Building LLMKube: K8s inference across NVIDIA, AMD, and Apple Silicon. Data stays yours. AI/ML Ops Director @ Ashley. Ham Radio: N7CPM
Gig Harbor, WA
Joined January 2018
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Chris Maher retweeted
Be careful what you wish for, America…
“The rest of the world is ready and willing” to build data centers - ARM CEO
Chris Maher retweeted
My boy @ViC305 has got a special gift for you ladies and gentleman running ONE DGX SPARK.
DEEPSEEK 1017 ON ONE DGX SPARK ⚡️
I REPEAT DSV4.1 ON 1 DGX SPARK ⚡️
SPIN IT UP BABY 🔥
🚨 𝗜 𝗝𝗨𝗦𝗧 𝗔𝗖𝗛𝗜𝗘𝗩𝗘𝗗 𝗦𝗢𝗠𝗘𝗧𝗛𝗜𝗡𝗚 𝗜𝗡𝗖𝗥𝗘𝗗𝗜𝗕𝗟𝗘 𝗢𝗡 𝗢𝗡𝗘 𝗗𝗚𝗫 𝗦𝗣𝗔𝗥𝗞! 🧪🔥
With the support of @AWERXAI, I'm excited to release something VERY special for the local AI community!
🧠 @deepseek_ai 𝗗𝗲𝗲𝗽𝗦𝗲𝗲𝗸 𝗩𝟰.𝟭 𝗙𝗹𝗮𝘀𝗵 𝟭𝟬𝟭𝟳
📦 𝗦𝗔𝗚𝗘-𝗘𝗫𝗟𝟯 𝗮𝘁 𝗷𝘂𝘀𝘁 𝟭.𝟲𝟮 𝗯𝗽𝘄 𝗲𝘅𝗽𝗲𝗿𝘁𝘀
💻 𝟵𝟵.𝟲𝟭% 𝗖𝗢𝗗𝗘 𝗮𝗻𝘀𝘄𝗲𝗿-𝘁𝗼𝗸𝗲𝗻 𝗮𝗴𝗿𝗲𝗲𝗺𝗲𝗻𝘁!
⚡ 𝟯𝟱.𝟱 𝘁𝗼𝗸/𝘀 𝗱𝗲𝗰𝗼𝗱𝗲 𝗼𝗻 𝘃𝗟𝗟𝗠!
🚀 𝟭,𝟭𝟴𝟵 𝘁𝗼𝗸/𝘀 𝗠𝗔𝗫 𝗣𝗥𝗘𝗙𝗜𝗟𝗟!
🧠 𝟮𝟮𝟵𝗞 𝗖𝗧𝗫 (𝘀𝘁𝗶𝗹𝗹 𝘄𝗼𝗿𝗸𝗶𝗻𝗴 𝘁𝗼 𝗽𝘂𝘀𝗵 𝗶𝘁 𝗳𝘂𝗿𝘁𝗵𝗲𝗿!)
𝗔𝗟𝗟 𝗢𝗡 𝗢𝗡𝗘. 𝗗𝗚𝗫. 𝗦𝗣𝗔𝗥𝗞. 🤯
And YES, the quantized weights are AVAILABLE NOW! 💚
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🧪 𝗧𝗛𝗘 𝗟𝗔𝗦𝗧 𝟳𝟮 𝗛𝗢𝗨𝗥𝗦 𝗛𝗔𝗩𝗘 𝗕𝗘𝗘𝗡 𝗜𝗡𝗦𝗔𝗡𝗘!
Just a few days ago, I published my article about @nvidiaai's FP4-Local format: The Good, The Bad & The Ugly.
One thing really stuck with me...
NVIDIA demonstrated what was possible when aggressive quantization and intelligent optimization come together.
But there's a problem.
𝗥𝗘𝗣𝗥𝗢𝗗𝗨𝗖𝗜𝗡𝗚 𝗠𝗔𝗦𝗦𝗜𝗩𝗘 𝗤𝗨𝗔𝗡𝗧𝗜𝗭𝗔𝗧𝗜𝗢𝗡 𝗧𝗥𝗔𝗜𝗡𝗜𝗡𝗚 𝗣𝗜𝗣𝗘𝗟𝗜𝗡𝗘𝗦 𝗜𝗦 𝗢𝗨𝗧 𝗢𝗙 𝗥𝗘𝗔𝗖𝗛 𝗙𝗢𝗥 𝗠𝗢𝗦𝗧 𝗟𝗢𝗖𝗔𝗟 𝗔𝗜 𝗘𝗡𝗧𝗛𝗨𝗦𝗜𝗔𝗦𝗧𝗦.
