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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.

0G Labs (Home of Infinite AI)

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48,838 次观看 • 5 个月前 •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.

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

13,063 次观看 • 5 个月前

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.

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

23,745 次观看 • 2 个月前

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.

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

69,532 次观看 • 7 个月前

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.

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.

Mike Gannotti

10,457 次观看 • 3 个月前

Introducing THIS / THAT Model 1.0. Trained by FLock. Not another chatbot. A best-in-class open-source model built to decide. 94.1% success rate on our 68-question decision test, outperforming GPT-5.6, Deepseek-4.1-flash, Kimi K3, Jev, and more. Just 1.9B parameters. Runs locally. Measured decision latency of ~31 ms on an RTX 5080 laptop GPU. We’re putting decentralized AI to work: giving developers and AI agents decision-making capabilities they can run on their own hardware. Your model. Your machine. Not every decision needs the cloud.

Introducing THIS / THAT Model 1.0. Trained by FLock. Not another chatbot. A best-in-class open-source model built to decide. 94.1% success rate on our 68-question decision test, outperforming GPT-5.6, Deepseek-4.1-flash, Kimi K3, Jev, and more. Just 1.9B parameters. Runs locally. Measured decision latency of ~31 ms on an RTX 5080 laptop GPU. We’re putting decentralized AI to work: giving developers and AI agents decision-making capabilities they can run on their own hardware. Your model. Your machine. Not every decision needs the cloud.

FLock.io

12,046 次观看 • 21 天前

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

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 🕷️

28,103 次观看 • 3 个月前

🔥 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)

🔥 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)

Wes Roth

10,242 次观看 • 2 年前

Today we're releasing ZONOS2, our next-generation real-time TTS model with high-fidelity voice cloning. ZONOS2 is the most expressive open-source TTS model, released under Apache 2.0 and available on Zyphra Cloud on AMD. 🧵

Today we're releasing ZONOS2, our next-generation real-time TTS model with high-fidelity voice cloning. ZONOS2 is the most expressive open-source TTS model, released under Apache 2.0 and available on Zyphra Cloud on AMD. 🧵

Zyphra

334,786 次观看 • 4 个月前

Today, we're laying the foundation to accelerate open & decentralized AI Introducing our protocol & testnet: A peer-to-peer compute and intelligence network. Enabling collective creation, ownership, and access of sovereign open-source AI Towards an open superintelligence future

Today, we're laying the foundation to accelerate open & decentralized AI Introducing our protocol & testnet: A peer-to-peer compute and intelligence network. Enabling collective creation, ownership, and access of sovereign open-source AI Towards an open superintelligence future

Prime Intellect

267,053 次观看 • 1 年前

Introducing HYPERWORM: The first verifiable digital organism onchain. Our open-source model adapted from Nematoduino runs in Marlin TEE network on Hyperliquid EVM. Visit: 🧵🪱

Introducing HYPERWORM: The first verifiable digital organism onchain. Our open-source model adapted from Nematoduino runs in Marlin TEE network on Hyperliquid EVM. Visit: 🧵🪱

