Hello guys, hoping you're doing fine. Wall of text next I know, but I want to get this info out of my chest, all hand written lol.

I wanted to make this post explaining some things about GPU unlocks that have been going on lately.

First of all, does affect gaming performance?

None known at the moment, except if somehow NVIDIA uses FP32 accumulation for DLSS or FG, which I doubt. I'm almost sure they use FP16 accumulation (or lower precisions) there, because if they used FP32 accumulation the perf hit would be insane on consumer cards.

What are these GPU unlocks doing?

NVIDIA on purpose neuters tensor core performance when using FP32 accumulation (FP16, BF16, FP8 and TF32 inputs accumulating in FP32, which is what most AI/LLM/ML tasks use), for market segmentation. Normal FP32 on the CUDA cores (no tensor cores) is NOT affected.

Consumer and prosumer GPUs use the exact same die (like GB202 between the 5090 and 6000 PRO, AD102 between the 4090 and 6000 Ada, or even literally 1:1 with the 3090 Ti vs A6000, both with the same core amounts and 84 SMs).

How it works: the speed of each kind of tensor operation is set by fuses burned at the factory. On consumer cards, the fuse for "FP16/BF16 with FP32 accumulation" says HALF_SPEED, on the pro cards it says FULL_SPEED. These fuses don't cut any circuit, the GPU just reads them at boot and runs at that speed. The GPU also has an override register that can set them back to FULL_SPEED, but it's locked so only NVIDIA's most privileged firmware can write to it, not your PC.

The unlocks open that lock and write FULL_SPEED on the override. With that, the consumer cards gain all the neutered performance imposed by NVIDIA. It doesn't give you more SMs, VRAM or anything like that, a 4090 is still a 4090, it just runs those instructions at the same rate a pro card does.

Why can Turing, Ampere and Ada be unlocked? And what is unlocked on these cards?

On these generations, the NVIDIA driver itself loads a small NVIDIA-signed firmware called the "Booter" onto a security microcontroller inside the GPU (SEC2, a "Falcon"), to start the GSP. The Booter runs in the highest privilege mode of the GPU (Heavy Secure, HS), which is exactly the one allowed to open the lock.

In June 2026, Jon Pry published a paper ("A Canary in the Crypto Mine") showing that the Booter of the newer drivers has a buffer overflow: it copies its "signature" data using a length that you control, so you can overflow its stack (including the stack canary that is supposed to stop exactly this) and take control of the Booter while it is running in HS mode. The signature check still passes, because it is NVIDIA's own signed code running, the bug is inside it.

With that, the unlock reuses instructions from NVIDIA's own Booter to open the lock, writes FULL_SPEED on the override, resets the card and lets the normal NVIDIA driver load. The unlock survives resets and driver reloads, but it's lost when you power off, which is why it runs on every boot before the NVIDIA driver loads.

This was first done on mining cards (CMP 170HX: FP32 from 1/32 to full speed, and 10GB to 80GB of VRAM), and then ported to GeForce:

• Turing: https://github.com/haohervchb/Turing-unlock (UEFI, Linux)
• Ampere: https://github.com/Zagitta/gpu-unlock (Linux kernel module)
• Ada: https://github.com/JustAResearcher/gpu-unlock-ada (Linux with kernel module)

What gets unlocked:

• FP16 with FP32 accumulation (Turing, Ampere, Ada)
• BF16 with FP32 accumulation (Ampere, Ada; Turing has no BF16)
• TF32 (Ampere, Ada)
• FP8 with FP32 accumulation (Ada; Turing and Ampere have no FP8)

Some numbers from those repos: 4090 FP16/BF16 with FP32 acc goes from 165 to 331 TFLOPS (raw instruction rate), 3090 Ti BF16 GEMM from 87 to 157 TFLOPS, 2080 Ti FP16 with FP32 acc from 47 to 94 TFLOPS. Real-world GEMMs gain less than 2x because the cards then hit their power limit (more on that below).