That inspired me.
I've been developing my own SAGE-EXL3 process, and I wanted to see just how far I could push the latest DeepSeek Flash release while keeping the results accessible to the community.
So I spent the last 72 hours pushing the limits.
And the results genuinely surprised me! 🔥
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🏆 𝗧𝗛𝗘 𝗥𝗘𝗦𝗨𝗟𝗧𝗦 𝗔𝗥𝗘 𝗖𝗥𝗔𝗭𝗬!
I measured my 1.62 bpw expert quant against DeepSeek's native FP4 release checkpoint using held-out sequences and full-vocabulary token predictions.
And when I saw the CODE results, I could barely believe it!
🧪 𝗙𝗜𝗗𝗘𝗟𝗜𝗧𝗬 𝗩𝗦 𝗗𝗘𝗘𝗣𝗦𝗘𝗘𝗞'𝗦 𝗡𝗔𝗧𝗜𝗩𝗘 𝗙𝗣𝟴/𝗙𝗣𝟰 𝗥𝗘𝗟𝗘𝗔𝗦𝗘
💻 𝗖𝗢𝗗𝗘 — 𝗛𝘂𝗺𝗮𝗻𝗘𝘃𝗮𝗹
✅ 99.08% Top-1 Agreement
✅ 𝟵𝟵.𝟲𝟭% 𝗔𝗡𝗦𝗪𝗘𝗥-𝗢𝗡𝗟𝗬 𝗔𝗚𝗥𝗘𝗘𝗠𝗘𝗡𝗧!
✅ KL Divergence: 0.0186
🧠 𝗥𝗘𝗔𝗦𝗢𝗡𝗜𝗡𝗚 — 𝗠𝗔𝗧𝗛-𝟱𝟬𝟬
✅ 96.04% Top-1 Agreement
✅ 97.98% Answer-Only Agreement
✅ KL Divergence: 0.0349
🔢 𝗠𝗔𝗧𝗛 — 𝗚𝗦𝗠𝟴𝗞
✅ 95.56% Top-1 Agreement
✅ 94.19% Answer-Only Agreement
✅ KL Divergence: 0.0477
📊 𝗢𝗩𝗘𝗥𝗔𝗟𝗟 𝗛𝗘𝗟𝗗-𝗢𝗨𝗧 𝗙𝗜𝗗𝗘𝗟𝗜𝗧𝗬
🏆 94.52% Top-1 Agreement
📉 Mean KL: 0.0982
🎯 Median KL: 0.00019
𝗡𝗘𝗔𝗥-𝗟𝗢𝗦𝗦𝗟𝗘𝗦𝗦 𝗖𝗢𝗗𝗘 𝗧𝗢𝗞𝗘𝗡 𝗙𝗜𝗗𝗘𝗟𝗜𝗧𝗬 𝗔𝗧 𝗝𝗨𝗦𝗧 𝟭.𝟲𝟮 𝗕𝗣𝗪 𝗘𝗫𝗣𝗘𝗥𝗧𝗦! 🤯
These are direct teacher-forced fidelity measurements against native FP8, not task-level pass rates.
But seeing this level of agreement at such aggressive compression is EXACTLY what I was working toward!
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⚡ 𝗔𝗡𝗗 𝗬𝗘𝗦, 𝗜𝗧 𝗔𝗖𝗧𝗨𝗔𝗟𝗟𝗬 𝗥𝗨𝗡𝗦 𝗢𝗡 𝗢𝗡𝗘 𝗦𝗣𝗔𝗥𝗞!
Not two Sparks.
Not four Sparks.
Not an expensive rented GPU cluster.
𝗢𝗡𝗘 𝟭𝟮𝟴𝗚𝗕 𝗗𝗚𝗫 𝗦𝗣𝗔𝗥𝗞.
My latest vLLM measurements:
⚡ Decode: 35.5 tok/s
🚀 Maximum measured prefill: 1,189 tok/s
For a model this large, compressed this aggressively, and maintaining this level of fidelity...
𝗧𝗛𝗔𝗧'𝗦 𝗔 𝗕𝗜𝗚 𝗗𝗘𝗔𝗟 𝗙𝗢𝗥 𝗟𝗢𝗖𝗔𝗟 𝗔𝗜! 💚
And remember, 1.62 bpw refers to the routed experts, not every parameter in the full model.
