Francis Maximus Ultimato (@CryptoMeex) header
Francis Maximus Ultimato (@CryptoMeex) profile picture

we really need more powerful AI faster, let us push push push. 😉

Earth
Joined August 2012
Photo by Francis Maximus Ultimato (@CryptoMeex)Photo by Francis Maximus Ultimato (@CryptoMeex)Photo by Francis Maximus Ultimato (@CryptoMeex)Photo by Francis Maximus Ultimato (@CryptoMeex)Photo by Francis Maximus Ultimato (@CryptoMeex)Photo by Francis Maximus Ultimato (@CryptoMeex)Photo by Francis Maximus Ultimato (@CryptoMeex)Photo by Francis Maximus Ultimato (@CryptoMeex)Photo by Francis Maximus Ultimato (@CryptoMeex)
Francis Maximus Ultimato retweeted
The people have spoken. Quality first. I’ve been working hard to prepare for Qwen’s weights so that I can get you a great FP8 recipe on day 0. That is my goal. There’s a little surprise coming too.
What do you value more: speed or quality? For the upcoming Qwen 4 27b release, I may release a full FP8 single-card recipe. This won't have the kind of eye-watering tok/s numbers we all love, but it will give us a great model at near lossless quality. Yes or no?
70%Make it accurate!
30%Make it fast!
56 votes • Final results
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Francis Maximus Ultimato retweeted
BREAKING: The Local AI community funded a 512GB Mac Studio faster than most labs can schedule a meeting. - You don't need H100s - You don't need a waitlist
- You don't need an API key - You don't need a datacenter
- You don't need a hyperscaler
- You don't need a press release - You don't need rate limits lifted
- You don't need a GPU allocation
- You don't need a million followers
- You don't need anyone's permission All you need is a few hundred people who want what you're building to exist and want you win! Incredible what a community can do ❤️🙏 Congratulations, @ashxhart!
The local Ai community is just wild 🤯😍 I’m incredibly great full and inspired to push TensorFold even further to give back to every single one of you 🫶🏼
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Francis Maximus Ultimato retweeted
The local Ai community is just wild 🤯😍 I’m incredibly great full and inspired to push TensorFold even further to give back to every single one of you 🫶🏼
We did it! Local AI community is so powerful! Less than 24 hours to help @ashxhart to get an M5 Ultra 512GB to push TensorFold to the next level! WE ARE THE OPEN AI! 💪
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Francis Maximus Ultimato retweeted
Target reached!!! Congrats Ash!!! 🥳🥳
Support Ash! Let's get him his Mac!! I just bought Ashley Hart a Coffee on Ko-fi! ❤️ Ko-fi.com/home/coffeeshop?tx…
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Francis Maximus Ultimato retweeted
We’re adding new neurons to running local AI models 🤯 Teach it a new fact, save it into the weights, then restart it. Open a fresh chat and it can still remember. That’s Living Weights in TensorFold. It learns between replies, on your own hardware. Still experimental, but this is pretty wild. Myself an @petruspennanen have been working on it and will be bringing a breakthrough soon 🤯🚀
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Francis Maximus Ultimato retweeted
>be me >bought a used 3090 in 2023 >"bro just get a 4090" >"bro just get a 5090" >"bro ampere is ancient, it doesn't even have FP8" >just keep running local models in my 24GB of VRAM >card is older than some of the zoomers making fun of it >oct 2026 >some guy drops a github repo >gpu-unlock >flips some speed selector bits before the driver loads >FP16/BF16 matmul with FP32 accumulate goes to full rate >check benchmarks >3090 Ti: 87 TFLOPS -> 157 TFLOPS >mfw nvidia was sitting on half my tensor cores this whole time >mfw my card got a free generational leap from a kernel module >mfw a 6 year old GPU just mogged the upgrade path >ebay sellers already adjusting listings >"3090 BF16 UNLOCKED READY NO LOWBALLERS" >$3000 soon trust the plan >the 4090 bros who sold their 3090s at $700 are in shambles >open nvtop >card is pulling 350W and purring >she knows >she was always this powerful >ampere never aged >it was just waiting
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That would be like x-mas, just 2 month early 😍 @JensenHuang wanna be Santa Claus in october?
Dear @NVIDIAAI @JensenHuang: the CMP 170HX is a GA100, the same chip as the A100, with ~1.6 TB/s of HBM2e. It's deliberately handicapped in firmware. We're in the middle of a compute shortage. Flip the switch, unlock them officially, and thousands of cards go back to work. Do it for the people.