Project Nema

195,698 次观看 • 1 年前

$Someone is running Qwen3.8-35B-A3B on a single RTX 3060 with just 12GB of VRAM and 16GB of system RAM. And the reported numbers are surprisingly good. ~50 tok/s decode ~550 tok/s prefill 262K context window 12GB VRAM 16GB system RAM No multi-GPU setup. Just a consumer graphics card and a carefully configured local inference engine. The model is particularly interesting because it’s Qwen3.8 distilled into the Qwen3.6-35B-A3B architecture. That makes it worth investigating beyond raw inference speed. The developer currently uses Ornith-1.5 as the engine behind their Hermes and Pi agents. Now the question is whether this new model can deliver enough quality and reliability to replace it. The next step is testing it as an agent, not just a chatbot. The planned comparison includes four models: • Qwen3.8-35B-A3B • Ornith-1.5-35B • Qwen3.8-27B • Base Qwen3.6-35B-A3B An agentic coding benchmark should help reveal whether the distilled model can follow instructions, modify code, solve problems, and complete multi-step tasks effectively. That distinction matters because high token throughput doesn’t automatically translate into a better coding agent. A model can generate code quickly and still struggle with planning, debugging, tool calls, or completing a task without intervention. The hardware configuration is also worth examining. The developer is using llama-server with the Q4_K_M GGUF quantization, Flash Attention enabled, and 26 MoE layers assigned to CPU processing. The configuration also enables a 262,144-token context window, Q8 KV-cache quantization, and reasoning mode. This is how the setup is configured: llama-server \ -m Qwen3.8-35B-A3B-Q4_K_M.gguf \ -ngl 99 \ --n-cpu-moe 26 \ -c 262144 \ -fa on \ --jinja \ -np 1 \ --cache-type-k q8_0 \ --cache-type-v q8_0 \ --temp 0.6 \ --top-p 0.95 \ --top-k 20 \ --reasoning on One important detail: the model’s 35B parameter count doesn’t mean all 35 billion parameters are activated for every token. The A3B designation indicates roughly 3 billion active parameters per token. That sparse MoE architecture helps explain how a model of this size can deliver high generation speeds on modest hardware. CPU-based expert processing also helps work around the GPU’s limited memory, although the exact trade-offs depend on the workload. The 262K context claim is equally interesting. Supporting a context window that large doesn’t mean every request at that length will maintain the same speed. Prompt length, KV-cache memory, and CPU/GPU placement can all affect performance. The developer’s next test is particularly useful: graphing decode speed against context length. That should help show how performance changes as the prompt grows, rather than presenting one speed figure without the conditions behind it. If the coding benchmark also holds up, this could become a serious option for people who want capable local agents without investing in expensive GPUs. For now, the reported inference numbers are promising. The real test is whether Qwen3.8-35B-A3B can match or outperform Ornith-1.5-35B on actual agentic coding tasks. Fast local inference gets your attention. Reliable autonomous task completion is what earns a model a place in the stack.$

Someone is running Qwen3.8-35B-A3B on a single RTX 3060 with just 12GB of VRAM and 16GB of system RAM. And the reported numbers are surprisingly good. ~50 tok/s decode ~550 tok/s prefill 262K context window 12GB VRAM 16GB system RAM No multi-GPU setup. Just a consumer graphics card and a carefully configured local inference engine. The model is particularly interesting because it’s Qwen3.8 distilled into the Qwen3.6-35B-A3B architecture. That makes it worth investigating beyond raw inference speed. The developer currently uses Ornith-1.5 as the engine behind their Hermes and Pi agents. Now the question is whether this new model can deliver enough quality and reliability to replace it. The next step is testing it as an agent, not just a chatbot. The planned comparison includes four models: • Qwen3.8-35B-A3B • Ornith-1.5-35B • Qwen3.8-27B • Base Qwen3.6-35B-A3B An agentic coding benchmark should help reveal whether the distilled model can follow instructions, modify code, solve problems, and complete multi-step tasks effectively. That distinction matters because high token throughput doesn’t automatically translate into a better coding agent. A model can generate code quickly and still struggle with planning, debugging, tool calls, or completing a task without intervention. The hardware configuration is also worth examining. The developer is using llama-server with the Q4_K_M GGUF quantization, Flash Attention enabled, and 26 MoE layers assigned to CPU processing. The configuration also enables a 262,144-token context window, Q8 KV-cache quantization, and reasoning mode. This is how the setup is configured: llama-server \ -m Qwen3.8-35B-A3B-Q4_K_M.gguf \ -ngl 99 \ --n-cpu-moe 26 \ -c 262144 \ -fa on \ --jinja \ -np 1 \ --cache-type-k q8_0 \ --cache-type-v q8_0 \ --temp 0.6 \ --top-p 0.95 \ --top-k 20 \ --reasoning on One important detail: the model’s 35B parameter count doesn’t mean all 35 billion parameters are activated for every token. The A3B designation indicates roughly 3 billion active parameters per token. That sparse MoE architecture helps explain how a model of this size can deliver high generation speeds on modest hardware. CPU-based expert processing also helps work around the GPU’s limited memory, although the exact trade-offs depend on the workload. The 262K context claim is equally interesting. Supporting a context window that large doesn’t mean every request at that length will maintain the same speed. Prompt length, KV-cache memory, and CPU/GPU placement can all affect performance. The developer’s next test is particularly useful: graphing decode speed against context length. That should help show how performance changes as the prompt grows, rather than presenting one speed figure without the conditions behind it. If the coding benchmark also holds up, this could become a serious option for people who want capable local agents without investing in expensive GPUs. For now, the reported inference numbers are promising. The real test is whether Qwen3.8-35B-A3B can match or outperform Ornith-1.5-35B on actual agentic coding tasks. Fast local inference gets your attention. Reliable autonomous task completion is what earns a model a place in the stack.