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💚 𝗪𝗛𝗬 𝗧𝗛𝗜𝗦 𝗠𝗔𝗧𝗧𝗘𝗥𝗦.
I have a simple goal.
𝗠𝗔𝗞𝗘 𝗣𝗢𝗪𝗘𝗥𝗙𝗨𝗟 𝗔𝗜 𝗔𝗖𝗖𝗘𝗦𝗦𝗜𝗕𝗟𝗘 𝗧𝗢 𝗣𝗘𝗢𝗣𝗟𝗘 𝗪𝗛𝗢 𝗗𝗢𝗡'𝗧 𝗛𝗔𝗩𝗘 𝗔𝗖𝗖𝗘𝗦𝗦 𝗧𝗢 𝗗𝗔𝗧𝗔𝗖𝗘𝗡𝗧𝗘𝗥𝗦.
I don't want the future of local AI to be determined by who can rent the most GPUs or afford the largest training runs.
I want developers, independent researchers and enthusiasts to have the freedom to experiment, quantize, optimize and run powerful models on hardware they actually OWN.
NVIDIA's FP4-Local research showed me just how much potential remains in aggressive quantization.
My goal with SAGE-EXL3 is to keep pushing that frontier while making advanced quantization more practical for the wider community.
Today, I'm sharing the quantized checkpoint and the one-Spark serving recipe.
𝗧𝗛𝗜𝗦 𝗜𝗦 𝗝𝗨𝗦𝗧 𝗧𝗛𝗘 𝗕𝗘𝗚𝗜𝗡𝗡𝗜𝗡𝗚.
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🧪 𝗡𝗢𝗪 𝗙𝗢𝗥 𝗧𝗛𝗘 𝗥𝗘𝗔𝗟 𝗧𝗘𝗦𝗧!
The token-level fidelity results are incredible, but I'm not stopping there.
I'm currently running a broader evaluation campaign to measure how closely this quant preserves the native FP8 model's actual task performance.
💻 HumanEval+ / MBPP+
🧠 MMLU-Pro STEM / MATH-500
🔬 SciCode / LiveCodeBench
🔧 BFCL / Multi-step tool chains
🌍 MGSM
𝗧𝗛𝗘 𝗚𝗢𝗔𝗟 𝗜𝗦 𝗧𝗢 𝗣𝗥𝗢𝗩𝗘 𝗧𝗛𝗔𝗧 𝗔𝗚𝗚𝗥𝗘𝗦𝗦𝗜𝗩𝗘 𝗖𝗢𝗠𝗣𝗥𝗘𝗦𝗦𝗜𝗢𝗡 𝗗𝗢𝗘𝗦𝗡'𝗧 𝗛𝗔𝗩𝗘 𝗧𝗢 𝗠𝗘𝗔𝗡 𝗔𝗚𝗚𝗥𝗘𝗦𝗦𝗜𝗩𝗘 𝗤𝗨𝗔𝗟𝗜𝗧𝗬 𝗟𝗢𝗦𝗦!
Those task-level results are coming next! 👀
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👀 𝗔𝗡𝗗 𝗢𝗡𝗘 𝗟𝗜𝗧𝗧𝗟𝗘 𝗧𝗘𝗔𝗦𝗘𝗥...
@mr_r0b0t and I have also been working on an GB10 specialized inference engine called 𝗥𝗘𝗫𝗟𝟯.
Early testing has been VERY exciting, including 60 tok/s with bursts reaching 74tok/s!
We're putting the finishing touches on it and making sure everything is solid before releasing it.
More on that VERY soon. 😏
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🙏 𝗔 𝗛𝗨𝗚𝗘 𝗧𝗛𝗔𝗡𝗞 𝗬𝗢𝗨!
To @AWERXAI for supporting my work and helping make this contribution to the community possible.
To @nvidiaai for the FP4-Local research that inspired me to push this experiment even further.
To @justinn_builds for his initial vllm TP4 recipe that got me started in the right direction for loading this model up.
To @mr_r0b0t for collaborating with me on what's coming next!
And to @deepseek_ai for continuing to release powerful open-weight models that make community experiments like this possible.
