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Introducing 0GM-1.0-35B-A3B. Our first proprietary AI model. Mixture of Experts (MoE), 35B parameters, 3B active per token. Trained on our own decentralized GPU network. Open source under Apache 2.0.

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0G Labs (Home of Infinite AI)

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48,838 görüntüleme • 3 ay önce •via X (Twitter)

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Alibaba just open-sourced Qwen 3.6-35B A3B 35B MoE model (only ~3B active), multimodal, agentic coding, Apache 2.0. This isn’t just efficient—it’s built for real workflows. Open models are catching up fast.
1:49

Alibaba just open-sourced Qwen 3.6-35B A3B 35B MoE model (only ~3B active), multimodal, agentic coding, Apache 2.0. This isn’t just efficient—it’s built for real workflows. Open models are catching up fast.

Mervin Praison

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NVIDIA'S $30K H100 JUST GOT OUT-INFERENCED BY A $250 RYZEN. 18 TOKENS/SEC ON A 35B MODEL, $0.03 IN ELECTRICITY, $47B GONE FROM NVDA BY FRIDAY download qwen3 35b-a3b -> load in lm studio -> disable gpu entirely -> run on cpu + ram -> hit 18 tokens/sec on a 6-core ryzen -> pay nothing per token. that loop is why moe models just made every consumer cpu a viable ai host and nvidia's inference monopoly is quietly on borrowed time. qwen3 35b-a3b + lm studio + ryzen 5 9600x + 32gb ddr5 + zero gpu - that's the stack. watch and save it, then run a 35b model on your laptop this weekend.
6:33

NVIDIA'S $30K H100 JUST GOT OUT-INFERENCED BY A $250 RYZEN. 18 TOKENS/SEC ON A 35B MODEL, $0.03 IN ELECTRICITY, $47B GONE FROM NVDA BY FRIDAY download qwen3 35b-a3b -> load in lm studio -> disable gpu entirely -> run on cpu + ram -> hit 18 tokens/sec on a 6-core ryzen -> pay nothing per token. that loop is why moe models just made every consumer cpu a viable ai host and nvidia's inference monopoly is quietly on borrowed time. qwen3 35b-a3b + lm studio + ryzen 5 9600x + 32gb ddr5 + zero gpu - that's the stack. watch and save it, then run a 35b model on your laptop this weekend.

Antid

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Qwen3.5-35B with only 3B active parameters This MoE model runs FASTER than most 7B dense models. Tested on 3 generations of NVIDIA: - 5090: 137 tok/s - 4090: 112 tok/s - 3090: 78 tok/s The surprise? The 4090 <> 5090 gap is only 22%. With a 3B active MoE, even old GPUs fly.
0:30

Qwen3.5-35B with only 3B active parameters This MoE model runs FASTER than most 7B dense models. Tested on 3 generations of NVIDIA: - 5090: 137 tok/s - 4090: 112 tok/s - 3090: 78 tok/s The surprise? The 4090 <> 5090 gap is only 22%. With a 3B active MoE, even old GPUs fly.

stevibe

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Qwen3.6-35B-A3B-NVFP4 Benchmark: MoE vs. Dense Performance Report This report details a performance comparisonbetween two artificial intelligence models, focusing on the Qwen3.6-35B-A3B-NVFP4 and its predecessor. While both models achieved flawless accuracy across a comprehensive 65-test benchmark covering vision, video, and reasoning, the 35B version demonstrated significant speed advantages. Utilizing a Mixture of Experts architecture, the 35B model achieved nearly four times higher throughput and drastically reduced latency for initial responses. It also showed superior efficiency in speculative decoding and successfully managed massive context lengths up to 128,000 tokens. Ultimately, the source recommends the 35B model for high-speed production environments, while noting the 27B model remains more stable for specific complex reasoning modes.
1:57

Qwen3.6-35B-A3B-NVFP4 Benchmark: MoE vs. Dense Performance Report This report details a performance comparisonbetween two artificial intelligence models, focusing on the Qwen3.6-35B-A3B-NVFP4 and its predecessor. While both models achieved flawless accuracy across a comprehensive 65-test benchmark covering vision, video, and reasoning, the 35B version demonstrated significant speed advantages. Utilizing a Mixture of Experts architecture, the 35B model achieved nearly four times higher throughput and drastically reduced latency for initial responses. It also showed superior efficiency in speculative decoding and successfully managed massive context lengths up to 128,000 tokens. Ultimately, the source recommends the 35B model for high-speed production environments, while noting the 27B model remains more stable for specific complex reasoning modes.

