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another 5 days later, zml/llmd runs fully on Metal, serving 8 simultaneous requests at full bf16 zml/llmd is our LLM serving software that all the modern niceties (continuous batching etc..) ps: "full bf16", what a time to be alive

Steeve Morin

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27,540 Aufrufe • vor 2 Monaten •via X (Twitter)

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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.

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

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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:

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

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Next mlx-vlm release will ship with continuous batching support on the server 🚀 What's coming: → Continuous batching — new requests join the active batch immediately, no waiting. Mixed image + text batches supported → OpenAI-compatible API — field-for-field match with mlx-lm, reasoning/content split for thinking models, tag-aware streaming → Multi-turn tool calling — full tool use support across streaming and non-streaming, works with Gemma4 and other templates → Vision feature caching — cache image embeddings across turns. Gemma4: 228x speedup, Qwen3.5: 23x on cache hit All running locally on Apple Silicon. Check our this demo running 4 concurrent requests (mixed image + text) to gemma-4-26B-A4B-IT by Google Gemma in bf16 using Pi + MLX-VLM server on my M3 Ultra. One of the requests ingests a 8K resolution image!

Next mlx-vlm release will ship with continuous batching support on the server 🚀 What's coming: → Continuous batching — new requests join the active batch immediately, no waiting. Mixed image + text batches supported → OpenAI-compatible API — field-for-field match with mlx-lm, reasoning/content split for thinking models, tag-aware streaming → Multi-turn tool calling — full tool use support across streaming and non-streaming, works with Gemma4 and other templates → Vision feature caching — cache image embeddings across turns. Gemma4: 228x speedup, Qwen3.5: 23x on cache hit All running locally on Apple Silicon. Check our this demo running 4 concurrent requests (mixed image + text) to gemma-4-26B-A4B-IT by Google Gemma in bf16 using Pi + MLX-VLM server on my M3 Ultra. One of the requests ingests a 8K resolution image!

Prince Canuma

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Yesterday, I asked the Government whether grooming gang perpetrators would serve their full sentences. The minister seemed to say that we should be grateful that these people are serving prison sentences *at all*. What on earth?!

Yesterday, I asked the Government whether grooming gang perpetrators would serve their full sentences. The minister seemed to say that we should be grateful that these people are serving prison sentences at all. What on earth?!

Katie Lam

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Continuous batching in LLMs, clearly explained: (a popular LLM interview question; bookmark this) In traditional ML inference, a batch is a matrix. Every input is padded to the same length, one forward pass runs, and every row finishes at the same moment. LLM decoding does not work that way. One forward pass produces one token per sequence, so a request needs as many passes as it has output tokens, and nobody knows that count until the model emits a stop token. Under static batching, membership is fixed when the batch starts. A request that finishes in 30 tokens holds its slot until the slowest request in the same batch finishes at 400. The GPU keeps paying the full weight read for a batch that is mostly empty. Loading model weights out of HBM costs the same whether four slots are producing tokens or one. Continuous batching moves the decision boundary. Instead of scheduling once per batch, the scheduler runs a single forward pass, gets control back, and decides again. A finished request leaves at the next iteration boundary, and a queued request takes its slot right there. No slot stays reserved for work that is already done. Anyscale benchmarked both OPT-13B on a single A100. With uniform generation lengths, the two policies came out about level (as expected), and as output length variance rose, static batching fell to around 81 tokens per second while vLLM reached 23x the throughput of naive Hugging Face serving. Variance drives the entire gap. Production traffic mixes 30-token replies with 400-token ones, which is exactly the condition static batching handles worst. None of this alters the model. vLLM, SGLang, TGI, and TensorRT-LLM all run it by default, and NVIDIA ships the same mechanism under the name in-flight batching. The animation below runs both policies on the same 16 requests and the same 4 slots, stepping in lockstep. The only difference is when a new request is allowed in. To dive deeper into continuous batching specifically, I wrote a full breakdown of the scheduler underneath it. It covers what happens between two forward passes, how tokens get handed out against a fixed budget, why the scheduler needs no separate path for prefill and decode, and what preemption costs you when the KV cache fills up mid-generation. Read it below.