𝗧𝗛𝗘 𝗕𝗘𝗦𝗧 𝗣𝗔𝗥𝗧 𝗢𝗙 𝗢𝗣𝗘𝗡 𝗔𝗜 𝗜𝗦 𝗪𝗛𝗔𝗧 𝗧𝗛𝗘 𝗖𝗢𝗠𝗠𝗨𝗡𝗜𝗧𝗬 𝗖𝗔𝗡 𝗕𝗨𝗜𝗟𝗗 𝗢𝗡 𝗧𝗢𝗣 𝗢𝗙 𝗜𝗧. 💚
🔗 𝗗𝗢𝗪𝗡𝗟𝗢𝗔𝗗 𝗠𝗬 𝗦𝗔𝗚𝗘-𝗘𝗫𝗟𝟯
huggingface.co/vcruz305/DSV4…
🛠️ 𝗢𝗡𝗘-𝗦𝗣𝗔𝗥𝗞 𝗥𝗘𝗖𝗜𝗣𝗘
github.com/vcruz305/DSV4.1-F…
𝗢𝗡𝗘 𝗦𝗣𝗔𝗥𝗞.
𝟭.𝟲𝟮 𝗕𝗣𝗪 𝗘𝗫𝗣𝗘𝗥𝗧𝗦.
𝟵𝟵.𝟲𝟭% 𝗖𝗢𝗗𝗘 𝗔𝗡𝗦𝗪𝗘𝗥 𝗔𝗚𝗥𝗘𝗘𝗠𝗘𝗡𝗧.
𝗟𝗘𝗧'𝗦 𝗞𝗘𝗘𝗣 𝗣𝗨𝗦𝗛𝗜𝗡𝗚 𝗟𝗢𝗖𝗔𝗟 𝗔𝗜 𝗙𝗢𝗥𝗪𝗔𝗥𝗗! 🚀🔥
Chris Maher retweeted
You download a local LLM. You fire it up in Hermes. No error messages. It says it's running perfectly... but the replies are completely blank, it's operating at a crawl, or the model suddenly feels incredibly dumb.
Youve spent hours googling, tweaking flags, and pulling your hair out thinking you did something wrong.
Spoiler: You’re not crazy. 🧠💥
Most "model problems" are actually hidden config traps hiding between your request and the response. Stop chasing ghosts and wasting your evenings.
The community has mapped out all 166 of these exact traps so you can diagnose your setup in under ten seconds.
If you're running your setup inside Hermes by @NousResearch , you can check your system instantly with just a single command. 🛠️👇
github.com/Blackwellboy/mode…
Chris Maher retweeted
LocalAI folks see this coming a mile away. OpenAI won’t be the only ones.
Hi,
Tomorrow we are re-opening the Pro $200 subscriptions to new subscribers, but together with it we are also changing how we calculate the usage for it. In effect, if you do the math, it will net out at half the dollar in API spend compared to the old Pro $200 plan.
Now that it's said, let me explain why this is happening and why you will still get more work done than if you were on the Pro $200 subscription one month ago.
(a) We didn't want to compromise in other ways and are committing to not reintroducing the 5h limit, so that you can fully use the weekly usage when you want.
(b) On the subscription, we guarantee that over time you always get more work done and with an increasing level of quality. This means that you will continue to get more value per dollar spent as a result of models getting more efficient and us passing down the improvements in the form of API price reductions.
(c) We don't want to put an incentive on ourselves to artificially inflate the API list prices to make it look like you are getting a lot (and workaround it through discounts, etc). Instead we want to continue to both rapidly reduce prices and increase capabilities of models on the API. This week we introduced GPT-6 Sol and GPT-6 Luna at 50% of their previous price. Over time, we see prices go low enough that it makes sense for most to buy usage as needed without there being a significant gap between what you get in a subscription and what you get in the API for a dollar spent.
(d) Tomorrow, we are adding more things to the subscription that won't draw on the usage, I won't reveal what that is yet.
I wanted to be transparent before all the big announcements tomorrow. Lots of new exciting things are coming to the subscriptions that will make it super compelling, but I wanted to make sure to share this change ahead of time so you can all understand it before we shower you with good news.
Codexingly,
Tibo
Chris Maher retweeted
Claim your compute.
Run AI locally.
Protect your privacy.
Keep your data.
Own the model.
Work offline.
No subscriptions.
No rate limits.
No outage page.
Your thoughts stay yours.
Your IP stays yours.
Your stack stays yours.
Nothing more important than this today.
