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Qwen3-235B Thinking Qwen is live on anycoder using Cerebras 1700 TPS • 131K Context • 1.7s time-to-full-answer

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AK

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17,419 görüntüleme • 1 yıl önce •via X (Twitter)

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Qwen3-235B is the most intelligent, and ergonomic model that can run at 192GB VRAM with full context. This to me is incredibly strange, in that I typically dislike Qwen models, due to the extensive rejections. It says something about having higher ACTIVE parameters. • Score: 32/35 (91.4%). - 60~ gen TPS 1.5k prefill
2:29

Qwen3-235B is the most intelligent, and ergonomic model that can run at 192GB VRAM with full context. This to me is incredibly strange, in that I typically dislike Qwen models, due to the extensive rejections. It says something about having higher ACTIVE parameters. • Score: 32/35 (91.4%). - 60~ gen TPS 1.5k prefill

0xSero

19,408 görüntüleme • 7 ay önce

We are just a little bit faster than@nvidia GPUs on Qwen 235B...18X faster. inference is blazing fast...come build cool stuff on Cerebras inference
0:23

We are just a little bit faster than@nvidia GPUs on Qwen 235B...18X faster. inference is blazing fast...come build cool stuff on Cerebras inference

Andrew Feldman

44,760 görüntüleme • 1 yıl önce

Launched by popular demand. Qwen3-32B is now live on GroqCloud. Supports over 100 languages and dialects with a 131k context window. It is not incremental. It is a leap. Only Groq delivers this level of context with real-time speed. Demo video below to spark your next build. GitHub link in the comments. ⬇️
0:49

Launched by popular demand. Qwen3-32B is now live on GroqCloud. Supports over 100 languages and dialects with a 131k context window. It is not incremental. It is a leap. Only Groq delivers this level of context with real-time speed. Demo video below to spark your next build. GitHub link in the comments. ⬇️

Groq Inc

26,498 görüntüleme • 1 yıl önce

Qwen3-Next (thinking & non-thinking) are now live in BF16 at Hyperbolic! Qwen3-Next is a huge efficiency leap: - 80B MoE with just 3B active params - 10x cheaper to train vs Qwen3-32B - 10x inference throughput for >32K tokens Proud to be a launch partner with Qwen - kudos to this amazing team for keeping pushing open-source AI forward. We’re the first to serve Qwen3-Next on Hugging Face. Give it a try!
0:23

Qwen3-Next (thinking & non-thinking) are now live in BF16 at Hyperbolic! Qwen3-Next is a huge efficiency leap: - 80B MoE with just 3B active params - 10x cheaper to train vs Qwen3-32B - 10x inference throughput for >32K tokens Proud to be a launch partner with Qwen - kudos to this amazing team for keeping pushing open-source AI forward. We’re the first to serve Qwen3-Next on Hugging Face. Give it a try!

Yuchen Jin

84,342 görüntüleme • 10 ay önce

Inspired by Junyang Lin 's suggestion — "Why not build a demo to show everyone how to use Qwen3-Max-Thinking on Qwen Chat?" — this little demo is here. Dedicated to the nearly three wonderful years of walking alongside Qwen 🌟
0:38

Inspired by Junyang Lin 's suggestion — "Why not build a demo to show everyone how to use Qwen3-Max-Thinking on Qwen Chat?" — this little demo is here. Dedicated to the nearly three wonderful years of walking alongside Qwen 🌟

Chen Cheng

27,963 görüntüleme • 6 ay önce

Kimi-K2-turbo is now live on AK 's Anycoder 🚀Time to vibe coding at turbo speed!
0:56

Kimi-K2-turbo is now live on AK 's Anycoder 🚀Time to vibe coding at turbo speed!

Kimi.ai

35,766 görüntüleme • 11 ay önce

Qwen3-235B-A22B-2507, the latest state-of-the-art open-source AI model from Alibaba, is now available to deploy on Akash using a template to deploy in under 60 seconds. Non-custodial. Opensource. Permissionless.
1:10

Qwen3-235B-A22B-2507, the latest state-of-the-art open-source AI model from Alibaba, is now available to deploy on Akash using a template to deploy in under 60 seconds. Non-custodial. Opensource. Permissionless.