Why can't consumer Blackwell be unlocked?

The funny part is that on Blackwell the limitation is EXACTLY the same thing: same fuses, same override registers. I checked on my own cards: the 5090/5080/5070 Ti have the FP32 accumulation fuses set, and the 6000 PRO/4000 PRO don't. So it's not a hardware difference there either.

What changed is the door. Starting with Hopper, and consumer Blackwell too, the root of trust of the GPU is the FSP (Foundation Security Processor), a RISC-V core that boots from its own ROM as soon as the GPU powers on, before the driver even loads. On Blackwell:

• There's no Booter to abuse. The driver just sends the FSP a message with a hash + RSA-3K signature of the next firmware, and the FSP verifies and boots it. The Linux kernel docs literally say: "There is no SEC2 involvement, no Booter ucode, and no FWSEC-FRTS stage". I also checked it myself: in NVIDIA's 615 driver source the Booter only exists for Turing, Ampere and Ada (TU102, TU116, GA100, GA102, AD102), and the RTX 50 VBIOSes don't carry the Falcon firmware (FWSEC) that older cards did.
• The firmware the driver ships for Blackwell is encrypted, and the debug and production images are the exact same encrypted bytes. The Ada payload was built from analysis of the decrypted debug Booter, on Blackwell there's no such shortcut.
• NVIDIA has said it uses SPARK (a formally verifiable programming language) to prove its most critical RISC-V firmware free of runtime errors, and a buffer overflow like the one used on the older cards is exactly that kind of error.

So, with what is known today, it can't be done. It isn't "impossible forever", but someone would need a brand new vulnerability in a way harder target. And if someone ever gets there, the unlock itself would very likely be the same couple of register writes.

FP4 is not affected, but all the rest is (FP16/BF16/FP8/TF32 with FP32 accumulation).

In which tasks is the unlock has most of the effect?

I will put some examples on my 6000 PRO vs 5090s, former using 400W and latter 600W, at how much is the impact in performance because the NVIDIA neutering.

• Training: Like LoRAs, finetuning, etc.
  • Example: Using the same batch size for 25GB VRAM to train a diffusion LoRA, my 6000 PRO is 70-90% faster than my 5090.
• Diffusion: Models that do txt2img, txt2vid, img2img, img2vid, txt2aud, etc.
  • Example: My 6000 PRO not using fp16 accumulation is about 30-40% faster than my 5090. If using fp16 accumulation, it is 15% faster.
• Prefill on LLMs: Heavily affected as well, since even when a model is marked as "int8" or "mixed precision" etc, the inference parts that the GPU uses include FP32 accumulation parts, except if explicitly set to not use them.
  • Example: My 6000 PRO on vLLM on a model that fits on a 5090 as well, it's 50-70% faster. On llamacpp, instead is about 30-40% faster.
• TG with big batching: TG with a single batch is mostly bandwidth bound, and it uses FP32 acc instructions, but since it is so little in comparison with the need of bandwidth, it isn't a limitation. On the other hand, when you use batches with batch size 2, 4, 8, etc, compute again starts to being a bottleneck and then you have a performance impact.
  • Example: Same results as previous point.

In which tasks the unlocks makes no difference:

• TG with batch size 1: almost entirely bandwidth bound.
• CUDA compute without tensor cores (normal FP32).
• INT4/INT8 tensor operations.
• FP4 on Blackwell (not neutered to begin with).
• GEMM operations with FP16 accumulation instead, but those aren't very used.
• Gaming.

Is there a risk by unlocking the cards, like burning the card, crashes, etc? Short answer: NO.

And again, NO. For the love of god guys, stop repeating this nonsense.

The cards are still limited by their max power limit. They won't magically use 200% of your power budget with an unlock, they will just use the cores more efficiently.