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Fully Aggressive variant Uncensored MoE model that won’t refuse red team or pentesting tasks & coding. - 35B parameters, 3B active (MoE) - 0 refusals (Aggressive variant) - Excellent agentic coding + multimodal Powerful uncensored coding model 73.4% on SWE-bench Verified & fully uncensored, It dangerous (in a good way). This is currently one of the best options if you want power without guardrails. High quality imatrix GGUF quants available
1:46

Fully Aggressive variant Uncensored MoE model that won’t refuse red team or pentesting tasks & coding. - 35B parameters, 3B active (MoE) - 0 refusals (Aggressive variant) - Excellent agentic coding + multimodal Powerful uncensored coding model 73.4% on SWE-bench Verified & fully uncensored, It dangerous (in a good way). This is currently one of the best options if you want power without guardrails. High quality imatrix GGUF quants available

Md Ismail Šojal 🕷️

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🔥 another win for open source AI SNOWFLAKE ARCTIC: 1] Mixture of Experts model with 128 (!) experts 2] open weights, open code 3] Company is sharing "cookbook" and data and teaching people how to build their own world class MoE models. 4] very cheap model to train (16x times less than Llama 3)
26:28

🔥 another win for open source AI SNOWFLAKE ARCTIC: 1] Mixture of Experts model with 128 (!) experts 2] open weights, open code 3] Company is sharing "cookbook" and data and teaching people how to build their own world class MoE models. 4] very cheap model to train (16x times less than Llama 3)

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With Kimi K3 Day-0 on vLLM: Open Frontier Intelligence for Everyone 🚀 At 2.8 trillion parameters, Moonshot AI's Kimi K3 is one of the most powerful open-weight models ever released. Starting today, you can serve it on vLLM the moment the weights are public. What K3 brings: 🧠 2.8T-parameter Mixture-of-Experts (16 of 896 experts active per token) 📚 1M-token context window 🛠️ Native multimodal understanding, including vision ⚡ Kimi Delta Attention: a hybrid of linear and full attention that makes million-token context affordable Huge thank you to Kimi.ai AI for the model release and partnership, Inferact for leading the vLLM optimizations, and to our partners at NVIDIA, AMD, and the broader vLLM community. 1/6

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🚨 Breaking: Google will soon launch Gemini CLI, a powerful open-source AI agent built for the terminal. - Built on Gemini 2.5 Pro - 1 million token context window - Free tier with 60 requests per minute and 1,000 per day - Google Search grounding for real-time context - Script and plugin support - Non-interactive mode for automation - Support for Model Context Protocol (MCP) - Integrated with Gemini Code Assist in VS Code - Fully open-source under Apache 2.0

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Cluster Protocol: Revolutionizing #AI with blockchain integrity. Proof of Compute and Deploy to earn ensures that every byte counts.🚀 We offer a platform for Compute, Data, AI models and an open source community of AI researchers. Harness idle #GPUs for collective computing. Join the shared growth with GPU Sharing Economy where GPU providers are rewarded even when their GPUs are idle sitting on the network.🤝 Collaborate on our 'GitHub for AI'. Mold the future with open-source decentralized AI models.👩‍💻 Democratizing AI, we're shattering data monopolies. Converge in our global think-tank of decentralized datasets.🌐 Together, we redefine the limits in almost every sector possible where AI can make it strides. Let's unlock our collective potential. 📈 Start your AI odyssey with us now 👋 For more details check our blog at

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Wan2.2 is now natively supported in ComfyUI on Day 0! 🔹 A next-gen video model with MoE (Mixture of Experts) architecture with dual noise experts, under Apache 2.0 license! - Cinematic-level Aesthetic Control - Large-scale Complex Motion - Precise Semantic Compliance 📚 Versions available: - Wan2.2-TI2V-5B: FP16 - Wan2.2-I2V-14B: FP16/FP8 - Wan2.2-T2V-14B: FP16/FP8 💻 Down to 8GB VRAM requirement for the 5B version with ComfyUI auto-offloading.

ComfyUI

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Today we open-source LingBot-Video — the first MoE-based video foundation model built for embodied intelligence. 🔹30B params, only 3B active at inference. 🔹Augmented with 70K hours of embodied data on top of large-scale internet video pretraining. 🔹Already outperforming Wan2.6, Seedance 1.5 Pro, and Cosmos3 Super on RBench. 🧵👇
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Today we open-source LingBot-Video — the first MoE-based video foundation model built for embodied intelligence. 🔹30B params, only 3B active at inference. 🔹Augmented with 70K hours of embodied data on top of large-scale internet video pretraining. 🔹Already outperforming Wan2.6, Seedance 1.5 Pro, and Cosmos3 Super on RBench. 🧵👇

