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Serving GLM5.2 NVFP4 Agentic Workload with SGLang: How We Reached 500 TPS on 8xB300 at bs=1 In this deep dive, we explain how SGLang reaches 500+ tok/s/user at bs=1 on 8xB300, with 18 to 34% higher single-user interactivity within two weeks since day-0, and 6 to 11% better peak...

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LMSYS Org

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107,317 görüntüleme • 1 ay önce •via X (Twitter)

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GLM-5.3 weights from Z.ai are live, with SGLang powering day-0 serving support! GLM-5.3 inherits every optimization and feature we battle-tested for GLM-5.2 over the past months. On real-world multi-turn agentic workloads, we measured 537.6 tok/s/user on NVFP4 and 413 tok/s/user on FP8, at BS=1 with TP8 on 8x B300. It's fast, efficient, and production-ready today on NVIDIA AI Blackwell and Hopper, and AI at AMD MI300X/325X/355X. We believe this is a big step forward for GLM-Series in agentic tasks, with a path toward mythos-class cyber capability. Can't wait to see what people build with GLM-5.3 and SGLang🚀 Cookbook👇
0:20

GLM-5.3 weights from Z.ai are live, with SGLang powering day-0 serving support! GLM-5.3 inherits every optimization and feature we battle-tested for GLM-5.2 over the past months. On real-world multi-turn agentic workloads, we measured 537.6 tok/s/user on NVFP4 and 413 tok/s/user on FP8, at BS=1 with TP8 on 8x B300. It's fast, efficient, and production-ready today on NVIDIA AI Blackwell and Hopper, and AI at AMD MI300X/325X/355X. We believe this is a big step forward for GLM-Series in agentic tasks, with a path toward mythos-class cyber capability. Can't wait to see what people build with GLM-5.3 and SGLang🚀 Cookbook👇

SGLang

98,271 görüntüleme • 11 gün önce

Timelapse #156 (36 hrs) - Worked with the tiny corp on getting GLM 5.2 running on 8xMI300X (sglang won here) - Launched KernelBench-Mega and updated Kernelbench-Hard with h100 and b200 sweeps - Took care of boring business stuff - Did some training sweeps for specialized technical vocab audio model - Some bugs with putting kernelbench-mega and hard on cloud instances so had to do some reruns. Learned a lot though - Setting up my own local rl infra and profiling concurrency 128 rollouts with vllm. Became clear to me that I need to serve in nvfp4, use MoE only for throughput, reap so training doesn’t OOM, dig into the vllm kernel graph itself to not underutilize my hardware from poor flashinfer/cutlass selections for my rtx pro 6000 sm120 architecture - Might do online distillation from glm 5.2 but for now taking it one step at a time - Slept for a bit then woke up and showered - Fixed an issue with SGLang tensor parallel deadlock on GLM 5.2 architecture with MTP enabled - GLM 5.2 inference is 2-3x faster than coding plans and running on amd boxes - Spent time with family - Hung out with some friends - Recorded some yoctogpt lectures with the revamped notebook (high taste btw) - Setting up dflash training for GLM 5.2
0:30

Timelapse #156 (36 hrs) - Worked with the tiny corp on getting GLM 5.2 running on 8xMI300X (sglang won here) - Launched KernelBench-Mega and updated Kernelbench-Hard with h100 and b200 sweeps - Took care of boring business stuff - Did some training sweeps for specialized technical vocab audio model - Some bugs with putting kernelbench-mega and hard on cloud instances so had to do some reruns. Learned a lot though - Setting up my own local rl infra and profiling concurrency 128 rollouts with vllm. Became clear to me that I need to serve in nvfp4, use MoE only for throughput, reap so training doesn’t OOM, dig into the vllm kernel graph itself to not underutilize my hardware from poor flashinfer/cutlass selections for my rtx pro 6000 sm120 architecture - Might do online distillation from glm 5.2 but for now taking it one step at a time - Slept for a bit then woke up and showered - Fixed an issue with SGLang tensor parallel deadlock on GLM 5.2 architecture with MTP enabled - GLM 5.2 inference is 2-3x faster than coding plans and running on amd boxes - Spent time with family - Hung out with some friends - Recorded some yoctogpt lectures with the revamped notebook (high taste btw) - Setting up dflash training for GLM 5.2

