A free Linux tool is squeezing up to 59% more AI throughput out of three-year-old gaming cards, right as renting a real H100 gets a lot more expensive.
On GitHub, a developer who goes by Zagitta published a kernel module called gpu-unlock earlier this month. It does one specific thing: it removes an artificial limit Nvidia built into its GeForce RTX 30-series cards that cuts their FP16 and BF16 tensor math rate in half, a rate reserved for the company’s much pricier professional and datacenter chips. Run the unlock, and an RTX 3090 Ti goes from 87.46 to 118.39 TFLOPS in FP16, and from 87.50 to 157.41 TFLOPS in BF16, according to the project’s own benchmark data. In ComfyUI image generation, real-world gains land between 47% and 59%. In vLLM prefill, the step where a large language model digests your prompt before it starts writing a response, gains run 14% to 36%.
That’s not a marketing number. It’s a driver-adjacent kernel module that writes two speed-selector values, SS0 and SS1, before Nvidia’s unmodified driver binds to the card, and then gets out of the way. No VBIOS flash, no firmware patch to redo after every driver update. Reddit’s r/LocalLLaMA picked it up within days, and from there it spread to tech outlets covering the AI hardware beat.
The timing is not a coincidence. Nebius raised its on-demand H100 rate from $3.85 to $4.50 per GPU-hour on October 1, its second rate hike in three months, a roughly 17% jump. Silicon Data’s broader H100 rental index put the per-hour price in the $2.50-to-$2.80 range in mid-October, well above where it stood at the end of last year. If you need inference compute right now and don’t want to pay neocloud prices, a three-to-five-year-old 3090 sitting in a closet suddenly looks like an asset again.
While gamers were unlocking tensor cores on cards they already own, a separate hack turned an entirely different market upside down. The CMP 170HX was Nvidia’s dedicated cryptocurrency mining card, a cut-down Ampere chip launched in 2021 with no display output and, notionally, 8GB or 10GB of VRAM. Nobody wanted it once mining profitability collapsed. Used units were selling for a few hundred dollars by June.
Then, following a crack method publicly disclosed by Arizona State University researchers in June, a tool called CMPUnlocker showed the card was quietly sitting on 64GB or 80GB of HBM2e memory the whole time, restricted at the firmware level. Wccftech reported prices exploding from around $100 to over $1,000 within days once the unlock went public, and listings for the card have since appeared above $4,400. A card that can load a 70-billion-parameter model without quantizing it to death is worth real money to someone running local inference, even if it can’t display a desktop.
These are two different hacks solving the same underlying problem. One frees up compute that was already there. The other frees up memory that was already there. Both exist because Nvidia segments its product line by disabling capability in software and firmware, not because the silicon in a GeForce card is actually worse than the silicon in an H100.
Does this undercut the neoclouds raising prices, or prove their case? Honestly, a bit of both. It shows real demand is pushing people toward any inference capacity they can scrounge, cheap gaming cards included, which is exactly the scarcity story Nebius and its rivals point to when they raise rates. But it also puts a ceiling on how far that pricing power stretches. If renting an H100 keeps climbing toward $3 an hour while a used 3090 with this unlock applied handles inference at a fraction of the cost per token, plenty of hobbyists and small shops will just buy hardware instead of renting it.
There’s a real asterisk here, and it’s not small. None of this is Nvidia-sanctioned, and running a kernel module that rewrites instruction-issue rates on a consumer card isn’t something you’d want to be doing on a production server. Token decoding speed and gaming frame rates are reportedly untouched by the RTX unlock, which is actually the reassuring part: it only kicks in for the tensor math paths that matter for prefill and image generation, not the paths that would make the card misbehave day to day. The CMP 170HX situation is riskier territory, since it unlocks memory the manufacturer firmware was actively hiding, and firmware-level modification is the kind of thing that tends to get flagged if the card ever needs support.
Nvidia hasn’t commented publicly on either tool. Given how directly gpu-unlock cuts into the performance gap the company uses to justify charging many times more for a professional-tier card than a GeForce card built on the same silicon, a response, official or otherwise, wouldn’t be surprising.
Also read: H100 rental prices are spiking even as Wall Street worries about an AI bubble • Traders Are Letting xAI’s Grok Bot Run Unsupervised, and It Already Lost Money • AI agents are starting to do the work of research economists on Wall Street
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