FHILY👑

19,254 次观看 • 2 天前

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

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

vLLM

104,622 次观看 • 2 个月前

UC Berkeley just open-sourced FreeToken. (2–4x faster local LLM inference than Ollama) the results are wild: - Qwen3.6-35B on an 8GB GPU at 39.3 tokens/s - DeepSeek-V4-Flash 284B on a 32GB GPU at 22 tokens/s - GLM-5.2 753B on a 96GB GPU at 14.9 tokens/s a 35B model at 16-bit precision needs about 70GB just for its weights. even at 4 bits it is close to 18GB, and FreeToken serves it on an 8GB GPU. let me explain how: all three models mentioned above are Mixture-of-Experts, and that is what FreeToken takes advantage of. each layer holds hundreds of separate experts plus a small router that picks a few of them per token. Qwen3.6-35B activates roughly 3B of its 35B parameters per token. DeepSeek-V4-Flash picks 6 of 256 experts per layer, so 13B of its 284B run at a time. so compute was never the bottleneck. the weights a single step touches fit comfortably on a consumer GPU. every expert the router might pick still has to exist somewhere. they sit in system RAM, and the GPU keeps a cache of the ones the model has been using recently. so everything comes down to what happens when the router picks an expert that is not on the GPU. there are two ways to serve that miss: 1. copy it over PCIe and run it on the GPU 2. run it on the CPU, where it already lives both read from the same system memory, so they compete for one pool of bandwidth instead of adding to each other. existing engines pick one option and freeze it when the model loads. but routing changes on every token, so a fixed choice misses most of what the model asks for. FreeToken measures both bandwidths on your machine and splits each step's misses between the two paths in proportion. the GPU and CPU results then merge exactly, with no approximation. two machines with the same GPU can end up wanting opposite strategies, which I did not expect. a 5090 in a gaming desktop should push nearly everything over PCIe, while an 8GB laptop is better off computing most misses on the CPU. none of that is readable off a spec sheet, so the engine profiles it once per machine. the second half of the design is about agents. coding agents constantly rewrite their own history, and every edit normally forces thousands of tokens back through prefill. FreeToken saves its checkpoints at the exact boundaries agent frameworks cut on, so it only reprocesses the new part. its slowest first token stays under 44 seconds, while llama.cpp peaks at 232 and KTransformers at 946. it serves the OpenAI and Anthropic APIs under Apache 2.0, so Claude Code and Codex can point at it directly. releasing weights publicly decides who can download a model, not who can afford to run one. frontier open models keep shipping, and running them still assumes a rented cluster. meanwhile there are over a hundred million consumer machines with discrete GPUs sitting mostly idle. closing that gap was never a hardware problem, and work like this is what turns open weights into something you can actually use. paper: repo: almost every idea in this post, from why memory bandwidth decides the outcome to why moving weights costs more than computing on them, comes straight out of how a GPU is built. I wrote a detailed primer on that. the article is quoted below.