Let me explain: if you have a 3090 power limited at 300W, and without the unlock you were seeing only 250W in use, but after the unlock it uses 300W, it's not because the unlock did malicious things to your card. It's because the card can NOW stretch its legs and use the power to do more work. Real example from the Ada repo: an unlocked 4090 doing the same GEMMs peaked at 346W vs 313W stock, still under its 450W limit.

If you were getting X performance at Y < max TDP, and now you get more than X performance at max TDP, obviously the card will be hotter, but that's still inside the POWER LIMIT YOU SET, not above it.

Power spikes: those are normal on every card, and if you're using it to the full extent of its capabilities they can be more common. Are they risky? No, assuming you have a PSU with the wattage NVIDIA recommends per GPU.

So what ARE the actual risks then? They're about the unlock process, not your card:

• It runs an exploit on NVIDIA's firmware before the driver loads. If something fails mid-way, the tools stop and you need a full power off before trying again (a reboot isn't enough). Read the repo instructions, especially on laptops and multi-GPU setups.
• NVIDIA could patch it in future drivers, or even burn anti-rollback fuses so the old Booter stops being accepted. That wouldn't damage your card, it would just close the door for good.

"But, NVIDIA bins the dies to make these work 24/7 on the prosumer side but not on consumer, that's why they're locked!!!11"

Also incorrect. NVIDIA uses the EXACT SAME dies for their consumer GPUs and their prosumer GPUs, and the FP32 accumulation speed has nothing to do with binning: the 3090 Ti and the A6000 have the exact same specs. The half speed is just a value written in a fuse that the firmware reads.

Unlocking a card will not make it die sooner, nor will it crack the die or something.

Sorry I have to repeat this again and again, but the amount of pure fury I get from reading comments like these made me do this.

The model I run says it is Q8, or FP6, or 4bit, or INT4, or anything that doesn't mention FP32, am I affected?

It depends on how the GPU actually does the math, not on the name of the quant:

• If the kernel dequantizes the weights to FP16/BF16 and multiplies on the tensor cores with FP32 accumulation, you're affected. That's the case for FP16/BF16 models and most vLLM paths, including AWQ/GPTQ with Marlin kernels (the weights are INT4, but the math is FP16 with FP32 accumulation).
• FP8 (W8A8) also accumulates in FP32, so it's affected too (Ada and Blackwell).
• If the kernel uses integer tensor math (INT8 x INT8), it isn't affected. A lot of llama.cpp's quantized matmuls work like this, which is one reason llama.cpp sees a smaller gap than vLLM.
• Attention (like FlashAttention) almost always uses FP16/BF16 with FP32 accumulation, so long contexts get hit even on quantized models.
• FP4 (NVFP4/MXFP4) on Blackwell: not affected.

Example: on a modded 48GB 4090, a quantized MoE model on an inference server got no gain at all after the unlock (0.98x on 8K prefill), while the FP16/BF16 matmuls doubled.

That's what I have for now, if you guys have any more questions, let me know!

DLSS is something unique, anyway there’s so much to discover and games I want to replay now.

About my PC:

CASE: Fractal Design Meshify 2
FANS: be quiet! silent wings 3, 4x140mm, 3x120mm
PSU: be quiet! straight power 11, Platinum 1000W
M2.SSD: Kingston A2000, 1024GB
M2.SSD: Samsung 980 PRO, 500GB
MB: Asus Rog Strix B550-E Gaming
RAM: Kingston Fury, 4x8GB, 3600mhz
COOLING: Arctic Liquid Freezer III Pro, 360mm AIO
GPU: ASUS Astral RTX 5080 OC
CPU: AMD Ryzen 7 5800X3D

Thanks in advance!

I want the maximum quality I can get idk which option do I use?

I'm mainly a software developer and never play any games, do video-editing etc. But I need a GPU to run 3x 2K monitors at 240Hz, maybe leave room for 4K monitors at 240Hz for future as well.

Would this card be able to easily handle that?

My replacement RTX 5090 FE just came back from RMA after the 12VHPWR connector melted, so I had to rebuild my PC again. NVIDIA's RMA was surprisingly very helpful and even allowed me to get a full replacement even if I didn't have the original proof of payment!

