NVIDIA Bumps All Tesla V100 Models to 32GB, Effective Immediately

When first launched last year, the original Tesla V100 shipped with 16GB of HBM2 memory. Now just a little less than a year into its lifetime, NVIDIA is announcing that their workhorse server accelerator is getting a memory capacity bump to 32GB, effectively immediately.

For the last couple of years now, NVIDIA has been relying on 4GB (4-Hi) HBM2 memory stacks for their Tesla P100 and Tesla V100 products, as this was the first HBM2 memory to be ready in reasonable commercial volumes. Now that Samsung and SK Hynix have a better grip on HBM2 manufacturing, 8GB (8Hi) HBM2 stacks are far more readily available and reliable. As a result, the conditions are right for NVIDIA to finally give their Tesla cards a long-awaited memory upgrade.

This upgrade will be across the entire Tesla V100 family – all SKUs for SMX and PCIe cards are getting their memory doubled. The Tesla V100’s specifications otherwise remain identical, with the same GPU and memory clocks along with the same TDPs. We’re also told that the mechanical specifications are identical as well, which would mean that the 8-Hi stacks won’t cause any cooling problems due to changes in memory stack height.

NVIDIA Tesla/Titan Family Specification Comparison
	Tesla V100 (SXM2)	Tesla V100 (PCIe)	Titan V (PCIe)	Tesla P100 (SXM2)
CUDA Cores	5120	5120	5120	3584
Tensor Cores	640	640	640	N/A
Core Clock	?	?	1200MHz	1328MHz
Boost Clock	1455MHz	1370MHz	1455MHz	1480MHz
Memory Clock	1.75Gbps HBM2	1.75Gbps HBM2	1.7Gbps HBM2	1.4Gbps HBM2
Memory Bus Width	4096-bit	4096-bit	3072-bit	4096-bit
Memory Bandwidth	900GB/sec	900GB/sec	653GB/sec	720GB/sec
VRAM	16GB 32GB	16GB 32GB	12GB	16GB
L2 Cache	6MB	6MB	4.5MB	4MB
Half Precision	30 TFLOPS	28 TFLOPS	27.6 TFLOPS	21.2 TFLOPS
Single Precision	15 TFLOPS	14 TFLOPS	13.8 TFLOPS	10.6 TFLOPS
Double Precision	7.5 TFLOPS	7 TFLOPS	6.9 TFLOPS	5.3 TFLOPS
Tensor Performance (Deep Learning)	120 TFLOPS	112 TFLOPS	110 TFLOPS	N/A
GPU	GV100	GV100	GV100	GP100
Transistor Count	21B	21B	21.1B	15.3B
TDP	300W	250W	250W	300W
Form Factor	Mezzanine (SXM2)	PCIe	PCIe	Mezzanine (SXM2)
Cooling	Passive	Passive	Active	Passive
Manufacturing Process	TSMC 12nm FFN	TSMC 12nm FFN	TSMC 12nm FFN	TSMC 16nm FinFET
Architecture	Volta	Volta	Volta	Pascal

It should be noted that this upgrade is a wholesale replacement of the existing 16GB versions in NVIDIA’s product stack, as NVIDIA won’t be retaining the 16GB versions now that the 32GB versions are out. So all new cards sold by NVIDIA going forward will be the 32GB cards, and OEMs will be making the same transition in Q2 as their 16GB stocks are depleted and replaced with 32GB cards.

NVIDIA’s Tesla V100-equipped systems will also be getting the same upgrade. Both the DGX-1 server and DGX Station will now ship with the 32GB cards. And while OEMs are a bit farther behind, ultimately I expect they’ll make the same move since the 16GB cards are going away anyhow. Meanwhile NVIDIA isn’t officially talking about pricing here for either the new V100 cards or the updated DGX systems. However as the new parts are essentially drop-in replacements in their respective ongoing lines, there aren’t any signs right now that pricing is changing.

As far as workloads go, since the specifications of the Tesla V100 aren’t changing outside of memory capacity, the benefit and performance impact of this upgrade is almost entirely rooted in dataset size. Workloads that rely on datasets that couldn’t previously fit entirely within a Tesla V100 card should see a sizable boost, as will any applications that can benefit from more local memory for caching purposes. However in pure compute-bound scenarios, the new cards shouldn’t perform any differently than the outgoing models.

Meanwhile, as for NVIDIA's workstation-oriented Titan V, nothing is being announced at this time. The 12GB card could easily be bumped to 24GB using the 8-Hi stacks, however it's not clear if NVIDIA is in a hurry to do so. In practice it will almost certainly come down to whether NVIDIA wants to keep GV100 GPU production to a single line for efficiency reasons, or if they'll operate two lines as part of a broader product segmentation strategy.

Lastly, this upgrade means that NVIDIA has finally caught up with arch-rival AMD in total memory capacity. AMD has been shipping a 32GB FirePro S9170 card using 16x2GB GDDR5 modules for the better part of the last 3 years now, a mark that NVIDIA couldn’t catch up to until 8-Hi HBM2 was ready. The Tesla V100 has always been significantly more powerful than the W9100 regardless, but this finally brings NVIDIA to parity in the one area where they were trailing AMD in the server space.

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