Updated, 12/8/2017, 2:45pm PT: Added credit for spec table
Nvidia announced a new Titan-class GPU that puts the previous model to shame. The company put the power of its GV100 Volta GPU into a desktop-class graphics card—for science!
Nvidia’s new Titan V offers an unprecedented level of computing performance in a single graphics card. The company said that the Titan V produces 9x more performance than the Titan Xp in deep learning compute tasks.
The Titan V features Nvidia’s GV100 GPU, which debuted earlier this year in the Tesla V100 data center card. The GV100 GPU is fabricated on TSMC 12nm FFN high-performance manufacturing process and boasts a massive 815mm2 die with 21.1 billion transistors. The chip features 5,120 Cuda cores for traditional GPU compute power,and 640 Tensor cores for deep learning. The Cuda cores and Tensors cores all operate at 1,200MHz with the potential to boost to 1,455MHz.
The Titan V cards also include 12GB of 1.7 Gb/s HMB2 memory that operates on a 3,072-bit memory bus and provides 653 GB/s of memory bandwidth. Nvidia said that the card also features a new combined L1 data cache and shared memory unit, which “improves performance while also simplifying programming.”
The Titan V’s Volta architecture also offers independent integer and floating-point data paths, which enables the GPU to handle workloads that require both computation and addressing calculations with better efficiency than previous GPU architectures could.
“Our vision for Volta was to push the outer limits of high performance computing and AI. We broke new ground with its new processor architecture, instructions, numerical formats, memory architecture and processor links,” said Jensen Huang, Founder, and CEO of Nvidia. “With TITAN V, we are putting Volta into the hands of researchers and scientists all over the world. I can’t wait to see their breakthrough discoveries.”
Comes With Software
Nvidia emphasized that the Titan V is meant for scientists and researchers. The company said that anyone who purchases a Titan V graphics card would be granted access to “Nvidia-optimised deep learning frameworks, third-party managed HPC applications, Nvidia HPC visualization tools, and the Nvidia TensorRT inferencing optimizer.”
This One’s Not For Games
Nvidia’s Titan series graphics cards were never meant for gamers. The cards are meant for scientists who can use the advanced computational capabilities of Nvidia’s graphics hardware. Though Nvidia doesn’t market the Titan directly to consumers, that doesn’t stop gamers with deep pockets from picking up the best GPU money could buy.
With the Titan V, gamers likely won’t be as enticed to drop one into their PC. The cards boast incredibly high Tensor compute performance, but it’s unclear how that would translate to gaming performance. What’s more, Nvidia isn’t asking $1,200 for the Titan V as it did with the Titan X, Titan X pascal, and Titan Xp. The Titan V is available now and you can order them directly from Nvidia's website, but this time around, Nvidia is asking for big bucks for the Titan-level card. If you want a Titan V, get ready to pony-up a whopping $2,999. That’s Titan Z territory, but the Titan V doesn’t include two GPUs as the Z did.
Spec Table Credit: AnandTech
| Product | Titan V | Tesla V100 (PCIe) | Tesla P100 (PCIe) | Titan Xp |
|---|---|---|---|---|
| CUDA Cores | 5,120 | 5,120 | 3,584 | 3,840 |
| Tensor Cores | 640 | 640 | N/A | N/A |
| Core Clock | 1,200MHz | ? | ? | 1,485MHz |
| Boost Clock(s) | 1,455MHz | 1,370MHz | 1,300MHz | 1,582MHz |
| Memory Clock | 1.7 Gb/s HBM2 | 1.75 Gb/s HBM2 | 1.4 Gb/s HBM2 | 11.4 Gb/s GDDR5X |
| Memory Bus Width | 3072-bit | 4096-bit | 4096-bit | 384-bit |
| Memory Bandwidth | 653 GB/s | 900 GB/s | 720 GB/s | 547 GB/s |
| VRAM | 12GB | 16GB | 16GB | 12GB |
| L2 Cache | 4.5MB | 6MB | 4MB | 3MB |
| Half Precision | 30 TFLOPS? | 28 TFLOPS | 18.7 TFLOPS | 0.19 TFLOPS (1/64 rate) |
| Single Precision | 15 TFLOPS | 14 TFLOPS | 9.3 TFLOPS | 12.1 TFLOPS |
| Double Precision | 7.5 TFLOPS? | 7 TFLOPS | 4.7 TFLOPS | 0.38 TFLOPS |
| Row 12 - Cell 0 | (1/2 rate) | (1/2 rate) | (1/2 rate) | (1/32 rate) |
| Tensor Performance (Deep Learning) | 110 TFLOPS | 112 TFLOPS | N/A | N/A |
| GPU | GV100 | GV100 | GP100 | GP102 |
| Die Size | 815mm2 | 815mm2 | 610mm2 | 471mm2 |
| Transistor Count | 21.1B | 21.1B | 15.3B | 12B |
| TDP | 250W | 250W | 250W | 250W |
| Form Factor | PCIe | PCIe | PCIe | PCIe |
| Cooling | Active | Passive | Passive | Active |
| Manufacturing Process | TSMC 12nm FFN | TSMC 12nm FFN | TSMC 16nm FinFET | TSMC 16nm FinFET |
| Architecture | Volta | Volta | Pascal | Pascal |
| Launch Date | 12/07/2017 | Q3'17 | Q4'16 | 04/07/2017 |
| Price | $2,999 | ~$10,000 | ~$6,000 | $1,299 |
