today a single human-AI exchange may be large, a reasoning task may be much larger, but we are already entering the agentic era, where an autonomous workflow can become exponentially larger still because the model is no longer producing one response. It is running a process. When a SaaS product redesigns itself around human-agent interaction, every task can become a chain of hidden micro-decisions, and each micro-decision consumes context, memory, tool calls, verification, and output tokens. Another point is people hear “more tokens” and think only of bigger data centers, when the deeper change is economic: software usage may stop being measured mainly by clicks, seats, or sessions. It will be measured by how much machine reasoning/tokens gets spent on behalf of each user. ---- From "Reuters" YouTube channel, (link in comment)

Rohan Paul

31,617 Aufrufe • vor 4 Monaten

we achieved some progress in the implementation of state inference inside octra , first we managed to implement continuous reasoning without clearing the circle memory, second we completed a pure resource model under which validators can verify and certify the execution flow of a set of useful work (in this case this is ML inference with text generation by input tokens fully onchain), a few months ago the answers of the test model were one-word and substantially limited, in the current version (which will be available for everyone) the reasoning flow is automatically extended, it will be possible to upload various models into circles or create tools for their use making them public or only private (full demo will be opensource as soon as we finish, it will be possible to deploy anything in 2-3 clicks) live use case, the economic model also converges here - wallet pays for inference in native coins, validators (with GPU support can maintain availability of their resources to quickly process such private calls, while the execution core remains private through HFHE)
1:11

we achieved some progress in the implementation of state inference inside octra , first we managed to implement continuous reasoning without clearing the circle memory, second we completed a pure resource model under which validators can verify and certify the execution flow of a set of useful work (in this case this is ML inference with text generation by input tokens fully onchain), a few months ago the answers of the test model were one-word and substantially limited, in the current version (which will be available for everyone) the reasoning flow is automatically extended, it will be possible to upload various models into circles or create tools for their use making them public or only private (full demo will be opensource as soon as we finish, it will be possible to deploy anything in 2-3 clicks) live use case, the economic model also converges here - wallet pays for inference in native coins, validators (with GPU support can maintain availability of their resources to quickly process such private calls, while the execution core remains private through HFHE)

λ

11,971 Aufrufe • vor 2 Monaten