Chris Maher retweeted
DeepSeek v4.1 Flash ⚡️ IS OUT!
DeepSeek v4.1 Flash ⚡️ IS OUT!
DeepSeek v4.1 Flash ⚡️ IS OUT!
DeepSeek v4.1 Flash ⚡️ IS OUT!
DeepSeek v4.1 Flash ⚡️ IS OUT!
DeepSeek v4.1 Flash ⚡️ IS OUT!
huggingface.co/deepseek-ai/D…
Chris Maher retweeted
nvidia quietly dropped an official NVFP4 of qwen 3.8 flash and almost nobody noticed, it had 16 downloads when i found it.
this one is designed for the 2x dgx spark owners. 132.7GB of weights, does not fit a single box, but across two sparks it leaves about 40GB more headroom than the official fp8 and keeps more precision than the community 4 bit i have been running.
and the model is already special. i served the community q4 on one dgx spark this morning, 44 tok/s with the mtp drafter, and the depth behavior is the best i have measured in this class, barely fades where others collapse. now the same model, official modelopt quant, on the full cluster. benching it next, numbers incoming.
huggingface.co/nvidia/Qwen3.…
Chris Maher retweeted
Exciting day for NVIDIA and @huggingface.
Open models strengthen safety and cybersecurity, accelerate innovation and diffusion, and enable sovereignty. They allow every developer, startup, university, industry and country to build with, customize and benefit from AI.
Thank you @ClementDelangue for coming to me.
NVIDIA is going to be a great home for Hugging Face, its community and the future of open models. 🤗
blogs.nvidia.com/blog/nvidia…
Chris Maher retweeted
20 hours. free up your devices!
huggingface.co/Qwen/Qwen3.8-…
Chris Maher retweeted
buun-llama now supports DFlash2. This is the fastest implementation on the market. While DFlash2 restricts rollback planes to 7, I have extended this to 12. My adaptive depth also prevents DFlash2 from collapsing on prose, a problem with DFlash. Works with --mmproj-gpu-swap.
🤖 Made with AI
Chris Maher retweeted
Barrier decisively broken, just in time for this work to be worthless and deprecated by Qwen 3.8 lol. Working on DFlash2 now for 3.8, should have something good tomorrow. But if you still want to run 3.6 at 326 tok/s tg on a single 3090, feel free.
🤖 Made with AI
Chris Maher retweeted
Alright guys, wow.
Qwen 3.8 27B is basically Qwen 4.
What we have here is one of the best one-shot shark survival games I've made with any model. I ran this same prompt through fable, and the biggest thing all models struggle with in this test is the top-down view of the sharks. Fable even had all of the fins reversed and details jumbled on the first turn. Before now, it was really a 2 to 8 turn test to get something final regardless of model.
Qwen 3.8 27B just nailed this in one output, no harness at all, just purely the model thoroughly thinking before outputting the final result.
On every asset used, different shark variants, everything, it all looks incredibly tight. The boat movement, shark speed and engagement, everything is so much better than I expected, and I was expecting a lot.
Imagine what 8 turns would do in a harness?
This confirms to me that while Qwen 3.8 27B thinks a lot, for the first time in a local model on the base weights, IT MIGHT ACTUALLY BE WORTH IT
This is a Frontier Lab model result, and it ran on my 5090 at 60tps in Q5_K_M. I am just in complete astoundment at what @Alibaba_Qwen has accomplished here, and I am incredibly grateful that this model is open source.
The speed will also increase drastically with optimisations.
This is just an incredible day. What a blessing. More to come!
Chris Maher retweeted
Qwen 3.8 27B feels SO GOOOOOD!
Here’s the thing, it’s thinking a lot, but for the first time it may be that the verbose thinking is less of a mistake than a lot of other local models.
It’s basically doing a full build, like a model would in a harness, but entirely within its reasoning trajectory. So the final result will be as close as possible to a final output.
There’s much less second guessing itself in reasoning than with Qwen 3.6. It’s very confidently peacing this together so it can have the full picture for the output.
This might be the play for smaller models. If the completeness of thought can overcome the increase in wall time, I’m pumped, we can always make it run faster!
I think this is going to be really exceptional, I will post the output soon 😎
Chris Maher retweeted
This is like my Super Bowl
huggingface.co/Qwen/Qwen3.8-…
Chris Maher retweeted
Got you covered bro, 27B is landing this week
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We believe in you!!!!