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Francis Maximus Ultimato retweeted
MiniMax M3.1 Flash with zero day support on TensorFold. @MiaAI_lab has you covered for a spark recipe 😁
MiniMax M3.1 Flash weights are now public. 428B parameters total, 23B activated per token. Agentic performance: A generational leap in agentic tasks, coding, and cybersecurity — 76.2 on BankerToolBench, 73.7% on DeepSWE v1.1, 61.7 on OSWorld V2, 29.6 on Agents' Last Exam, and 82.2 on CyberGym. Efficient by design: MiniMax Sparse Attention and DSpark speculative decoding improve inference efficiency. Hugging Face: huggingface.co/MiniMaxAI/Min… ModelScope: modelscope.cn/models/MiniMax… API: platform.minimax.io M Plan: minimax.io/m-planMiniMax MCode for desktop: agent.minimax.io/download
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Francis Maximus Ultimato retweeted
NVIDIA sold millions of 3090s with 50% of the tensor speed switched off. Not missing. Switched off. 1 kernel module flips it to full, and my BF16 tensor pipe went from 82 to 155 TFLOPS. Nothing else changed on the cards, and the outputs match the stock run byte for byte. All so the gaming card wouldnt eat into workstation sales. Every 3090 owner ran at 50% for 6 years, so a pro card could look worth the price. Fine, its their business model. And LLMs are the least interesting place for this. Decode waits on memory, and my engine runs its experts in int8. Still +10% on prefill. The models that live on BF16 tensor math are everything else. Qwen Image 2.1 and FLUX 3 Klein, where every diffusion step is big matmuls. MiniMax H3, which takes 4 to 7 minutes on my cards. Music and speech models. And training, every LoRA anyone ever trained on a 3090 went through exactly the path that was cut. Thats what I measure next. This is only the start.
My 3090s just got 1.9x FASTER at BF16 tensor math!!! Same cards, same driver, same 300W, one Linux kernel module. bf16 tensor pipe: 82 -> 155 TFLOPS cuBLAS BF16 GEMM 8192: 64 -> 99 TFLOPS TF32: 41 -> 79 TFLOPS NVIDIA ships every 3090 with fp32-accumulate tensor math at half speed. The driver even reports it: FMLA32 = 1/2. gpu-unlock by Zagitta sets it to full before the NVIDIA driver loads. After a cold boot all 4 of my cards say FMLA32 = full. Real inference got less than I predicted. Strata, Qwen3.8 Flash-Next on 2 cards, prefill: 4K: +12% 10K: +12% 90K: 3205 -> 3536 tok/s, +10% Before installing I said +14 to 19% at 90K, so I was wrong. The GEMM phases got 23% shorter, but they are only about 40% of Strata prefill, and the int8 experts, copies and CUDA core kernels didnt get any faster. Outputs did not change. With the same expert cache size, my log-prob dump over 1996 teacher-forced positions is the same file byte for byte as the stock run before the module. Next is Strata itself, now its everything around the GEMMs that makes prefill slow. How I set it up is in the replies.
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Francis Maximus Ultimato retweeted
A big new release for TraceMaker! Version 0.10.0 has new ways to get those last few pesky traces routed. Complex boards are now hitting 100%. It also runs simulation to tell you if there are any issues with the routing.
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Francis Maximus Ultimato retweeted
DeepSeek V4.1 Flash 1017 4× CMP 170HX ⚡️🔥🎙️ Structured 180 tok/s Code 137 tok/s Prose 97 tok/s Aggregate tok/s, 2 / 4 / 8: Structured 248 / 419 / 621 Code 128 / 173 / 376 Prose 134 / 188 / 283 KV pool 1.53M Routed experts only are EXL3 2.9. Attention, shared experts, head, draft, and Engram stay the official FP8 1017 weights. LibertAI's full 2.9 pack also quantizes attention: +1.8% code perplexity, +0.4% prose vs unquantized. This build quantizes less, so it should sit closer. Needle 9/9 at 30K, 121K, and 241K github.com/CurtisAccelerate/…
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Francis Maximus Ultimato retweeted
Deepseek v4.1 Flash 1017 on 4 CMP 170HXs. Coming this week.
