NVIDIA GeForce RTX 2080 8GB
Overview
The GeForce RTX 2080 is a 46-SM TU104 and the card that put tensor cores in the mainstream tier. Its precision ladder is unusually well documented for a consumer part: NVIDIA prints a full column for it in both the Turing and the Ampere GA102 architecture whitepapers, so the FP16, INT8 and INT4 figures here are transcribed rather than derived. Like every GeForce Turing card it halves FP16 tensor throughput when accumulating in FP32, giving 42.4 TFLOPS in that mode against 84.8 in the FP16-accumulate one. There is no BF16, no TF32, no FP8 and no structured sparsity in this generation, so nothing here needs discounting before comparison. Two cards can be bridged over NVLink for 50 GB/s and 16 GB in total, though not as one address space.
Performance
Peak theoretical throughput by precision type
| Precision | Peak |
|---|---|
FP64 64-bit floating point | 0.3TFLOPS |
FP32 32-bit floating point | 11TFLOPS |
TF32 | No verified data available |
BF16 | No verified data available |
FP16 16-bit floating point | 42TFLOPS |
FP8 | No verified data available |
FP6 | No verified data available |
FP4 | No verified data available |
INT8 8-bit integer | 170TOPS |
INT4 4-bit integer | 339TOPS |
Every figure here is dense. The vendor documents no structured (2:4) sparsity mode for this part, so there is no second number to quote.
Per watt divides the dense figure by the 225 W board power. Where only a sparse figure exists, the cell says so.
The GeForce RTX 2080 8GB in the GPU landscape
Peak FP16 TFLOPS (dense) against TDP, single-GPU parts tracked by Flopper
Higher and further left is better: more half-precision throughput for less power.
Specifications
Architecture
Turing
Form Factor
PCIe
Launch Year
2018
Process Node
12nm
Memory
8 GB GDDR6
Bandwidth
448 GB/s
TDP
225 W
Max Power
225 W
Transistors
13.6 billion
Interconnect
50 GB/s NVLink
CUDA Cores
2,944
Spec Confidence
Official
Full Specifications
| Compute Engine | |
|---|---|
| CUDA Cores | 2,944 |
| Tensor Cores | 368 (Turing) |
| Streaming Multiprocessors | 46 |
| Base Clock | 1.51 GHz |
| Boost Clock | 1.80 GHz |
| Chip Design | |
| Transistors | 13.6 billion |
| Die Size | 545 mm² |
| Process Node | 12nm |
| Memory | |
| VRAM | 8 GB |
| Memory Type | GDDR6 |
| Bandwidth | 448 GB/s |
| Interface Width | 256-bit |
| Memory Clock | 14 GT/s |
| Interconnect & I/O | |
| GPU-to-GPU | NVLink |
| Interconnect Bandwidth | 50 GB/s |
| Interconnect Links | 1 |
| PCIe | 3.0 x16 |
| Cache | |
| L2 Cache | 4 MB |
| Power & Thermal | |
| TDP | 225 W |
| Max Board Power | 225 W |
| Cooling | Active |
| Enterprise Features | |
| ECC Memory | No |
| Sparsity | No |
| Compute APIs | CUDA, DirectX 12, Vulkan, OpenGL |
| General | |
| Form Factor | PCIe |
| Architecture | Turing |
| Process Node | 12nm |
| Launch Year | 2018 |
Datasheet & Resources
Flopper Datasheet
NVIDIA GeForce RTX 2080 8GB specifications, generated from our database. Printable.
Vendor product page
NVIDIA documentation for the GeForce RTX 2080 8GB
GeForce RTX 2080 product page (nvidia.com, archived 2018-11-09)
NVIDIA · 2018-08-20
NVIDIA Developer Documentation
Technical specs, programming guides
Data Provenance
Every figure traced to a source
Primary Source
- Publisher
- NVIDIA
- Published
- 2018-08-20
Data Quality
- Spec confidence
- Official
- Clock basis
- Boost
- Core precisions with figures
- 4 of 9
- Normalization
- All values in TFLOPS
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Frequently Asked Questions
How many TFLOPS does the NVIDIA GeForce RTX 2080 have?
The NVIDIA GeForce RTX 2080 delivers 11 TFLOPS FP32 and 42 TFLOPS FP16 at peak. Flopper does not currently have a verified FP8 throughput figure for it.
What is the power consumption of the NVIDIA GeForce RTX 2080?
The NVIDIA GeForce RTX 2080 has a TDP (Thermal Design Power) rating of 225 watts.
How much memory does the NVIDIA GeForce RTX 2080 have?
The NVIDIA GeForce RTX 2080 is equipped with 8 GB of VRAM with 448 GB/s of memory bandwidth.
What architecture is the NVIDIA GeForce RTX 2080 based on?
The NVIDIA GeForce RTX 2080 is based on the Turing architecture, launched in 2018.
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