UC Berkeley just open-sourced FreeToken. (2–4x faster local LLM inference than Ollama) the results are wild: - Qwen3.6-35B on an 8GB GPU at 39.3 tokens/s - DeepSeek-V4-Flash 284B on a 32GB GPU at 22 tokens/s - GLM-5.2 753B on a 96GB GPU at 14.9 tokens/s a 35B model at 16-bit precision needs about 70GB just for its weights. even at 4 bits it is close to 18GB, and FreeToken serves it on an 8GB GPU. let me explain how: all three models mentioned above are Mixture-of-Experts, and that is what FreeToken takes advantage of. each layer holds hundreds of separate experts plus a small router that picks a few of them per token. Qwen3.6-35B activates roughly 3B of its 35B parameters per token. DeepSeek-V4-Flash picks 6 of 256 experts per layer, so 13B of its 284B run at a time. so compute was never the bottleneck. the weights a single step touches fit comfortably on a consumer GPU. every expert the router might pick still has to exist somewhere. they sit in system RAM, and the GPU keeps a cache of the ones the model has been using recently. so everything comes down to what happens when the router picks an expert that is not on the GPU. there are two ways to serve that miss: 1. copy it over PCIe and run it on the GPU 2. run it on the CPU, where it already lives both read from the same system memory, so they compete for one pool of bandwidth instead of adding to each other. existing engines pick one option and freeze it when the model loads. but routing changes on every token, so a fixed choice misses most of what the model asks for. FreeToken measures both bandwidths on your machine and splits each step's misses between the two paths in proportion. the GPU and CPU results then merge exactly, with no approximation. two machines with the same GPU can end up wanting opposite strategies, which I did not expect. a 5090 in a gaming desktop should push nearly everything over PCIe, while an 8GB laptop is better off computing most misses on the CPU. none of that is readable off a spec sheet, so the engine profiles it once per machine. the second half of the design is about agents. coding agents constantly rewrite their own history, and every edit normally forces thousands of tokens back through prefill. FreeToken saves its checkpoints at the exact boundaries agent frameworks cut on, so it only reprocesses the new part. its slowest first token stays under 44 seconds, while llama.cpp peaks at 232 and KTransformers at 946. it serves the OpenAI and Anthropic APIs under Apache 2.0, so Claude Code and Codex can point at it directly. releasing weights publicly decides who can download a model, not who can afford to run one. frontier open models keep shipping, and running them still assumes a rented cluster. meanwhile there are over a hundred million consumer machines with discrete GPUs sitting mostly idle. closing that gap was never a hardware problem, and work like this is what turns open weights into something you can actually use. paper: repo: almost every idea in this post, from why memory bandwidth decides the outcome to why moving weights costs more than computing on them, comes straight out of how a GPU is built. I wrote a detailed primer on that. the article is quoted below.

Akshay 🚀

344,809 次观看 • 1 个月前

🚨 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

🚨 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

AshutoshShrivastava

217,433 次观看 • 1 年前

Introducing OpenChatKit 🚀 - The first open-source alternative to ChatGPT! A team of ex-OpenAI fellows at Together have released a 20B chat-GPT model, fine-tuned for chat using EleutherAI's GPT-NeoX-20B, with over 43 million instructions under the Apache-2.0 license. >>>

Introducing OpenChatKit 🚀 - The first open-source alternative to ChatGPT! A team of ex-OpenAI fellows at Together have released a 20B chat-GPT model, fine-tuned for chat using EleutherAI's GPT-NeoX-20B, with over 43 million instructions under the Apache-2.0 license. >>>

Itamar Golan 🤓

555,549 次观看 • 3 年前

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.

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

84,618 次观看 • 1 年前

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. 🧵👇

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

1,183,506 次观看 • 3 个月前

🌍 What if an AI-generated world didn’t end after a few seconds—but kept running for hours, responded to every move, let you attack, cast spells, shoot, or summon storms, and continued evolving through AI agents? That’s LingBot-World 2.0: our open-source real-time interactive world model, scaling to 720p/60fps in our full setup. Today, we’re open-sourcing 3 more models: 🔹 LingBot-World 2.0 Small (1.3B) 🔹 LingBot-World 2.0 Bidirectional 🔹 LingBot-World 2.0 Causal Pretrain And now, with our 1.3B Small Model, that world can run in real time on a single consumer GPU. 🧵

🌍 What if an AI-generated world didn’t end after a few seconds—but kept running for hours, responded to every move, let you attack, cast spells, shoot, or summon storms, and continued evolving through AI agents? That’s LingBot-World 2.0: our open-source real-time interactive world model, scaling to 720p/60fps in our full setup. Today, we’re open-sourcing 3 more models: 🔹 LingBot-World 2.0 Small (1.3B) 🔹 LingBot-World 2.0 Bidirectional 🔹 LingBot-World 2.0 Causal Pretrain And now, with our 1.3B Small Model, that world can run in real time on a single consumer GPU. 🧵

Robbyant

1,998,495 次观看 • 1 个月前