Akash Network

16,220 görüntüleme • 1 yıl önce

GLM-4.6 is now live on Cerebras 🟧 - 1,000 TPS: 17x faster than Claude - World’s #1 tool calling model - Tasteful and reliable codegen - Self-serve API starting at $10 Try it now:
0:36

GLM-4.6 is now live on Cerebras 🟧 - 1,000 TPS: 17x faster than Claude - World’s #1 tool calling model - Tasteful and reliable codegen - Self-serve API starting at $10 Try it now:

Cerebras

33,135 görüntüleme • 8 ay önce

100 TPS on 16 concurrent processes running a modified version of Cursor using QWEN 3.6 27B to build a 3D Bowmasters game.
0:58

100 TPS on 16 concurrent processes running a modified version of Cursor using QWEN 3.6 27B to build a 3D Bowmasters game.

Youssof Altoukhi

11,243 görüntüleme • 2 ay önce

GLM-4.7 from Z.ai is live on Cerebras! - Frontier intelligence for coding, tool-driven agents, and multi-turn reasoning - Record coding speed: ~1,000 tokens per second (up to 1,700 TPS for other uses) - Strong price-performance: ~10x higher than Sonnet 4.5
0:39

GLM-4.7 from Z.ai is live on Cerebras! - Frontier intelligence for coding, tool-driven agents, and multi-turn reasoning - Record coding speed: ~1,000 tokens per second (up to 1,700 TPS for other uses) - Strong price-performance: ~10x higher than Sonnet 4.5

Cerebras

135,059 görüntüleme • 6 ay önce

On-device realtime transcription on iPhone 15 Pro max 🚀 Using MLX-Audio-Swift + Qwen3-ASR-0.6B by Qwen It’s much faster and more consistent with the latest adjustments. Almost ready to push to GH.
1:46

On-device realtime transcription on iPhone 15 Pro max 🚀 Using MLX-Audio-Swift + Qwen3-ASR-0.6B by Qwen It’s much faster and more consistent with the latest adjustments. Almost ready to push to GH.

Prince Canuma

44,103 görüntüleme • 5 ay önce

opencode making a pong game in vite+react using (4bit) qwen/qwen3-235b-a22b-2507 locally, served by lmstudio. It used like 130GB of RAM, 0 issues with tool calls. This is completely usable locally now. Whether it's at claude level or not, idk yet, but I've no doubt we'll be there very soon if not. Anthropic better run fast to the Saudis for help.
8:57

opencode making a pong game in vite+react using (4bit) qwen/qwen3-235b-a22b-2507 locally, served by lmstudio. It used like 130GB of RAM, 0 issues with tool calls. This is completely usable locally now. Whether it's at claude level or not, idk yet, but I've no doubt we'll be there very soon if not. Anthropic better run fast to the Saudis for help.

SIGKITTEN

30,470 görüntüleme • 1 yıl önce

Just for fun, here's what 32 simultaneous long-context generations with Qwen3 Next 80B looks like on an M3 Ultra. Using the new batch generation in mlx-lm. Context size for each is about 5k tokens:
0:41

Just for fun, here's what 32 simultaneous long-context generations with Qwen3 Next 80B looks like on an M3 Ultra. Using the new batch generation in mlx-lm. Context size for each is about 5k tokens:

Awni Hannun

50,272 görüntüleme • 10 ay önce

experimenting generating html as fast as possible 1. using cerebras glm 4.7 @ 2k tps 2. parent agent generates html scaffold with "slots" 3. each child agent is assigned to generate slot /* SLOT_START:initializeScene */ function initializeScene() { return ""; }
0:39

experimenting generating html as fast as possible 1. using cerebras glm 4.7 @ 2k tps 2. parent agent generates html scaffold with "slots" 3. each child agent is assigned to generate slot /* SLOT_START:initializeScene */ function initializeScene() { return ""; }

Steve Krouse

14,753 görüntüleme • 5 ay önce

I seriously cannot believe this is a 0.6B LLM! 🤯 Qwen just released Qwen3, a series of hybrid reasoning models that allow you to control how much "thinking" the model does for a given task. They can even run locally in your browser on WebGPU with 🤗 Transformers.js!
0:24

I seriously cannot believe this is a 0.6B LLM! 🤯 Qwen just released Qwen3, a series of hybrid reasoning models that allow you to control how much "thinking" the model does for a given task. They can even run locally in your browser on WebGPU with 🤗 Transformers.js!