Case: FormD T1 V2.1 Silver with CNC Aluminum Side Panels
CPU: AMD Ryzen 7 9850X3D
GPU: NVIDIA RTX 5090 Founders Edition
Motherboard: Asus ROG Strix B850-I
RAM: T-Create 64GB DDR5 6000Mhz CL34
Storage: WD SN8100 4TB PCIE Gen 5 NVME + WD SN7100 2TB PCIE Gen 4 NVME
PSU: Corsair SF1000 SFX
CPU Cooler: EK Basic 240mm AIO with 1 x Phanteks T30 and 1 x Silverstone Air Slimmer 120
Others:
- Custom PSU Cables from Cablester on Etsy
- FormD T1 V2.1 Flipped 5090/5080 Founders Edition Travel Kit / Gen 5 Riser Cable
- FormD T1 Fittings Pack
- FormD T1 USB-C front port
- CNC Aluminum Power Button
- CNC Aluminum FormD T1 Gen 5 Riser Lock Bar
- CNC Aluminum AirTag Holder
- Apple AirTag 2
- Aluminum RAM Heatsinks
- 120mm Fan Grill for the Phanteks T30

I have an RTX 5070 and a 1440p monitor, and I’m trying to determine which DLSS settings provide the best overall image quality and performance.
I’m considering these options:

DLSS 4 — Preset K at Quality
DLSS 4.5 — Preset L at Quality, Balanced, Performance, or Ultra Performance
DLSS 4.5 — Preset M at Quality, Balanced, Performance, or Ultra Performance

Since DLSS 4.5 Presets L and M are designed to work with the newer transformer model, I’m wondering whether using them at lower rendering resolutions, such as Performance or Ultra Performance, can produce image quality comparable to or better than DLSS 4 Preset K at Quality.

I’m particularly interested in:
Overall image quality and sharpness at 1440p
Fine details, foliage, and distant objects
Ghosting, shimmering, and temporal stability in motion

Performance differences on an RTX 5070
Whether DLSS 4.5 at Performance or Ultra Performance can compete visually with DLSS 4 at Quality

My priority is the best possible image quality at 1440p while maintaining good performance.

For anyone who has tested these presets, which combination would you recommend? Is DLSS 4 Preset K at Quality still the best overall choice, or can DLSS 4.5 Preset L or M at Performance or Ultra Performance deliver a better balance of image quality and FPS?

How do you optimize ultra-demanding AAA titles on RTX 4070 Ti at 1440p? (Custom DLSS scales, 12GB VRAM limits, Reflex & Frame-Pacing setup)

While running DLDSR 2.25x is a breeze for older titles and DLAA handles lighter workloads effortlessly, heavy AAA games especially Path Tracing or UE5 titles, force a much tighter balancing act on my RTX 4070 Ti at 1440p. I wanted to open a thread on how you dial in settings for maximum visual fidelity and low input latency when a game pushes the GPU to its absolute limit.

1. DLSS Custom Scaling & SDK Presets

When native 1440p or DLAA isn't viable:

Are you sticking to standard DLSS Quality (67%) or forcing custom internal resolution scales via DLSSTweaks (e.g., 75%–80% / ~1108p–1152p internal)?

Are you overriding default game DLLs to force Preset K or testing DLSS 4.5 Transformer Presets (L/M) for thin-geometry stability and less smearing on fast camera rotations?

2.Sync, Reflex, and Frame Rate Capping Pipeline

My baseline stack for zero tearing and minimum latency:

G-Sync: ON (Global)

V-Sync: ON (NVIDIA Control Panel) / OFF (In-Game)

NVIDIA Reflex: ON / ON + BOOST (letting Reflex auto-cap frame rate below display refresh rate)

Questions:

In games without Reflex, do you prefer in-game limiters, NVCP Max Frame Rate caps, reflex inside afterburner? or RTSS (RivaTuner) for frame-time line smoothing despite the tiny latency penalty?