Continuous batching in LLMs, clearly explained: (a popular LLM interview question; bookmark this) In traditional ML inference, a batch is a matrix. Every input is padded to the same length, one forward pass runs, and every row finishes at the same moment. LLM decoding does not work that way. One forward pass produces one token per sequence, so a request needs as many passes as it has output tokens, and nobody knows that count until the model emits a stop token. Under static batching, membership is fixed when the batch starts. A request that finishes in 30 tokens holds its slot until the slowest request in the same batch finishes at 400. The GPU keeps paying the full weight read for a batch that is mostly empty. Loading model weights out of HBM costs the same whether four slots are producing tokens or one. Continuous batching moves the decision boundary. Instead of scheduling once per batch, the scheduler runs a single forward pass, gets control back, and decides again. A finished request leaves at the next iteration boundary, and a queued request takes its slot right there. No slot stays reserved for work that is already done. Anyscale benchmarked both OPT-13B on a single A100. With uniform generation lengths, the two policies came out about level (as expected), and as output length variance rose, static batching fell to around 81 tokens per second while vLLM reached 23x the throughput of naive Hugging Face serving. Variance drives the entire gap. Production traffic mixes 30-token replies with 400-token ones, which is exactly the condition static batching handles worst. None of this alters the model. vLLM, SGLang, TGI, and TensorRT-LLM all run it by default, and NVIDIA ships the same mechanism under the name in-flight batching. The animation below runs both policies on the same 16 requests and the same 4 slots, stepping in lockstep. The only difference is when a new request is allowed in. To dive deeper into continuous batching specifically, I wrote a full breakdown of the scheduler underneath it. It covers what happens between two forward passes, how tokens get handed out against a fixed budget, why the scheduler needs no separate path for prefill and decode, and what preemption costs you when the KV cache fills up mid-generation. Read it below.

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Today I celebrated Thanksgiving by serving meals to Coloradans in need at the Denver Rescue Mission. Thanksgiving is a time for good food, friends, and family, and I encourage all Coloradans to reflect on what they are thankful for and lend a helping hand to our fellow Coloradans.

Today I celebrated Thanksgiving by serving meals to Coloradans in need at the Denver Rescue Mission. Thanksgiving is a time for good food, friends, and family, and I encourage all Coloradans to reflect on what they are thankful for and lend a helping hand to our fellow Coloradans.

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Here's where feminism lied to you! It told you that serving is slavery; that your role as a wife, being a helper to your husband: that's bondage. Since when did we take on the demonic idea that serving is slavery. SERVING IS CHRISTLIKE. "For even the Son of Man did not come to be served, but to serve, and to give his life as a ransom for many" Mark 10:45 Paul introduced himself as a "doulos" AKA a SERVANT of God. Husbands and wives are both called to serve, but our serving looks DIFFERENT. The husband serves us by providing and protecting. They serve by carrying the ultimate responsibility of making sure there's food on the table every day. They serve us by protecting us from evil - both physical evil and evil ideas. They protect us against false ideas. They are responsible for the spiritual condition of the home. Wives are called to serve by honoring his leadership, showing gratitude, creating a house full of peace of warmth, putting food in his belly and raising children. One way of serving is not inferior to the other. Let's JOYFULLY serve our husbands. SERVING ISN'T SLAVERY... SERVING IS A PRIVILEGE. LET'S REPLACE COMPLAINING WITH GRATITUDE. IT'S AN HONOR TO SERVE. IT'S AN HONOR TO BE MY HUSBAND'S HELPER. WHEN I SERVE MY HUSBAND, I HONOR GOD.