Elliot Arledge

45,447 görüntüleme • 2 ay önce

📢 Qwen's Qwen3.8-2.4T-A95B weights just dropped, and SGLang already has day-0 support with DSpark. HF: Cookbook: 🖖 What kind of model can help optimize an inference engine? Qwen3.8-2.4T-A95B can take on complex engineering tasks, plan and iterate on its own, and keep going until the job is done. We even asked it to optimize its own serving on SGLang, and it ran unattended for 4.5 hours with verified performance improvements. Thanks to the Qwen team Qwen, NVIDIA AI, and AI at AMD for the close collaboration.
0:36

📢 Qwen's Qwen3.8-2.4T-A95B weights just dropped, and SGLang already has day-0 support with DSpark. HF: Cookbook: 🖖 What kind of model can help optimize an inference engine? Qwen3.8-2.4T-A95B can take on complex engineering tasks, plan and iterate on its own, and keep going until the job is done. We even asked it to optimize its own serving on SGLang, and it ran unattended for 4.5 hours with verified performance improvements. Thanks to the Qwen team Qwen, NVIDIA AI, and AI at AMD for the close collaboration.

SGLang

27,249 görüntüleme • 27 gün önce

SGLang now supports DSpark, enabling confidence-driven, variable-length verification for speculative decoding 🎉 DSpark addresses a key bottleneck under load: instead of verifying every draft token, it verifies only where the draft model is confident, so the gains hold even as batch size scales. We heavily optimized variable-length verification in SGLang. Across batch sizes 1 to 256, DSpark gives the best throughput/latency tradeoff on DeepSeek-V4-Flash, ahead of both MTP and non-spec. At high concurrency, dynamic scheduling provides up to ~20% higher throughput compared to a fixed budget, while maintaining high verification quality across workloads. With fused kernels and zero-overhead scheduling, DeepSeek-V4-Pro reaches 383.7 tok/s at B=1 on B300. DSpark is now available in SGLang with support for Qwen3 and DeepSeek-V4. Thanks DeepSeek for open-sourcing! Blog with full technical details and commands to run below 👇
0:33

SGLang now supports DSpark, enabling confidence-driven, variable-length verification for speculative decoding 🎉 DSpark addresses a key bottleneck under load: instead of verifying every draft token, it verifies only where the draft model is confident, so the gains hold even as batch size scales. We heavily optimized variable-length verification in SGLang. Across batch sizes 1 to 256, DSpark gives the best throughput/latency tradeoff on DeepSeek-V4-Flash, ahead of both MTP and non-spec. At high concurrency, dynamic scheduling provides up to ~20% higher throughput compared to a fixed budget, while maintaining high verification quality across workloads. With fused kernels and zero-overhead scheduling, DeepSeek-V4-Pro reaches 383.7 tok/s at B=1 on B300. DSpark is now available in SGLang with support for Qwen3 and DeepSeek-V4. Thanks DeepSeek for open-sourcing! Blog with full technical details and commands to run below 👇

LMSYS Org

170,303 görüntüleme • 2 ay önce

SGLang day-0 speed on Kimi K3: 423 tok/s (measured on gsm8k), plus RL support ready in Miles RadixArk! How the largest open-source model runs this fast: we natively implemented and deeply optimized K3’s new architecture with fused KDA decode kernels, DP attention, DSpark, PD disagg, and KDA-aware prefix caching. We've passed Kimi Vendor Verifier and are ready for production! Thanks to Kimi.ai, NVIDIA, AMD, KVCache.AI, Modal, and Baseten for building this with us, and to Google Cloud, Nebius Token Factory, fal, DigitalOcean, Runpod, DeepInfra and GMI Cloud for serving K3 on SGLang. Blog, cookbook, benchmarks in the comments. P.S. This demo video? Kimi K3 made it itself. Play the game 👇
0:52

SGLang day-0 speed on Kimi K3: 423 tok/s (measured on gsm8k), plus RL support ready in Miles RadixArk! How the largest open-source model runs this fast: we natively implemented and deeply optimized K3’s new architecture with fused KDA decode kernels, DP attention, DSpark, PD disagg, and KDA-aware prefix caching. We've passed Kimi Vendor Verifier and are ready for production! Thanks to Kimi.ai, NVIDIA, AMD, KVCache.AI, Modal, and Baseten for building this with us, and to Google Cloud, Nebius Token Factory, fal, DigitalOcean, Runpod, DeepInfra and GMI Cloud for serving K3 on SGLang. Blog, cookbook, benchmarks in the comments. P.S. This demo video? Kimi K3 made it itself. Play the game 👇

