Same Chip, Half the Speed — NVIDIA's Half-Rate Tensor Cores, Explained in Pictures
In the gaming GPU you bought, the part that does AI math spends half its time waiting. It is not broken and it is not too slow; it was set that way at the factory, and full speed is kept for professional cards that cost a good deal more. In October 2026 someone unlocked it, and I tried it on my own laptop. On the same power, AI math ran about 1.6 times as fast. This post tells the story in pictures; the details are in the in-depth edition.
The backstory: one chip, two price tags
NVIDIA makes the same chip into two products: gaming cards for players (GeForce) and professional cards for companies. The professional ones cost a good deal more, and part of the gap is not extra hardware. Some abilities are held back on the gaming card at the factory.
This time the held-back part is the tensor core, the part of a GPU built to multiply matrices. Almost everything today’s AI does, from training models to drawing images to reading a prompt, is matrix multiplication.
Where the lock sits: the dispatcher hands out work every other beat
The tensor core itself can run at full speed. What was tampered with is the dispatcher in front of it. On every clock cycle (one “beat”) it could hand the tensor core an instruction; on gaming cards it was changed to one instruction every two beats. Flip the buttons below to compare:
So the lock does not make the part slower; it leaves it idle half the time. When the same chip becomes a professional card, the dispatcher hands out work on every beat and the tensor core runs at full speed.
Where the key came from
This back-and-forth, the vendor holding something back and the community prying it loose, has gone several rounds. Click “Next” to step through:
Step 1 of 6
One chip, two prices
NVIDIA uses firmware and drivers to split one chip generation into tiers: gaming cards are cheaper with some abilities held back; professional cards cost more with everything on.
Step 2 of 6
Lock one: the mining limiter
In 2021, with miners buying up gaming cards, NVIDIA added a mining limiter to RTX 30. First one of its own beta drivers lifted it by accident, then third-party software bypassed it completely, and in the end NVIDIA removed it from the driver itself.
Step 3 of 6
Lock two: mining cards
On CMP cards, sold only to miners, firmware locked away memory and compute. The community found a flaw in the firmware a GPU runs as it boots and set them free.
Step 4 of 6
Lock three: half-rate tensor cores
On 2026-10-18 someone used the same flaw on a different lock: the half-rate tensor core. The project, gpu-unlock, reached Hacker News on 10-24 and had about 12,000 stars by 10-29.
Step 5 of 6
I tried it on my laptop
On 10-29 I installed it on an RTX 3060 laptop and ran the same work before and after. The results are in the next section.
Step 6 of 6
New cards: the lock stays, the key is gone
RTX 50 boots a different way, so the flaw no longer applies. Third parties have measured the RTX 5090: the half rate is still there; no one can lift it.
What I tested
In one sentence: on the same laptop, run the same AI math before and after installing the key, see how much faster it got, and check that the kinds of math that were never locked did not move.
The three locked half-precision formats (the ones AI training and image models use) all got about 1.6 times as fast. The two formats that were never locked served as controls and did not budge, which shows the gain really comes from the unlock and not from something else:
On a real job: I trained a small model and it processed 40% more tokens per second, and the trained result came out the same as before the unlock.
Why not twice as fast
Twice the work per beat, yet only about 1.6 times the speed: the difference is power. The laptop’s GPU tops out at about 105 W, and it sat at that line both before and after. With twice the work on every beat, the same power cannot hold the old clock speed, so the clock dropped by about 17%:
Desktop cards with more power to spare get closer to double. The project’s README reports +87% on a desktop RTX 3060; I did not measure that one.
What it means
First: people running AI locally get a free chunk of speed, and gamers get nothing. Only the math AI relies on was unlocked, so:
Second: with the same chip, you may not be getting all of it. A GPU’s price depends not only on its parts but on how much of them is switched on. This time it was the tensor cores that came to light; before that it was mining speed, and the memory on mining cards. Next time you read a GPU benchmark, it is worth asking: is this the whole chip, or the factory setting?
Third: this key only fits older cards. The new RTX 50 changed how it boots, closing the road in advance:
Want the details?
The in-depth edition, The Half-Speed Tensor Core, covers what this one leaves out:
- which register holds the lock, and why only those three formats are capped
- how it opens the lock in the boot window, before the GPU driver loads
- why RTX 50 cannot be unlocked, and how two third parties showed the cap is still there
- every measurement, the clock and power logs, and a benchmark script you can run yourself
The previous post in this series, Is a Mac Actually Good for Running Large Language Models?, asks a different question: which few numbers decide whether a local LLM is fast.
In one line: the tensor core did not get faster; it just stopped waiting.