Here's where feminism lied to you! It told you that serving is slavery; that your role as a wife, being a helper to your husband: that's bondage. Since when did we take on the demonic idea that serving is slavery. SERVING IS CHRISTLIKE. "For even the Son of Man did not come to be served, but to serve, and to give his life as a ransom for many" Mark 10:45 Paul introduced himself as a "doulos" AKA a SERVANT of God. Husbands and wives are both called to serve, but our serving looks DIFFERENT. The husband serves us by providing and protecting. They serve by carrying the ultimate responsibility of making sure there's food on the table every day. They serve us by protecting us from evil - both physical evil and evil ideas. They protect us against false ideas. They are responsible for the spiritual condition of the home. Wives are called to serve by honoring his leadership, showing gratitude, creating a house full of peace of warmth, putting food in his belly and raising children. One way of serving is not inferior to the other. Let's JOYFULLY serve our husbands. SERVING ISN'T SLAVERY... SERVING IS A PRIVILEGE. LET'S REPLACE COMPLAINING WITH GRATITUDE. IT'S AN HONOR TO SERVE. IT'S AN HONOR TO BE MY HUSBAND'S HELPER. WHEN I SERVE MY HUSBAND, I HONOR GOD.

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Another Mountain is about to erupt 🌋 "Hello Mhesh, I'm also from Mt. Elgon, and I fully endorse the statement you read on the floor of the House. We need policies that truly serve the people, not systems that allow a few individuals to act at their own convenience. I appreciate the work you do and your commitment to oversight. Please continue standing for what is right and serving with integrity". #FreeEducationForAll

Another Mountain is about to erupt 🌋 "Hello Mhesh, I'm also from Mt. Elgon, and I fully endorse the statement you read on the floor of the House. We need policies that truly serve the people, not systems that allow a few individuals to act at their own convenience. I appreciate the work you do and your commitment to oversight. Please continue standing for what is right and serving with integrity". #FreeEducationForAll

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What a time to be alive! Hard to believe with everything that's gone on there was a sleaze probe vote at the start of the week. That was before we even knew about our Ang getting trollied. Luckily for the PM, all his favourite Us Labours came through for him:

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Intel Lady

22,540 Aufrufe • vor 3 Monaten

North Carolina HC Scott Forbes on what this Tar Heels team means to him. “I couldn't be prouder of a group. I just talked to them in the locker room. We talk all the time about service, about serving one another. I don't tell them this, but I write it down every day for myself, in Galatians 5:13: True service, serving one another through love, humbly in love and how powerful love is. This group loved each other all season. They took us on a ride that was unbelievable. We came up a little bit short. But I would take that ride again every day of the year.” “The thing I'm going to miss the most is just being with them. That's what I'm going to miss the most. Obviously, it gets emotional back there. But I'm thankful, too. I'm thankful that I've been given the opportunity. It's an honor and a privilege to be the head coach at University of North Carolina. I don't take that lightly.”

North Carolina HC Scott Forbes on what this Tar Heels team means to him. “I couldn't be prouder of a group. I just talked to them in the locker room. We talk all the time about service, about serving one another. I don't tell them this, but I write it down every day for myself, in Galatians 5:13: True service, serving one another through love, humbly in love and how powerful love is. This group loved each other all season. They took us on a ride that was unbelievable. We came up a little bit short. But I would take that ride again every day of the year.” “The thing I'm going to miss the most is just being with them. That's what I'm going to miss the most. Obviously, it gets emotional back there. But I'm thankful, too. I'm thankful that I've been given the opportunity. It's an honor and a privilege to be the head coach at University of North Carolina. I don't take that lightly.”

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NVIDIA AI Releases Nemotron 3 Embed: An Open Embedding Collection Whose 8B Checkpoint Ranks #1 on RTEB Most RAG stacks treat the embedding model as a commodity — pick one, index, move on. Nemotron 3 Embed is NVIDIA's argument that the retrieval layer is where agent cost actually gets set. They released three open checkpoints — Nemotron-3-Embed-8B-BF16, 1B-BF16, and 1B-NVFP4 — built on Ministral bases, trained with bidirectional attention masking, pooled by averaging token-level representations, all taking 32,768-token inputs under OpenMDW-1.1. Here's what's actually interesting: → The 8B ranks #1 overall on RTEB: 78.46 avg NDCG@10, alongside 75.45 on MMTEB Retrieval and 60.60 on ViDoRe-V3 text → The 1B wasn't trained small. It was pruned from a 3B parent with ModelOpt mcore_minitron NAS, then distilled from the 8B teacher on COS + MSE loss — twice → That pipeline lands the 1.14B checkpoint at 72.38 RTEB, up 10.4 points on the prior-generation llama-nemotron-embed-vl-1b-v2 → NVFP4 costs 0.38 RTEB points (72.00 vs 72.38, ~99.5% retention) and buys up to 2x BF16 throughput on Blackwell Full Analysis: Model weight: Technical details: NVIDIA AI NVIDIA