LMSYS Org

204,539 görüntüleme • 1 ay önce

🎉 Congrats to MiniMax (official) on releasing MiniMax M3! Frontier coding and agentic capabilities, native image and video input, computer use, and a 1M-token context window, all in a single open model. At the heart of M3 is MSA, a new sparse attention architecture: instead of attending densely over the full KV cache, each query scores 128-token KV blocks and runs attention only over the top blocks. That is what makes 1M-token context practical to serve. M3 runs in vLLM with day-0 support, verified on NVIDIA and AMD hardware: ✨ MSA sparse attention with dedicated prefill and decode kernels ✨ 1M-token context serving with prefix caching and chunked prefill ✨ BF16 and MXFP8 checkpoints, with MoE backends for both Hopper and Blackwell ✨ Native multimodal input (image + video) ✨ Tool calling, reasoning parsing, and thinking-mode control for agent workloads Day-0 support like this is a true team effort. Grateful to the teams at MiniMax (official), NVIDIA AI, AI at AMD, and Inferact, and to the vLLM community for making it happen. 🙏 Deep dive into the implementation, kernel work, and deployment recipes: 🔗
0:21

🎉 Congrats to MiniMax (official) on releasing MiniMax M3! Frontier coding and agentic capabilities, native image and video input, computer use, and a 1M-token context window, all in a single open model. At the heart of M3 is MSA, a new sparse attention architecture: instead of attending densely over the full KV cache, each query scores 128-token KV blocks and runs attention only over the top blocks. That is what makes 1M-token context practical to serve. M3 runs in vLLM with day-0 support, verified on NVIDIA and AMD hardware: ✨ MSA sparse attention with dedicated prefill and decode kernels ✨ 1M-token context serving with prefix caching and chunked prefill ✨ BF16 and MXFP8 checkpoints, with MoE backends for both Hopper and Blackwell ✨ Native multimodal input (image + video) ✨ Tool calling, reasoning parsing, and thinking-mode control for agent workloads Day-0 support like this is a true team effort. Grateful to the teams at MiniMax (official), NVIDIA AI, AI at AMD, and Inferact, and to the vLLM community for making it happen. 🙏 Deep dive into the implementation, kernel work, and deployment recipes: 🔗

vLLM

40,306 görüntüleme • 2 ay önce

Laguna S 2.1 is built to run locally. We heard the ask for a serious open model in the 80–120B range. Laguna S 2.1 is our answer, and we want it to redefine what this size class can do. Weights ship in BF16, FP8, INT4 and NVFP4, with official GGUF and MLX conversions, plus official DFlash draft models for faster inference. Day-one support across llama.cpp, ollama, Valerie Lee Make Up, SGLang, NVIDIA TRT-LLM, Hugging Face transformers and ZML. Watch it run on an NVIDIA AI DGX Spark.
0:39

Laguna S 2.1 is built to run locally. We heard the ask for a serious open model in the 80–120B range. Laguna S 2.1 is our answer, and we want it to redefine what this size class can do. Weights ship in BF16, FP8, INT4 and NVFP4, with official GGUF and MLX conversions, plus official DFlash draft models for faster inference. Day-one support across llama.cpp, ollama, Valerie Lee Make Up, SGLang, NVIDIA TRT-LLM, Hugging Face transformers and ZML. Watch it run on an NVIDIA AI DGX Spark.