Xenova

74,954 görüntüleme • 1 yıl önce

Introducing Jan-v1: 4B model for web search, an open-source alternative to Perplexity Pro. In our evals, Jan v1 delivers 91% SimpleQA accuracy, slightly outperforming Perplexity Pro while running fully locally. Use cases: - Web search - Deep Research Built on the new version of Qwen's Qwen3-4B-Thinking (up to 256k context length), fine-tuned for reasoning and tool use in Jan. You can run the model in Jan, llama.cpp, or vLLM. To enable search in Jan, go to Settings → Experimental Features → On, then Settings → MCP Servers → enable a search-related MCP such as Serper. Use the model: - Jan-v1-4B: - Jan-v1-4B-GGUF: Credit to the Qwen team for Qwen3 4B Thinking & Georgi Gerganov for llama.cpp.
0:24

Introducing Jan-v1: 4B model for web search, an open-source alternative to Perplexity Pro. In our evals, Jan v1 delivers 91% SimpleQA accuracy, slightly outperforming Perplexity Pro while running fully locally. Use cases: - Web search - Deep Research Built on the new version of Qwen's Qwen3-4B-Thinking (up to 256k context length), fine-tuned for reasoning and tool use in Jan. You can run the model in Jan, llama.cpp, or vLLM. To enable search in Jan, go to Settings → Experimental Features → On, then Settings → MCP Servers → enable a search-related MCP such as Serper. Use the model: - Jan-v1-4B: - Jan-v1-4B-GGUF: Credit to the Qwen team for Qwen3 4B Thinking & Georgi Gerganov for llama.cpp.

👋 Jan

690,311 görüntüleme • 11 ay önce

Jan v1 is now more reliable when handling searches. It's an open-source alternative to Perplexity Pro, and it now avoids infinite loops with Jan v1 2509. Performance updates: - Gains on reasoning & creativity benchmarks - Small drop on SimpleQA (91.1 -> 90.7), still competitive with Perplexity Pro The updated version is live on Jan Hub. You can run the model in Jan, llama.cpp, or vLLM. Use the model: - Model: - GGUF: Shoutout to the Qwen team for Qwen3-4B Thinking and Georgi Gerganov for llama.cpp.
0:36

Jan v1 is now more reliable when handling searches. It's an open-source alternative to Perplexity Pro, and it now avoids infinite loops with Jan v1 2509. Performance updates: - Gains on reasoning & creativity benchmarks - Small drop on SimpleQA (91.1 -> 90.7), still competitive with Perplexity Pro The updated version is live on Jan Hub. You can run the model in Jan, llama.cpp, or vLLM. Use the model: - Model: - GGUF: Shoutout to the Qwen team for Qwen3-4B Thinking and Georgi Gerganov for llama.cpp.

👋 Jan

17,759 görüntüleme • 10 ay önce

Excited to release: Jupyter Agent 2 The agent can load data, execute code, plot results inside Jupyter faster than you can scroll! 🤖 Powered by Qwen3-Coder ⚡️ Running on Cerebras ⚙️ Executed in E2B ↕️ Upload your files All videos are in *real time*!
1:15

Excited to release: Jupyter Agent 2 The agent can load data, execute code, plot results inside Jupyter faster than you can scroll! 🤖 Powered by Qwen3-Coder ⚡️ Running on Cerebras ⚙️ Executed in E2B ↕️ Upload your files All videos are in *real time*!

Leandro von Werra

66,251 görüntüleme • 11 ay önce

Stop listening to the quote out of context Here is the FULL answer from Justin Fields #Bears #BearDown #DaBears Link to FULL video below
2:11

Stop listening to the quote out of context Here is the FULL answer from Justin Fields #Bears #BearDown #DaBears Link to FULL video below

Swift Sports Network

92,427 görüntüleme • 2 yıl önce

OK Qwen 3 is really good You can create an app 100% locally and for free thanks to its code performance. Thinking mode reasons step by step to give a near-perfect answer. How to use it and prompt below
0:41

OK Qwen 3 is really good You can create an app 100% locally and for free thanks to its code performance. Thinking mode reasons step by step to give a near-perfect answer. How to use it and prompt below

Paul Couvert

108,474 görüntüleme • 1 yıl önce