NVIDIA AI Releases Nemotron 3 Embed: An Open Embedding Collection Whose 8B Checkpoint Ranks #1 on RTEB Most RAG stacks treat the embedding model as a commodity — pick one, index, move on. Nemotron 3 Embed is NVIDIA's argument that the retrieval layer is where agent cost actually gets set. They released three open checkpoints — Nemotron-3-Embed-8B-BF16, 1B-BF16, and 1B-NVFP4 — built on Ministral bases, trained with bidirectional attention masking, pooled by averaging token-level representations, all taking 32,768-token inputs under OpenMDW-1.1. Here's what's actually interesting: → The 8B ranks #1 overall on RTEB: 78.46 avg NDCG@10, alongside 75.45 on MMTEB Retrieval and 60.60 on ViDoRe-V3 text → The 1B wasn't trained small. It was pruned from a 3B parent with ModelOpt mcore_minitron NAS, then distilled from the 8B teacher on COS + MSE loss — twice → That pipeline lands the 1.14B checkpoint at 72.38 RTEB, up 10.4 points on the prior-generation llama-nemotron-embed-vl-1b-v2 → NVFP4 costs 0.38 RTEB points (72.00 vs 72.38, ~99.5% retention) and buys up to 2x BF16 throughput on Blackwell Full Analysis: Model weight: Technical details: NVIDIA AI NVIDIA

Marktechpost AI

32,352 Aufrufe • vor 1 Monat

🎉 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: 🔗

🎉 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: 🔗

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[WATCH] I would not want to be a part of people who are going to destabilize our movement. When I am done serving in my current role, I want to look back and say that we came in during a very difficult time, and now the ANC is back, and we are witnessing its rise again. Full interview: #LetsDoMoreTogether

[WATCH] I would not want to be a part of people who are going to destabilize our movement. When I am done serving in my current role, I want to look back and say that we came in during a very difficult time, and now the ANC is back, and we are witnessing its rise again. Full interview: #LetsDoMoreTogether

ANC SECRETARY GENERAL | Fikile Mbalula

76,999 Aufrufe • vor 2 Jahren

This guy installed a personal Nvidia AI data center on his desk and now runs 7 agents 24/7 for $0/month DGX Spark on a desk = $0 per token, forever. The 7 agents now running on it: → Morning brief agent - reads every source, drops 400 words at 6am → Coding agent - runs 24/7 against the private repo, code never leaves the machine → Document Q&A on NDA files he'd never paste into a public model → Hourly competitive intel sweep across 30+ sources → Weekly 70B fine-tune on his own dataset → Always-on local voice assistant → Sunday archive re-processor that re-runs the entire knowledge base under a new prompt Each line above was a $200-500/month API charge. All of it runs on $3,248 of owned hardware now. The numbers: → Tier 1 ($249) - every "small enough for 8GB" task → Tier 2 ($2,999) - 70B at full BF16, 405B at Q3, fine-tuning → $3,248 one-time, full stack → 6 weeks to payback → $0/month forever after The unlock isn't the savings. It's that he stopped rationing. Cloud pricing teaches you to count tokens. Owning the box teaches you to ship the agent you've been "considering" for 6 months. Full breakdown 👇