Poolside

31,242 görüntüleme • 1 ay önce

I love using GLM 5.2 for web app iteration. My workflow: generate 6 variations, then pick the best one and continue iterating on it. I built Recast to make this even easier. Give it a prompt, get 6 variations, download the code for your favorite version, then continue iterating in your own coding agent. GLM 5.2 works really well for this use case since it's fast, cheap, and great at design. On average, you can make 3-6 landing pages with GLM 5.2 for every one you can generate with Opus 4.8. And the design variety of the GLM landing pages helps a lot with trying to think through what style you want to go with. Recast is launching soon (as a web app + skill).
0:34

I love using GLM 5.2 for web app iteration. My workflow: generate 6 variations, then pick the best one and continue iterating on it. I built Recast to make this even easier. Give it a prompt, get 6 variations, download the code for your favorite version, then continue iterating in your own coding agent. GLM 5.2 works really well for this use case since it's fast, cheap, and great at design. On average, you can make 3-6 landing pages with GLM 5.2 for every one you can generate with Opus 4.8. And the design variety of the GLM landing pages helps a lot with trying to think through what style you want to go with. Recast is launching soon (as a web app + skill).

Hassan

20,164 görüntüleme • 2 ay önce

What's next: CoRover Agentic AI Studio! The best of the world's AI in one platform - coming soon! This is evolution in action! 🌐 Our goal is to help businesses and society at scale, and we'll keep pushing the boundaries to achieve it. Dive into the world of Conversational Agentic AI with CoRover AI! This 15-minute video takes you on a journey featuring testimonials, demos, and a sneak peek at the power of BharatGPT - India's first sovereign AI LLM. And now, find out... What's next for CoRover? 🤔 Watch till the end to know! 🚀 𝐂𝐨𝐑𝐨𝐯𝐞𝐫.𝐚𝐢: 𝐀𝐈 𝐰𝐢𝐭𝐡 𝐏𝐮𝐫𝐩𝐨𝐬𝐞 & 𝐓𝐫𝐮𝐬𝐭!
14:58

What's next: CoRover Agentic AI Studio! The best of the world's AI in one platform - coming soon! This is evolution in action! 🌐 Our goal is to help businesses and society at scale, and we'll keep pushing the boundaries to achieve it. Dive into the world of Conversational Agentic AI with CoRover AI! This 15-minute video takes you on a journey featuring testimonials, demos, and a sneak peek at the power of BharatGPT - India's first sovereign AI LLM. And now, find out... What's next for CoRover? 🤔 Watch till the end to know! 🚀 𝐂𝐨𝐑𝐨𝐯𝐞𝐫.𝐚𝐢: 𝐀𝐈 𝐰𝐢𝐭𝐡 𝐏𝐮𝐫𝐩𝐨𝐬𝐞 & 𝐓𝐫𝐮𝐬𝐭!

Ankush Sabharwal

536,079 görüntüleme • 7 ay önce

Learn how to build an optimized LLM inference system from the ground up in our new short course, Efficiently Serving LLMs, built in collaboration with Predibase by Rubrik and taught by Travis Addair. Whether you're serving your own LLM or using a model hosting service, this course will give you a deep understanding of the optimizations required to efficiently serve many users at once. - Learn how LLMs generate text one token at a time, and how techniques like KV caching, continuous batching, and quantization speed things up and optimize memory usage for serving multiple users. - Benchmark the performance of these LLM optimizations to explore the trade-offs between quickly responding to an individual user’s request vs. serving many users at once. - Use techniques like low-rank adaptation (LoRA) to efficiently serve hundreds of unique, custom fine-tuned models on a single device, without sacrificing throughput. - Use Predibase's LoRAX framework to see optimization techniques in action on a real LLM server. Sign up here:
2:57

Learn how to build an optimized LLM inference system from the ground up in our new short course, Efficiently Serving LLMs, built in collaboration with Predibase by Rubrik and taught by Travis Addair. Whether you're serving your own LLM or using a model hosting service, this course will give you a deep understanding of the optimizations required to efficiently serve many users at once. - Learn how LLMs generate text one token at a time, and how techniques like KV caching, continuous batching, and quantization speed things up and optimize memory usage for serving multiple users. - Benchmark the performance of these LLM optimizations to explore the trade-offs between quickly responding to an individual user’s request vs. serving many users at once. - Use techniques like low-rank adaptation (LoRA) to efficiently serve hundreds of unique, custom fine-tuned models on a single device, without sacrificing throughput. - Use Predibase's LoRAX framework to see optimization techniques in action on a real LLM server. Sign up here:

