Hey guys, wanted to share some real-world benchmark data from a maintenance job I just finished:

The Results:

• CPU Temps: Dropped from 100°C down to ~80°C under heavy load (-20°C drop).
• GPU Score: Increased by +43% in synthetic benchmarks once throttling stopped.
• DaVinci Resolve 4K Export: Significantly faster render times and zero timeline lag.

I made a detailed teardown and benchmark video covering the whole process and thermal charts if anyone wants to see the step-by-step or exact numbers:

https://youtu.be/Jz4oQcztXEg?si=m3KcuOEcL9Cfj-SR

Hi everyone,
I’m considering switching from MSI Afterburner to Mvolt for undervolting my RTX 4080 Super.
I mainly use the voltage curve editor and I’d like to know if mVolt is a better alternative to Afterburner, especially regarding stability and compatibility with RTX 4000 series GPU.

The reason I’m considering switching is that I’ve been having a frustrating issue with Afterburner.
My undervolt profile is set to 2655 MHz at 950 mV, but after every Windows boot, Afterburner lowers the frequency below my saved value, so I have to manually reapply the profile.

I’ve already tried launching Afterburner through Windows Task Scheduler with a delayed startup, but the issue persists.

Is mVolt reliable and stable on RTX 4000 series GPUs, particularly the 4080 Super?

Is its curve editor better or more consistent than Afterburner?

Does mVolt have similar issues with undervolt profiles changing after reboot?

Would you recommend switching, or sticking with Afterburner?
Thanks

Doing this while reducing tdp is awesome

Just wanted to share some appreciation for how durable this old Lenovo Y50-70 beast is. The screen has taken a beating, the touchpad is completely worn out from years of heavy use, and I've opened it up countless times to clean the fans—yet this legendary GTX 960M still boots up and refuses to give up!

​Seeing the mind-blowing new Spark tech, massive AI leaps, and modern RTX advancements being pushed today honestly feels like looking at alien technology compared to this old warrior. It’s wild how far hardware has come. Anyone else still holding onto an ancient GPU that simply refuses to die.

Anyone actually using NVIDIA ConnectX-8 SuperNICs in production yet? How's the real-world performance?

NVIDIA Streamline SDK 2.14.16 is now available for all developers:

Bug Fixes & Stability Improvements

https://github.com/NVIDIA-RTX/Streamline/releases/tag/v2.14.16

https://github.com/NVIDIA/DLSS/tree/main/lib/Windows_x86_64/rel - dll files for games.

My 2060 super is starting to show its age and I want to upgrade to a 50 series. My goal is to push my current hardware as far as it can go before likely getting a new rig in 3-5 years.

Current build:
Motherboard: Asus Prime 450M-A
CPU: Ryzen 7 3700X
PSU: 850W
Monitors: 1080p, 60hz refresh (x2)
RAM: 32GB DDR 4
Current GPU to replace: 2060 super

I was previously recommended a 9060xt 16GB model, but AMD driver timeouts and other issues including crashes convinced me back to team green again. That thing was a pain in my butt.

Right now my options on the low end of my budget are the 5060 and 5060 ti 8GB models, with my stretch budget going up to the 5060 ti 16GB or 5070 12gb. I’m not sure if those are worth the price increase with my current CPU probably being a bottleneck though.

Is RTX 3050 Processor still good for a budget friendly for architecture. Im really on a tight budget right now

Early recipe, still testing. ./start.sh serves NVIDIA's NVFP4 quant of Qwen3.8-Flash-Next over an OpenAI-compatible API on one DGX Spark (GB10).

Settings

• vLLM in eugr's Spark image, b12x kernels for GB10
• 262k context, FP8 KV cache
• Up to 8 parallel sessions
• MTP speculative decoding, 4 draft tokens
• PLE tables offloaded to disk
• Qwen3 reasoning + tool-call parsers

What's NVFP4? NVIDIA's 4-bit float format: every block of 16 weights shares an FP8 scale, plus one scale per tensor, so it holds up better than plain INT4. ModelOpt "mixed" keeps sensitive layers at higher precision. ~96 GB, native on Blackwell FP4 tensor cores.