This guy installed a personal Nvidia AI data center on his desk and now runs 7 agents 24/7 for $0/month DGX Spark on a desk = $0 per token, forever. The 7 agents now running on it: → Morning brief agent - reads every source, drops 400 words at 6am → Coding agent - runs 24/7 against the private repo, code never leaves the machine → Document Q&A on NDA files he'd never paste into a public model → Hourly competitive intel sweep across 30+ sources → Weekly 70B fine-tune on his own dataset → Always-on local voice assistant → Sunday archive re-processor that re-runs the entire knowledge base under a new prompt Each line above was a $200-500/month API charge. All of it runs on $3,248 of owned hardware now. The numbers: → Tier 1 ($249) - every "small enough for 8GB" task → Tier 2 ($2,999) - 70B at full BF16, 405B at Q3, fine-tuning → $3,248 one-time, full stack → 6 weeks to payback → $0/month forever after The unlock isn't the savings. It's that he stopped rationing. Cloud pricing teaches you to count tokens. Owning the box teaches you to ship the agent you've been "considering" for 6 months. Full breakdown 👇

ZEUS⚡️

37,445 Aufrufe • vor 2 Monaten

A serving Indian Army personnel travelling between Maharashtra and MP crossed 6 tolls each side and faced the same confusion everywhere. At Gadha Toll, he was exempted three times… but the 4th time, cash was demanded because staff had “no clarity” on the rules. When policies clearly grant exemption to serving members of the Indian Army, why is there confusion on ground? Clear, written instructions must be issued by National Highways Authority of India to all toll operators. Our faujis serve 24×7. The least we can ensure is clarity in implementation. 🇮🇳

A serving Indian Army personnel travelling between Maharashtra and MP crossed 6 tolls each side and faced the same confusion everywhere. At Gadha Toll, he was exempted three times… but the 4th time, cash was demanded because staff had “no clarity” on the rules. When policies clearly grant exemption to serving members of the Indian Army, why is there confusion on ground? Clear, written instructions must be issued by National Highways Authority of India to all toll operators. Our faujis serve 24×7. The least we can ensure is clarity in implementation. 🇮🇳

SSBCrack

17,359 Aufrufe • vor 6 Monaten

At Outseta: 💰 A full-time salary is $210,000 for everyone 🗓️ You can choose to work 1 to 5 days / week 🏠 Everybody earns equity on the same terms as our founders I get so many questions about this—I finally wrote a deep dive. Basics in video; full details in first comment.

At Outseta: 💰 A full-time salary is $210,000 for everyone 🗓️ You can choose to work 1 to 5 days / week 🏠 Everybody earns equity on the same terms as our founders I get so many questions about this—I finally wrote a deep dive. Basics in video; full details in first comment.

Geoff Roberts

35,109 Aufrufe • vor 2 Jahren

Man takes a blow torch to Oreos at different time intervals The longest duration is 30 full seconds and the Oreo is still in perfect condition, it looks untouched “What is that we are putting in our bodies because this is not normal. This is not normal at all”

Man takes a blow torch to Oreos at different time intervals The longest duration is 30 full seconds and the Oreo is still in perfect condition, it looks untouched “What is that we are putting in our bodies because this is not normal. This is not normal at all”

Wall Street Apes

6,857,477 Aufrufe • vor 1 Jahr

🚨 UPDATE: Ugandan Mayor Mamdani just went FULL ISLAM on America and says he’s honoring the “legacy” of Muhammad ISLAM IS A THREAT and he’s pretending it’s the “religion of peace” “Here in New York City, his example reminds us that caring for one another, embracing our diversity, and serving our communities, makes us stronger as we work to build a better New York City for everyone.” This comes after he voiced Islamic ways at a recent speech. Remember: Muslims are taught to LIE to our faces His goal is to conquer, not “coexist”

🚨 UPDATE: Ugandan Mayor Mamdani just went FULL ISLAM on America and says he’s honoring the “legacy” of Muhammad ISLAM IS A THREAT and he’s pretending it’s the “religion of peace” “Here in New York City, his example reminds us that caring for one another, embracing our diversity, and serving our communities, makes us stronger as we work to build a better New York City for everyone.” This comes after he voiced Islamic ways at a recent speech. Remember: Muslims are taught to LIE to our faces His goal is to conquer, not “coexist”

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