NVIDIA Launches A2 Accelerator: Entry-Level Ampere For Edge Inferenceby Ryan Smith on November 9, 2021 7:30 AM EST
- Posted in
- Machine Learning
Alongside a slew of software-related announcements this morning from NVIDIA as part of their fall GTC, the company has also quietly announced a new server GPU product for the accelerator market: the NVIDIA A2. The new low-end member of the Ampere-based A-series accelerator family is designed for entry-level inference tasks, and thanks to its relatively small size and low power consumption, is also being aimed at edge computing scenarios as well.
Along with serving as the low-end entry point into NVIDIA’s GPU accelerator product stack, the A2 seems intended to largely replace what was the last remaining member of NVIDIA’s previous generation cards, the T4. Though a bit of a higher-end card, the T4 was designed for many of the same inference workloads, and came in the same HHHL single-slot form factor. So the release of the A2 finishes the Ampere-ficiation of NVIDIA accelerator lineup, giving NVIDIA’s server customers a fresh entry-level card.
|NVIDIA ML Accelerator Specification Comparison|
|FP32 CUDA Cores||6912||3584||1280|
|Memory Clock||3.2Gbps HBM2e||2.4Gbps HBM2||12.5Gbps GDDR6|
|Memory Bus Width||5120-bit||3072-bit||128-bit|
|Single Precision||19.5 TFLOPS||10.3 TFLOPS||4.5 TFLOPS|
|Double Precision||9.7 TFLOPS||5.2 TFLOPS||0.14 TFLOPS|
|INT8 Tensor||624 TOPS||330 TOPS||36 TOPS|
|FP16 Tensor||312 TFLOPS||165 TFLOPS||18 TFLOPS|
|TF32 Tensor||156 TFLOPS||82 TFLOPS||9 TFLOPS|
|PCIe 4.0 x16 +
NVLink 3 (4 Links)
|PCIe 4.0 x8|
|Manufacturing Process||TSMC 7N||TSMC 7N||Samsung 8nm|
|Form Factor||SXM4||SXM4||HHHL-SS PCIe|
Going by NVIDIA’s official specifications, the A2 appears to be using a heavily cut-down version of their low-end GA107 GPU. With only 1280 CUDA cores (and 40 tensor cores), the A2 is only using about half of GA107’s capacity. But this is consistent with the size and power-optimized goal of the card. A2 only draws 60W out of the box, and can be configured to drop down even further, to 42W.
Compared to its compute cores, NVIDIA is keeping GA107’s full memory bus for the A2 card. The 128-bit memory bus is paired with 16GB of GDDR6, which is clocked at a slightly unusual 12.5Gbps. This works out to a flat 200GB/second of memory bandwidth, so it would seem someone really wanted to have a nice, round number there.
Otherwise, as previously mentioned, this is a PCIe card in a half height, half-length, single-slot (HHHL-SS) form factor. And like all of NVIDIA’s server cards, A2 is passively cooled, relying on airflow from the host chassis. Speaking of the host, GA107 only offers 8 PCIe lanes, so the card gets a PCIe 4.0 x8 connection back to its host CPU.
Wrapping things up, according to NVIDIA the A2 is available immediately. NVIDIA does not provide public pricing for its server cards, but the new accelerator should be available through NVIDIA’s regular OEM partners.
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sabot00 - Tuesday, November 9, 2021 - linkCan this play games if one virtualizes the card (to get around no display out).
RU482 - Tuesday, November 9, 2021 - linkI doubt you'd be blown away by the performance if it could
mode_13h - Tuesday, November 9, 2021 - linkYes, quite likely. Given that it's based on a GA107, performance would be similar to a RTX 3050.
Spunjji - Wednesday, November 10, 2021 - linkAbout 2/3 of an RTX 3050 when you balance out the clock speed and core count differences.
catavalon21 - Tuesday, November 9, 2021 - linkI'm trying to figure out the purpose of this card. With a 1/32 Double Precision ratio and a core count not seen so small in accelerators since Maxwell - what niche is this filling? As a T4 successor it falls a bit short in comparison. Less memory bandwidth. Fewer cores. Less compute capability. Just...why?
mode_13h - Tuesday, November 9, 2021 - linkAs they say, its niche is inference. For that, you mostly need int8 and BF16.
Compared to their other Tesla products, what it gives you is a GPU that can run with no aux power and has the same CUDA capability as the rest of their Ampere line. That includes tensor cores with BF16, which I think T4 is lacking.
mode_13h - Tuesday, November 9, 2021 - link> Less compute capability.
Uh, those words have a very specific meaning, in Nvidia/CUDA context. What I think you mean is less compute capacity or performance.
catavalon21 - Thursday, November 11, 2021 - linkpoint taken
Gigaplex - Tuesday, November 9, 2021 - linkReplacement for the low end Quadro line, often used in workstation grade office boxes that don't need much GPU power, but need something with certified drivers (Intel support is garbage).
Gigaplex - Tuesday, November 9, 2021 - linkScratch that, I didn't see that this line doesn't have the display outputs. Stupid NVIDIA claiming that Quadro will be replaced by the A model line and then mixing it up with stuff like this.