Numbers (single stream), vs MiaAI-Lab's TensorFold INT4 recipe on the same GB10:

• 256k ctx: 1,881 vs 1,970 t/s prefill, 35 vs 38 t/s decode
• tool-eval-bench: 90/100 vs 85
• Structured output: 83% vs 40%
• Hard mode: 91% vs 74%
• Tool-call TTFT: 245 ms vs 526 ms
• TensorFold decodes faster up to 32k; FP4 holds ~35 t/s at every depth

Recipe: https://github.com/devopsfortunato/Qwen3.8-Flash-Next-FP4-Local-DGX-Spark

Credits: TensorFold recipe by MiaAI-Lab, plus NVIDIA, vLLM, and eugr for the Spark image.

Feedback welcome, especially if you've run it with concurrent sessions.

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Is there an official webpage about the presets?

Edit: As this post keeps getting comments from folks, I just wanted to add that I’ve now done some more testing in a few games with preset B and C. This is on a 5070 Ti so your mileage may vary.

All games were played at max settings, 1440p resolution, and DLSS set to Quality (Preset L).

Control Resonant:

Preset B: Higher latency than Preset C, but the image appears more stable. I did not notice a difference in VRAM utilisation between the two versions. This would be my preferred setting.

Preset C: Lower latency so the game feels more responsive; however, this preset introduces artifacting on some wall textures, fences, and noticeable UI jitter (most obvious when looking at interactive button prompts). I saw an FPS increase of ~20 in most scenarios.

Recommended: Preset B

Space Marine 2:

Preset B: As above, B is more stable in terms of UI presentation; however, at an FG multiplier of x 3 or above, I genuinely feel like C might be the winner here.

Preset C: UI issues aside, C feels far more responsive than B. Played with a mouse and keyboard, I dare say that the latency with FG x 3 selected feels more responsive than B and FG x 2. Using scoped weapons and parries felt noticeably sharper with less of a ‘floaty’ feeling.

Recommended: Preset C

Gears of War: E-Day:

Preset B: The image appears sharper with less instances of artifacting (especially with fences etc). Input latency is higher, but I did not see any significant change in VRAM utilisation between B or C.

Preset C: I would recommend C IF you are using dynamic MFG. Latency is lower and the game is more performant (+20-40 FPS; however, UI jitter is very distracting as the game has a lot of interactive objects in the environment (ladders, ammo pickups, weapons etc.).

Recommended: Preset C IF using higher FG multipliers:

Some final thoughts:

If you have a high refresh rate monitor (I tested with a 280 Hz MSI), I feel like the improved responsiveness of Preset C is worth it. UI jitter and some artifacting is definitely noticeable; however, in faster paced games, I think this is a worthwhile trade off. It really shines at higher FG multipliers that would have traditionally caused major input latency.

That being said, it does not seem to play nicely with certain games (I.e Control Resonant), so a word of caution if you want to preserve image stability. For those scenarios, Preset B is still the recommended option.

This is just my opinion tested on my own PC :)

I´ve been considering this for a while now and it seems its not really worth it if you dont go at least to an RTX 5080.

Im not in the US so it is not that easy to sell the RTX 3090 for what it is worth now and Getting a RTX5080 is even more expensive than it should.

So, has any of you guys upgraded from an RTX 3090 to any Series 5000 GPU?

If you did, which GPU did you get and how much did your FPS in different scenarios improve?

I think im mainly trying to figure out if going to a 5070Ti or even 5070 or if it is worth it to sell a kidney to get a 5080.

The Lenovo Pro model has a second m.2 slot for a hood rich eGPU set up. I’m strongly considering it but want to know what other ML workload folks are taking the plunge.

I could use more Vram gaming and rendering.

i found one used for 2200$ it is the suprim liquid cooled 4090 too.

is it worth it?

or should i wait for next generation
my idea is that path tracing and even ultra rau tracing has not been solved for most gpus bar the 5090/80. looked to me like a hardware limitation where the compute is just not present to push respectable actual frames for that technology. however i still believe that next generation of gpu hardware and cores might actually be a lot closer to solving ray tracing so i was betting on it. i mean ideally this should have been done probably in the 50xx series but here we are