How are you handling post-processing sharpening, bypassing built-in DLSS sharpeners for ReShade passes like Depth-Aware RCAS (DA-RCAS), or running zero sharpening?

Curious to hear your exact profiles, configs, and VRAM management strategies for heavy hitters like Cyberpunk 2077 (Path Tracing), Alan Wake 2, or Black Myth: Wukong.

For example in ac black flag resynced I use: custom dlss 80% preset dlss, frame gen on, RCAS 0.22 sharpening with renodx HDR and reflex inside afterburner. What do you think?

With my overclock/undervolt combo of 3120mhz core and +3000mem at .975mv, I used to average 8150-8200 in speedway. With the new driver, I am seeing an average of 8700. Anyone else notice a measurable increase with the latest driver? Note that I only use MSI Afterburner and haven't bothered with my Xbar. I've been running the same profile for 8 months now with zero crashing, artifacting, or instability in games or benchmarks. Steel nomad seems to be unchanged averaging around 7550-7600.

I need to run Blender, and to play some games.

Here are the graphics scores only from the different 3DMark benchmarks I've run so far while using the 5080, comparing various NVIDIA driver versions. The aim is simply to see the difference in scores using the gpu stock values.

It's just ddu old driverr > install new driver > run all 3dmark

No task/background process is killed before the benches.

Has anyone tried an undervolt and OC on this card on the latest drivers?

What are your settings and benchmark results?
Also interested in how stable it is in-game

FE only

Current rig: 5800X3D / ROG Astral RTX 5090 / 32GB DDR4 / ASUS TUF X570-PLUS Wi-Fi / 4K 240Hz ROG OLED. Still loving this card 😮‍💨😍

I ran an experiment on one RTX PRO 4000 Blackwell SFF: 24 GB GDDR7, factory 70 W, no auxiliary power connector.

Using u/panchovix’s loader and patched NVIDIA open kernel modules, I loaded a modified VBIOS into memory, raised the power limit to 120 W, and tested memory and core offsets. No EEPROM flash or soldering.

Across 158 benchmark requests, I tested Qwen3.8 27B with DFlash2 and Qwen3.6 35B A3B. What surprised me was how differently they answered “does more power help?”

With zero clock offsets, 27B kept gaining up to 120 W, while 35B A3B was almost flat between 106 and 120 W. After combined power and clock tuning, the best observed 27B generation result was 35.04 → 58.53 tok/s (+67%): 120 W, VRAM +20%, core +180 MHz. That 67% is not the gain from memory OC alone.

The memory sweep was more interesting. At 120 W with the core offset held at zero, pushing VRAM from +20% to +33.3% slightly reduced 27B generation: 57.70 → 56.91 tok/s. It is a small difference with few repetitions, so it needs more testing. The actual SM clock also fell. That is consistent with memory and compute sharing a power budget, but I did not isolate the cause.

On 35B A3B, memory +33.3% instead gave +19.8% generation versus no memory OC at 120 W. Prefill barely changed in that sweep.

My takeaway: the fastest profile is not necessarily the one with the biggest numbers in the panel, but the one that shortens your actual workload. Prefill versus decode matters, especially with longer prompts.

120 W without auxiliary power is not a free lunch. It exceeds the standard 75 W PCIe slot budget, and a GPU temperature sensor cannot tell me the temperature of the slot contacts. This is an experiment, not a long-term safety claim.

For context: these headline comparisons use 8,000 input tokens, ECC OFF. There were only 1–3 requests per configuration; 158 is the whole study. The 70 W baseline used the same modified VBIOS, not factory firmware. The models also used different quantization/speculative-decoding settings, so this is not a controlled dense-versus-MoE comparison.

Full write-up and CSVs on my blog.

Disclosure: I ran the measurements and wrote the source text in Polish. AI helped translate and edit the English, including clarifying the benchmark limitations. The linked blog is mine.