NVIDIA Kepler architecture
E758261
NVIDIA Kepler architecture is a GPU microarchitecture designed to deliver high parallel computing performance and improved energy efficiency for graphics and high-performance computing workloads.
All labels observed (2)
| Label | Occurrences |
|---|---|
| NVIDIA Kepler architecture canonical | 1 |
| Nvidia Kepler | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T8822683 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: NVIDIA Kepler architecture Context triple: [NVIDIA Tesla data center GPUs, architectureBasedOn, NVIDIA Kepler architecture]
-
A.
NVIDIA Ampere architecture
NVIDIA Ampere architecture is a GPU microarchitecture from NVIDIA that powers RTX 30-series graphics cards, delivering significant improvements in ray tracing, AI performance, and power efficiency over previous generations.
-
B.
Nvidia Maxwell GPU
The Nvidia Maxwell GPU is a graphics processing architecture from Nvidia known for significantly improved power efficiency and performance, widely used in mid-2010s desktop, mobile, and embedded devices.
-
C.
NVIDIA Ada Lovelace architecture
NVIDIA Ada Lovelace architecture is a GPU microarchitecture from NVIDIA that powers the RTX 40-series graphics cards, delivering major advances in ray tracing, AI acceleration, and power efficiency over previous generations.
-
D.
NVIDIA Tesla data center GPUs
NVIDIA Tesla data center GPUs are high-performance graphics processing units designed for accelerated computing workloads such as AI, machine learning, and high-performance computing in server and data center environments.
-
E.
NVIDIA CUDA
NVIDIA CUDA is a parallel computing platform and programming model that enables developers to use NVIDIA GPUs for general-purpose high-performance computing.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: NVIDIA Kepler architecture Target entity description: NVIDIA Kepler architecture is a GPU microarchitecture designed to deliver high parallel computing performance and improved energy efficiency for graphics and high-performance computing workloads.
-
A.
NVIDIA Ampere architecture
NVIDIA Ampere architecture is a GPU microarchitecture from NVIDIA that powers RTX 30-series graphics cards, delivering significant improvements in ray tracing, AI performance, and power efficiency over previous generations.
-
B.
Nvidia Maxwell GPU
The Nvidia Maxwell GPU is a graphics processing architecture from Nvidia known for significantly improved power efficiency and performance, widely used in mid-2010s desktop, mobile, and embedded devices.
-
C.
NVIDIA Ada Lovelace architecture
NVIDIA Ada Lovelace architecture is a GPU microarchitecture from NVIDIA that powers the RTX 40-series graphics cards, delivering major advances in ray tracing, AI acceleration, and power efficiency over previous generations.
-
D.
NVIDIA Tesla data center GPUs
NVIDIA Tesla data center GPUs are high-performance graphics processing units designed for accelerated computing workloads such as AI, machine learning, and high-performance computing in server and data center environments.
-
E.
NVIDIA CUDA
NVIDIA CUDA is a parallel computing platform and programming model that enables developers to use NVIDIA GPUs for general-purpose high-performance computing.
- F. None of above. chosen
Statements (49)
| Predicate | Object |
|---|---|
| instanceOf | GPU microarchitecture ⓘ |
| architectureGeneration | NVIDIA 3rd-generation unified shader architecture NERFINISHED ⓘ |
| codename | Kepler NERFINISHED ⓘ |
| designGoal |
enhance GPU computing for HPC
ⓘ
improve compute density ⓘ increase performance per watt ⓘ |
| developer | NVIDIA NERFINISHED ⓘ |
| family |
NVIDIA GeForce
NERFINISHED
ⓘ
NVIDIA Quadro NERFINISHED ⓘ NVIDIA Tesla NERFINISHED ⓘ |
| firstLaunchYear | 2012 ⓘ |
| keyFeature |
ECC memory support on selected models
ⓘ
GPU Boost technology NERFINISHED ⓘ Hyper-Q NERFINISHED ⓘ PCI Express 3.0 support ⓘ SMX streaming multiprocessor design NERFINISHED ⓘ dynamic parallelism ⓘ enhanced warp schedulers ⓘ high parallel computing performance ⓘ improved energy efficiency ⓘ improved instruction throughput ⓘ improved power management ⓘ support for CUDA programming model ⓘ support for DirectX 11 ⓘ support for OpenGL 4.x ⓘ |
| marketSegment |
consumer graphics
ⓘ
data center computing ⓘ professional visualization ⓘ |
| notableImprovementOver |
higher shader count compared to Fermi
ⓘ
reduced power consumption compared to Fermi ⓘ |
| predecessor | NVIDIA Fermi architecture NERFINISHED ⓘ |
| processNode | 28 nm ⓘ |
| successor | NVIDIA Maxwell architecture NERFINISHED ⓘ |
| supportsAPI |
CUDA 5.x generation
ⓘ
DirectCompute NERFINISHED ⓘ OpenCL NERFINISHED ⓘ |
| supportsFeature | double-precision floating point on selected models ⓘ |
| targetApplication |
GPGPU
ⓘ
graphics processing ⓘ high-performance computing ⓘ |
| usedInProduct |
GeForce GTX 660 Ti
NERFINISHED
ⓘ
GeForce GTX 670 NERFINISHED ⓘ GeForce GTX 680 NERFINISHED ⓘ GeForce GTX 690 NERFINISHED ⓘ Quadro K5000 NERFINISHED ⓘ Quadro K6000 NERFINISHED ⓘ Tesla K20 NERFINISHED ⓘ Tesla K20X NERFINISHED ⓘ Tesla K40 NERFINISHED ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: NVIDIA Kepler architecture Description of subject: NVIDIA Kepler architecture is a GPU microarchitecture designed to deliver high parallel computing performance and improved energy efficiency for graphics and high-performance computing workloads.
Referenced by (2)
Full triples — surface form annotated when it differs from this entity's canonical label.
subject surface form:
Nvidia Maxwell
this entity surface form:
Nvidia Kepler