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Francis Maximus Ultimato retweeted
Keys and Wasserman are on fire with their AI Studio
🚨🚨🚨 ::: weekend update:: keys/Wasserman AI Studio powered by TensorFold v 2.8.0 just released (for both Mac and DGX Spark (linux) This allows you literally produce the media content you want end to end without having to go to any other apps -fast + quality minimax h3 implementation repo in thread : 👇🧵 -Qwen image 2.1 turbo -Ref2VA integration -Fish Audio Drama3 -Flux-3 Klein -Motion Previs -Slate AI studio -custom text encoder -custom resolution -custom passes -custom Metal core (H, Drama3, Flux3) -Custom CUDA core(H, Drama3, Flux3) -Hybrid ComfyUI workflow -integrated automatic Prompt/scene producer -Enabled Parallel processing on DGX sparks -built in 2x 2k upscaler -and so much more to list... WTF running out the space just go read me in Gibhub
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Francis Maximus Ultimato retweeted
OUT NOW: Qwen 3.8 Flash Next on 2 CMP 170HXs ⚡️🔥 C1: up to 153 t/s. C8: up to 675 t/s. Prefill: 2,650 t/s. KV cache: 1M tokens. Quant: EXL3 5.05bpw (almost lossless vs BF16). More improvements coming soon. One command install in the repo below. 👇
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Francis Maximus Ultimato retweeted
We made open models run over 2x faster than Ollama, without any new hardware Introducing Magnitude, an open source inference engine that runs open models as fast as your hardware allows. It tunes its kernels on your machine to find the fastest way to run each model Works on Apple Silicon, NVIDIA, AMD, or just a CPU On average across 11 popular local models: Mac M4 Pro vs Ollama (MLX) 1.32x faster prefill, 2.19x faster decode DGX Spark vs Ollama (llama.cpp) 1.34x faster prefill, 2.17x faster decode Ollama, LM Studio, and llama.cpp ship one-size-fits-all kernels, because tuning each model for each chip by hand would take forever Magnitude tunes them automatically. When you download a model, it spends up to a minute on your actual chip trying different ways to split up the math (how many GPU threads team up, how big a chunk each one takes, how far ahead to load data) and keeps the fastest combination that doesn't change the model's output Each model also comes paired with the best speculative decoding drafter, for even more speed One click connects Magnitude to the agent you already use (Pi, OpenCode, Hermes, Codex, and more). It's also built specifically for agents: • Each agent uses about half the memory, thanks to a quantized KV cache • Memory grows and shrinks with your sessions, so your computer stays usable • Agents share prefix caches, so several can run at once without slowing down Next, we're working on getting even more out of your hardware: • Expert streaming from RAM or disk, to run models bigger than your GPU allows • Splitting one model across all your GPUs and CPU so nothing sits idle • Attention pruning that skips low-impact math, for much faster prefill Ships as a desktop app on Mac, Windows, and Linux 100% free and open source Download → magnitude.dev/download GitHub → github.com/magnitudedev/magn…
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Francis Maximus Ultimato retweeted
How does the EAQ v1 performs? The idea is to use LOWER bit quant and still be at least AS smart as a HIGHER bit EXL3. In Qwen3.8-27B, not only that, it BEATS it in agentic tool use by a large margin, while being 22% smaller! - Qwen3.8-27B EAQ 3.0 BPW - 12 GB - Qwen3.8-EXL3 3.5 BPW - 15.34 GB This gives you an idea of what EAQ is going to be, and this is just an experimental v1! COMING SOON !
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Francis Maximus Ultimato retweeted
Shenzhen speed in one photo. This scooter man handing me ICs through my factory window, 30 mins after I realised I’d picked the wrong chip. Wrong motor encoder IC. Found the right one on Taobao, added the seller on WeChat, booked this guy. Back to writing firmware. Investor demo prototype saved. This is how the hardware game is won.
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Francis Maximus Ultimato retweeted
EAQ v1 should be ready in the coming days. Version 1 is a quantization built specifically for TensorFold: - Same size as stock EXL3, better weights: a conversion recipe tuned for coding, tool calling and agentic work - A compressed embedding that only TensorFold loads, so it takes less memory than the original embedding - Tested inside TensorFold on DGX Spark and RTX 5090: coding and tool calling hold up against stock, long-context checks passed at 32k and 128k It won't be the final version though. I have a full plan to keep improving it through v1.1 and v1.2, and finally v2. EAQ v2 will be its own engine-aware format, built from the ground up for TensorFold. Thanks to @jgavinray for giving me access to his RTX PRO 6000, and to @sl_analytic for his DGX Spark 🙏
Engine-Aware Quantization (EAQ) vs stock EXL3 Same size, 37% lower KL. Measured on TensorFold. mia-ai.net/eaq
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Francis Maximus Ultimato retweeted
I’m going to need a bigger box! 4 more CMP 170HXs just landed. That’s 512GB of HBM2e, at ~1.5TB/s per card. 🔥🔥 CPU: Epyc 7663, 56 cores Motherboard: H12SSL-i RAM: 256GB PC3200 DDR4 SSD: 4TB Gen 4 HDD: 24TB I’ll need some time to rehome this beasty.
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