Andrew Ng

104,727 görüntüleme • 2 yıl önce

Episode 10 of How I AI is my personal favorite so far: we talk to John Blackman, 91 years old, about using AI to code a web app for the first time in his life. John and his grandson Brandon show us: - Claude for writing user stories - Replit agents for coding a HUGE volunteer support app - the challenges of going from dev to production - user feedback for Anthropic and Replit ⠕ as a beginner If you’ve been sitting on the sidelines, this is your call to action! No excuses, at any age. As always, thanks to our fab sponsors! 💼 WorkOS: make your app enterprise ready today 🔀 Orkes: the platform for reliable apps and agentic workflows
1:17

Episode 10 of How I AI is my personal favorite so far: we talk to John Blackman, 91 years old, about using AI to code a web app for the first time in his life. John and his grandson Brandon show us: - Claude for writing user stories - Replit agents for coding a HUGE volunteer support app - the challenges of going from dev to production - user feedback for Anthropic and Replit ⠕ as a beginner If you’ve been sitting on the sidelines, this is your call to action! No excuses, at any age. As always, thanks to our fab sponsors! 💼 WorkOS: make your app enterprise ready today 🔀 Orkes: the platform for reliable apps and agentic workflows

claire vo 🖤

138,420 görüntüleme • 1 yıl önce

“This is a problem that begins and ends with men.” In the last part of our special series on violence against women, we hear from the men who are leading the way and calling on others to stand up. Watch #9News at 6pm to find out how you can help keep women safe.
0:54

“This is a problem that begins and ends with men.” In the last part of our special series on violence against women, we hear from the men who are leading the way and calling on others to stand up. Watch #9News at 6pm to find out how you can help keep women safe.

9News Adelaide

11,187 görüntüleme • 2 yıl önce

Inkling, Thinking Machines' first open model, dropped today: 975B total / 41B active MoE, up to 1M context, reasoning natively over text, images, and audio. Serving and RL support are already live: you can run and shape it on an open stack, starting now. Day 0 support on SGLang SGLang and Miles RadixArk👇 - Inkling's new architecture (ShortConv, attention with relative positional embedding, shared expert sink MoE) is natively implemented and deeply optimized, with prefill full CUDA graph and MXFP8 KV cache - Full parameter and LoRA RL in a customized Megatron backend, train inference consistency via customized kernels, routing replay, and cross-runtime parameter synchronization - DFlash speculative decoding from Modal for low-latency serving Launching now, blog and cookbook in the comments ⬇️
1:01

Inkling, Thinking Machines' first open model, dropped today: 975B total / 41B active MoE, up to 1M context, reasoning natively over text, images, and audio. Serving and RL support are already live: you can run and shape it on an open stack, starting now. Day 0 support on SGLang SGLang and Miles RadixArk👇 - Inkling's new architecture (ShortConv, attention with relative positional embedding, shared expert sink MoE) is natively implemented and deeply optimized, with prefill full CUDA graph and MXFP8 KV cache - Full parameter and LoRA RL in a customized Megatron backend, train inference consistency via customized kernels, routing replay, and cross-runtime parameter synchronization - DFlash speculative decoding from Modal for low-latency serving Launching now, blog and cookbook in the comments ⬇️

LMSYS Org

146,186 görüntüleme • 1 ay önce

GLM 4.7 is one of the top open-source models on LM Arena—and it's going toe-to-toe with Claude Opus 4.5 and Gemini Pro. We sat down with Anastasios Nikolas Angelopoulos, co-founder and CEO of Arena.ai, to break down 8,000+ developer votes: → Within 30 points of Gemini Pro in math & coding → Frontier-level multi-turn & instruction following → The open-source model devs are actually switching to The best part? You can run it at 1,500+ tokens/sec on Cerebras—for free.
4:14

GLM 4.7 is one of the top open-source models on LM Arena—and it's going toe-to-toe with Claude Opus 4.5 and Gemini Pro. We sat down with Anastasios Nikolas Angelopoulos, co-founder and CEO of Arena.ai, to break down 8,000+ developer votes: → Within 30 points of Gemini Pro in math & coding → Frontier-level multi-turn & instruction following → The open-source model devs are actually switching to The best part? You can run it at 1,500+ tokens/sec on Cerebras—for free.

