Throttled RTX cards: what the FP32 accumulation "unlock" really means

What this is about

On the r/LocalLLaMA community, user panchovix posted a short explainer, since there has been a lot of talk lately about "unlocks" for RTX graphics cards. The core claim: NVIDIA deliberately throttled the performance of operations that run in matrix multiplications (GEMMs) with FP32 accumulation on consumer GPUs. The goal is market segmentation, so gaming cards are slower than pro and data center cards at training and some inference tasks. This is one person's account. The source contains no confirmation from NVIDIA and no independent measurements.

What "FP32 accumulation" means

In GEMMs, the GPU multiplies low-precision numbers (e.g. FP16, BF16, FP8) and adds up the results. This summing step ("accumulation") can be done in higher precision, FP32, which keeps the math numerically stable and is common in training. According to the author, this exact mode runs at only half speed on consumer cards.

Which cards are affected

According to the post's list, these are unlocked (full speed): for Turing, the Quadro RTX cards (e.g. Quadro 8000); for Ampere, the RTX A6000, A40, A100 and the A-series; for Ada, the RTX 6000 Ada, L40 and the RTX Ada pro cards; all Hopper and all Blackwell data center cards; and for "Blackwell 2.0", the RTX PRO 6000 and the RTX PRO series.

Throttled are the RTX 20, 30, 40 and 50 series. In detail:

Which workloads are affected

According to the author, compute-heavy tasks suffer most: training (LoRAs, finetuning), diffusion models (text-to-image, text-to-video, image-to-image and so on), LLM prefill (processing the prompt) and token generation with large batches. As an example, he says his RTX PRO 6000 is about 70% faster than an RTX 5090, even though it has only 10–15% more compute units. It barely matters for token generation at batch size 1, which depends almost entirely on memory bandwidth, for CUDA math without Tensor Cores, and for INT4/INT8 GEMMs, which already use FP16 accumulation and see little use.

How realistic is an unlock?

The author rates the odds like this:

• RTX 20 and 30: high chance, given enough reverse engineering.
• RTX 40: relatively high chance, probably still in progress; likely via a patched UEFI and a modified VBIOS.
• RTX 50: low chance for now, because NVIDIA uses fuses that read the flag and throttle performance.

From his code analysis, it's just a single flag with the values "HALF_SPEED" or "FULL_SPEED", and all consumer cards are set to "HALF_SPEED". He sees no risk to the card, since, for example, the RTX A6000 and RTX 3090 Ti use identical chips that differ only in the flag. The same goes for the 4090/6000 Ada, 2080 Ti/Titan RTX and 5090/6000 PRO. For all Ampere consumer cards, he points to the GitHub project Zagitta/gpu-unlock.

Our take

The claims are plausible but unproven: there are no before-and-after benchmarks for an unlock, and the "no risk" claim is the author's own assessment. In practice, anyone who modifies a card's VBIOS or UEFI risks voiding the warranty and, in the worst case, bricking the card, so don't take that lightly. Still, the takeaway is useful if you run local AI: for pure token generation at batch size 1, an unlock would do little. For training, image and video generation and prompt processing, it could help a lot.