Is This The FASTEST AI Model In The World?!! (Xiaomi MiMo V2.5 Pro UltraSpeed)

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00:00:00Holy cow, ShowMe, you know the Chinese company that makes phones, just made an AI model which
00:00:05might be the fastest in the world. It's called ShowMe Mimo V2.5 Ultra Speed and it is truly
00:00:13mind-blowing. In today's video we'll take a look at this model, see how it works and I actually
00:00:18managed to get early access to this model so we'll also test it out with some interesting examples
00:00:24to see how fast it actually is. It's gonna be a lot of fun so let's dive into it.
00:00:30Before we look under the hood of this model, let's see what massive differences are we actually
00:00:39dealing with here. So on Frontier models like GPT 5.5 or Claude 4 Opus, you're often wading through
00:00:46massive reasoning lags, scraping by at roughly 50 or 60 tokens per second. Now that's not bad but it's
00:00:54kind of slow. But ShowMe's new Mimo Ultra Speed model is clocking in at over 1000 tokens per second
00:01:00and what's even crazier is the fact that this model is also massive in size. It's a 1 trillion parameter
00:01:07mixture of experts model. So you might be thinking, okay they're probably using some kind of super
00:01:13advanced custom hardware setup for this. Well actually not quite. ShowMe teamed up with their
00:01:19systems partner Tile RT and they achieved this by using just a single standard server with eight
00:01:25commodity GPUs. But if that's not the answer then that begs the question, how do you force a trillion
00:01:31parameter model to spit out text at microsecond speeds on a standard hardware? Well they came up
00:01:39with something they call extreme model system co-design. They attacked the latency bottleneck
00:01:44from three different angles simultaneously. First, they optimized memory bandwidth. Moving a trillion
00:01:50parameters through GPU memory during text generation phase creates massive traffic jams. To fix this,
00:01:57ShowMe used MXFP4 quantization. But because 4-bit compression can normally make an AI
00:02:04less accurate, they used quantization aware training or QAT and they kept the core routing layers at a higher
00:02:12precision. This alleviated the memory pressure while keeping the model's intelligence nearly identical
00:02:18to the uncompressed version. Second, they ultimately changed the way the model predicts words. So standard
00:02:25speculative decoding works by having a tiny draft model guess a few words ahead and then the massive main
00:02:32model checks the math. But ShowMe did something different here with what they call D-Flash. Instead of guessing one
00:02:39token at a time, it predicts an entire block of hidden tokens all at once in parallel forward pass. And
00:02:46through testing, they discovered that when you use it for coding tasks, the main model actually keeps an
00:02:52average of 6.3 out of every eight tokens that D-Flash guesses. So it essentially lets the model take
00:02:58massive eight token leaps forward at a time instead of taking baby steps. And third, they use the special
00:03:04engine which solves a really annoying hardware bottleneck. So when you're pushing a thousand tokens a second,
00:03:11standard GPUs actually can't keep up with the instruction logic. Normally, a GPU launches a math
00:03:17operation, finishes it, clears out the memory and then waits to launch the next one. And even though these
00:03:23pauses only last microseconds, they completely kill your momentum. To fix that, TileRT built a persistent
00:03:30engine kernel that just sits inside the GPU and never leaves. They used a trick called warp specialization
00:03:37to assign permanent roles to different parts of the hardware. While one section is moving data,
00:03:42another is running the math, and a third is handling communication all at the exact same time. So the
00:03:48pipeline literally never stops moving. And this is so interesting because I just did a video on diffusion
00:03:55gemma, which is also super fast, but it tackles the same problem in a very different way. So check out
00:04:00that video if you're interested. And that my friends is how show me gets to 1000 token per second speeds,
00:04:07allegedly. But now let's actually test it out and see if this promise holds up. So for my first test,
00:04:14I decided to take one of lead code's hard questions and run it by the model. And it was blazingly fast.
00:04:20How wild is that? Plus, as we can see here, it peaked at 3451 tokens per second, which is absolutely insane.
00:04:29Now, there might be a possibility that this lead code question was part of the model's training data.
00:04:34So as impressive as this looks, it's probably not a fair comparison. So let's move on to something more sophisticated.
00:04:41Next, I asked it to build a simple UI personal finance dashboard in one single HTML file with no
00:04:48external libraries and nothing too fancy. And in this test, we could now actually see how insanely
00:04:54performant it is. It was averaging about 700 tokens per second for the reasoning part and about 1000 tokens
00:05:02per second for the output operations. And it took the model just 65 seconds to complete the task.
00:05:09And I think the result is pretty good. I'll be at some of the buttons are not working and some of
00:05:14the actions are broken, but the design as a whole is pretty good. I mean, not bad for a one minute task.
00:05:21So then I decided to challenge the model to build something even more sophisticated. I prompted it to
00:05:26build a Khan Academy style math explainer web page showcasing 10 popular math concepts to see how
00:05:34complex of a website can we actually produce here. And this is where things started getting a bit rough.
00:05:40I tried this test twice and both times after about two or three minutes, the model just stopped
00:05:45generating and completely froze. So I assumed that with this task, I hit the model's context limit or
00:05:51maybe show me has put a rate limiter of some sort. So then I decided to simplify the task a bit by asking
00:05:58it to design a web page with only five mathematical concepts. And this time it finally worked. It managed
00:06:04to finish the task in 75 seconds. And the output is actually quite nice. And the first three mathematical
00:06:10concept widgets are actually functional, but everything past that point is broken, non-functional or empty.
00:06:17So I don't know what exactly happened here. Maybe the model dropped some of its context during the reasoning
00:06:23phase, but nonetheless, I think this is a pretty good result, especially taking into consideration that
00:06:29we were averaging 500 tokens per second during the reasoning phase. And for my last test, I decided to
00:06:34do something a little bit more fun. I just simply prompted this very short sentence to build a subway surfer
00:06:41clone using three JS, and it actually managed to build a fully functional subway surfer clone in just 50
00:06:49seconds. Now that is crazy. I do have to say that although it is functional, as you can see here, it
00:06:55doesn't include any obstacles or coins or anything like that. So it's kind of boring. So I then decided to
00:07:01give it a follow-up prompt to fix these minor issues. And after two passes, it managed to successfully
00:07:07add some coins and some obstacles. And honestly, when I was testing it, this was a flawless demo.
00:07:14The functionality was there. Everything was working. It was even saving my high score after every round.
00:07:20So this particular demo really surprised me in a very positive way. I'm sure nowadays we can all
00:07:26build subway surfer clones with other models as well. But the fact that I could get a working prototype,
00:07:32which is not completely terrible and which is actually fun to play and all of that in just 50 seconds with
00:07:39some follow-up prompts, that is pretty impressive. So as we all saw in the tests, the model managed to
00:07:45reach a record speed of more than 3000 tokens per second. So this is indeed the absolute fastest model
00:07:52I've ever seen. And as far as the outputs go, I mean, yeah, sure. Some of them are broken. Some of them
00:07:58are half-baked. Surely this is no Claude Opus or GPT 5.5. But I'm sure that Xiaomi's models will definitely keep
00:08:06improving over time. So it's going to be very interesting to see what they come up with in the future.
00:08:12So there you have it, folks. That is Xiaomi Mimo V2.5 Ultra Speed in a nutshell. So what do you think
00:08:18about this model? Are you impressed? Disappointed? Indifferent? Let us know in the comments section down below.
00:08:24And folks, if you like these types of technical breakdowns, please let me know by smashing that
00:08:29like button underneath the video. And also don't forget to subscribe to our channel.
00:08:33This has been Andrus from BetterStack, and I will see you in the next videos.