Cerebras

28,515 görüntüleme • 7 ay önce

We've just launched beta for Solana Vault Standard! The first step of Superteam Brasil roadmap of a common, OS, tooling suite to help out the day to day of every type of Solana developer 💻 Our goal is to enable builders to fork and rely on audited code already, similar to how evm devs rely on OpenZeppelin packages - but ready for the agentic world and with more expansive UX 🚀 With SVS, you can create, configure, manage, monitor, audit and power up your yield bearing, compliant and/or private tokenized vault in a single sdk. Check it out at
1:14

We've just launched beta for Solana Vault Standard! The first step of Superteam Brasil roadmap of a common, OS, tooling suite to help out the day to day of every type of Solana developer 💻 Our goal is to enable builders to fork and rely on audited code already, similar to how evm devs rely on OpenZeppelin packages - but ready for the agentic world and with more expansive UX 🚀 With SVS, you can create, configure, manage, monitor, audit and power up your yield bearing, compliant and/or private tokenized vault in a single sdk. Check it out at

kaue

12,014 görüntüleme • 6 ay önce

Robert Saleh was asked on a s scale of 1-10, how much does he want to be a head coach again: “We all want to get to the top of our profession. But honestly, I’m just in this space right now where I’m solely focused on trying to do my best for the guys here. It’s not a secret, we all want to at the top of our profession and show that we can achieve greatness at the top of our profession, but I say this with great humility, my focus is here and just trying our best to get to the playoffs and see if we can make some damage and host a Super Bowl here in our building.” via: JosinaAnderson
0:42

Robert Saleh was asked on a s scale of 1-10, how much does he want to be a head coach again: “We all want to get to the top of our profession. But honestly, I’m just in this space right now where I’m solely focused on trying to do my best for the guys here. It’s not a secret, we all want to at the top of our profession and show that we can achieve greatness at the top of our profession, but I say this with great humility, my focus is here and just trying our best to get to the playoffs and see if we can make some damage and host a Super Bowl here in our building.” via: JosinaAnderson

Coach Yac 🗣

160,001 görüntüleme • 9 ay önce

| $SGT Latest Update is LIVE! At we're dedicated to building an all-in-one DeFi ecosystem that covers everything from the hottest crypto trends and breaking news to top performers and in-depth analysis of call groups and influencer signals. Our latest update is designed to enhance your user experience, making it easier to navigate through our utilities and access critical insights. Here’s What’s New: 🔍 Search Bar: for tokens mentioned on our platform by name or contract hash, streamlining your access to key data. 📊 Token Profile Pages: with essential metrics such as Market Cap, Volume, Price, and Performance Charts. A detailed table at the bottom shows which influencers mentioned the token and how it performed afterward. 📈 Top 5 Visited Tokens: Check out the most viewed tokens on our platform. This new table highlights the tokens generating the most user interest, helping you stay ahead of the trends. 💻 Telegram App & Bots: With our focus on Telegram, we've added dedicated pages for our app and bots, making it easier to explore their features and utilities as we continue to expand our offerings. ⚡️ UI & Speed Enhancements: The platform has been optimized for a faster, smoother experience, with an improved user interface for better navigation. 📺 Watch the full tutorial: Join our community for more news and updates! 🌐 Visit us at :
3:51

| $SGT Latest Update is LIVE! At we're dedicated to building an all-in-one DeFi ecosystem that covers everything from the hottest crypto trends and breaking news to top performers and in-depth analysis of call groups and influencer signals. Our latest update is designed to enhance your user experience, making it easier to navigate through our utilities and access critical insights. Here’s What’s New: 🔍 Search Bar: for tokens mentioned on our platform by name or contract hash, streamlining your access to key data. 📊 Token Profile Pages: with essential metrics such as Market Cap, Volume, Price, and Performance Charts. A detailed table at the bottom shows which influencers mentioned the token and how it performed afterward. 📈 Top 5 Visited Tokens: Check out the most viewed tokens on our platform. This new table highlights the tokens generating the most user interest, helping you stay ahead of the trends. 💻 Telegram App & Bots: With our focus on Telegram, we've added dedicated pages for our app and bots, making it easier to explore their features and utilities as we continue to expand our offerings. ⚡️ UI & Speed Enhancements: The platform has been optimized for a faster, smoother experience, with an improved user interface for better navigation. 📺 Watch the full tutorial: Join our community for more news and updates! 🌐 Visit us at :

ShillGuard $SGT

16,239 görüntüleme • 1 yıl önce