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* 32GB of memory - best option * OpenVINO is a pretty good tool, windows users up-and-running in minutes * Decent speed - think 5060 but with 32GB of ram.. * For AI workloads, great budget option! * openvino.. sentence embeddings I have between the two are nearly identical * Lower power than other brands * EXCELLENT in linux 7 / ubuntu 26.04 - 0 setup needed. Just worked OOTB. 0 issues.

Low power draw (300w max) along with 2 space width makes this an easy card to double up in a chassis for use with larger models.

I was an early adopter and grabbed a b70 on day 1, it was horrible. It barely worked, pcie 5.0 would not negotiate, drivers timed out, power would drop out, performance was barely above b580 in games, the only thing that was fast was some edge case pytorch training. Even the vllm performance was not great and very limited models supported. After a few months, now in the gamer drivers in windows we get fan control and overclocking, gaming performance really is about on target with a rx 6800 xt or 9060 xt, DCS world runs great, COD Warzone with FG on 1080p XESS Native = 250+ fps. I'm sure not all issues are fixed especially on the model side with llamacpp and vllm but this is now good enough and well beyond day 1. Sure my 7900 xtx smokes it in all gaming aspects but this gpu really is faster than a 4090 in some pytorch training scenarios and 35% faster than a 5080 in bert tokenization. One of the biggest issues for daily life was the fan control on the day 1 intel version of the b70 I had. The gpu was idling 60-70c. The asrock version has the same vapor chamber as the radeon ai pro r9700 which is definitely a better GPU for AI/Training/Inference and about twice as fast gaming. Now with fan control the gpu idles at 28-30c in windows and gaming hits 58-60c with a relatively inaudible (with headphones) fan speed. 270k tuned at 185w tpp pro b70 games and workloads less than 200w Doesnt heat up the room

Motherboard powered 16GB GDDR6 Low profile compatible Unbeatable price

- Easy to setup - Plenty of Support - Works on Ubuntu/Win11 easily Ollama/LM Studio - Gemma 4:26b - Flux 2

Minimum memory for models I wanted to work with, even quantized, is 24gb. $2,000+ just to experiment seemed like a lot in today's economy. Intel offered the B60 Pro for $600! So far, it's been everything I'd hoped. Gemma 4 26b and 31b models run comfortably and it's pretty amazing what capabilities they provide for local AI. I found that, for me, LM Studio was a little easier to use than AI Playground. YMMV Be sure to attach a monitor, even if you plan to use remote or onboard graphics. If no monitor is detected the board may not load the drivers. Turn off ECC memory or you'll be working with 21 instead of 24gb. It's astonishing how much free software is out there for GPUs (like the B60) that have enough VRAM to run that software. It's an incredible value proposition for anyone interested in learning about / experimenting with local AI.

Professional video card. Works great with games as well Ultra low power and efficient video card. Performance on par with a Nvidia 4060, but in a single slot low profile 50 watt card

- Stays cool even through rigorous stress testing - Crushes large datasets and point cloud processing - Incredibly efficient, completes all my work I used to do on a rtx 4070 super with no sacrifices or penalties. - Gaming capable, could run Arc Raiders on Epic Settings with no struggles at 60fps