Robbyant

1,181,155 görüntüleme • 1 ay önce

let me save you 3 hours of head scratching. if you're running local models like Qwen3.5-35B-A3B through Claude Code via llama.cpp's Anthropic endpoint, the chain will break every 3 to 5 minutes. tool call fails. flow stops. you reprompt. it recovers. 2 minutes later it stops again. the model is fine. the harness chokes on local inference latency. switch to OpenCode. same localhost endpoint. same model. same GPU. the chain doesn't break. the tradeoff: OpenCode sometimes loops. the model forgets what it already read and repeats the same tool call. but a loop you can interrupt. a broken chain kills your momentum and you start over. watch both side by side. proprietary agent vs open source agent. same 3B model. different failure modes. pick your poison.
0:19

let me save you 3 hours of head scratching. if you're running local models like Qwen3.5-35B-A3B through Claude Code via llama.cpp's Anthropic endpoint, the chain will break every 3 to 5 minutes. tool call fails. flow stops. you reprompt. it recovers. 2 minutes later it stops again. the model is fine. the harness chokes on local inference latency. switch to OpenCode. same localhost endpoint. same model. same GPU. the chain doesn't break. the tradeoff: OpenCode sometimes loops. the model forgets what it already read and repeats the same tool call. but a loop you can interrupt. a broken chain kills your momentum and you start over. watch both side by side. proprietary agent vs open source agent. same 3B model. different failure modes. pick your poison.

Sudo su

72,538 görüntüleme • 5 ay önce

Unleash the Power of AI on #Solana 🚀 FOMO is changing the game as the first AI agent launcher on SOL! 🎮 Gamified Token Launches: Each token pairs with its own unique, autonomous AI agent. 💧 Built-in Liquidity: Seamlessly trade with our bonding curve model. 🪂 Airdrop Potential: Grow your token beyond the biggest meme coins. 📈 Next Meta: AI is the future of crypto—get in early. Join 13M+ users reshaping decentralized AI! Build now: $FOMO token: Live on KuCoin, Gate & MEXC!
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Unleash the Power of AI on #Solana 🚀 FOMO is changing the game as the first AI agent launcher on SOL! 🎮 Gamified Token Launches: Each token pairs with its own unique, autonomous AI agent. 💧 Built-in Liquidity: Seamlessly trade with our bonding curve model. 🪂 Airdrop Potential: Grow your token beyond the biggest meme coins. 📈 Next Meta: AI is the future of crypto—get in early. Join 13M+ users reshaping decentralized AI! Build now: $FOMO token: Live on KuCoin, Gate & MEXC!

CZAR 👑 ₿

24,055 görüntüleme • 1 yıl önce

single RTX 3090. 24 GB VRAM. Qwen3.5-35B-A3B. 4-bit quant, 113 tokens per second at full 262K context harnessing Claude Code locally with no API, no subscription, no proxy. told it what it is. 30 Mamba2 layers, 10 attention, 256 experts, 8 active per token. said "build something that shows off what you can do." it visualized its own architecture. interactive. tokens flowing through layers. 256 experts lighting up on routing. served in the browser from the same GPU running inference. single prompt. then i said level up. 3D. Three.js. separate files. flythrough camera. clickable layers. it planned first, scaffolded 6 files, hit one API bug, fixed it itself, then optimized for smooth framerate. two iterations to a working 3D neural network explorer. llama.cpp just merged a native Anthropic endpoint. Claude Code points at localhost. the whole setup is two commands. no LiteLLM. no proxy config. the open source models coming out of china right now are genuinely changing what's possible on consumer hardware. respect to the Qwen team. this is acceleration.
0:07

single RTX 3090. 24 GB VRAM. Qwen3.5-35B-A3B. 4-bit quant, 113 tokens per second at full 262K context harnessing Claude Code locally with no API, no subscription, no proxy. told it what it is. 30 Mamba2 layers, 10 attention, 256 experts, 8 active per token. said "build something that shows off what you can do." it visualized its own architecture. interactive. tokens flowing through layers. 256 experts lighting up on routing. served in the browser from the same GPU running inference. single prompt. then i said level up. 3D. Three.js. separate files. flythrough camera. clickable layers. it planned first, scaffolded 6 files, hit one API bug, fixed it itself, then optimized for smooth framerate. two iterations to a working 3D neural network explorer. llama.cpp just merged a native Anthropic endpoint. Claude Code points at localhost. the whole setup is two commands. no LiteLLM. no proxy config. the open source models coming out of china right now are genuinely changing what's possible on consumer hardware. respect to the Qwen team. this is acceleration.

Sudo su

110,206 görüntüleme • 5 ay önce