Key Takeaway

Through architectural co-design, custom kernel optimization, and parallel token prediction, the 1-trillion parameter Mimo V2.5 Ultra Speed model achieves record-breaking generation speeds exceeding 3,000 tokens per second on standard server hardware.

Highlights

  • Xiaomi's Mimo V2.5 Ultra Speed AI model reaches speeds exceeding 1,000 tokens per second, with peak performance hitting 3,451 tokens per second.

  • The model utilizes a 1 trillion parameter mixture of experts architecture running on a single server equipped with eight commodity GPUs.

  • Extreme model system co-design employs MXFP4 quantization with quantization-aware training to maintain intelligence levels while reducing memory bottlenecks.

  • D-Flash speculative decoding enables the model to predict entire blocks of hidden tokens in parallel rather than guessing a single token at a time.

  • A persistent engine kernel with warp specialization maintains continuous GPU pipeline activity by assigning permanent hardware roles to data movement, math operations, and communication.

  • Functional prototypes, such as a Subway Surfer clone, can be generated in approximately 50 seconds.

Timeline

Technical Architecture and Performance Bottlenecks

  • Mimo V2.5 Ultra Speed achieves generation speeds exceeding 1,000 tokens per second.
  • The system runs on a standard server configuration with eight commodity GPUs.
  • Three specific engineering strategies address latency: memory bandwidth optimization, block-based speculative decoding, and persistent GPU engine kernels.

While frontier models often struggle with reasoning lags and speeds of 50-60 tokens per second, Mimo V2.5 uses MXFP4 quantization combined with quantization-aware training to compress the 1-trillion parameter model without sacrificing accuracy. The D-Flash method allows the model to predict 8-token blocks in parallel, resulting in a 6.3-out-of-8 token success rate for coding tasks. Furthermore, a custom TileRT engine uses warp specialization to ensure the GPU pipeline never pauses, effectively eliminating the microsecond-level overhead found in standard execution logic.

Performance Testing and Real-world Application

  • The model reached a peak speed of 3,451 tokens per second during LeetCode testing.
  • Complex task generation, such as a multi-widget web page, encountered context limits or freezing after two to three minutes.
  • Functional application prototypes, including a functional Subway Surfer clone, were generated in under 60 seconds.

Practical tests demonstrated that the model is highly performant for coding tasks, though it struggles with long-form context retention on complex, multi-component web design requests. The model successfully produced a functional, playable Subway Surfer clone in 50 seconds, with minor follow-up prompts allowing for the successful addition of gameplay elements like coins and obstacles.

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