Nvidia Tegra K1
E939237
The Nvidia Tegra K1 is a mobile system-on-chip that introduced desktop-class Kepler GPU architecture to smartphones and tablets, significantly boosting their graphics and compute performance.
All labels observed (2)
| Label | Occurrences |
|---|---|
| Nvidia Tegra K1 canonical | 3 |
| Tegra K1 | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T11660312 — 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 Tegra K1 Context triple: [Nvidia Tegra X1, predecessor, Nvidia Tegra K1]
-
A.
Nvidia Tegra X1
The Nvidia Tegra X1 is a mobile system-on-chip that combines ARM CPU cores with an integrated Maxwell-based GPU, widely known for powering devices like the Nintendo Switch.
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B.
Nvidia Tegra 4
Nvidia Tegra 4 is a mobile system-on-chip by Nvidia that significantly improved performance and power efficiency for smartphones and tablets over its predecessor.
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C.
Nvidia Tegra X2
Nvidia Tegra X2 is a high-performance system-on-chip by Nvidia that combines ARM CPU cores with Pascal-based GPU architecture, designed primarily for embedded, automotive, and edge AI applications.
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D.
Nvidia Tegra 3
Nvidia Tegra 3 is a quad-core ARM-based mobile system-on-chip designed by Nvidia for tablets and smartphones, known for integrating CPU, GPU, and memory controller to deliver improved performance and power efficiency.
-
E.
Nvidia Tegra 2
Nvidia Tegra 2 is a dual-core ARM-based system-on-chip by Nvidia that powered many early smartphones and tablets, marking one of the first widely adopted multi-core mobile processors.
- 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 Tegra K1 Target entity description: The Nvidia Tegra K1 is a mobile system-on-chip that introduced desktop-class Kepler GPU architecture to smartphones and tablets, significantly boosting their graphics and compute performance.
-
A.
Nvidia Tegra X1
The Nvidia Tegra X1 is a mobile system-on-chip that combines ARM CPU cores with an integrated Maxwell-based GPU, widely known for powering devices like the Nintendo Switch.
-
B.
Nvidia Tegra 4
Nvidia Tegra 4 is a mobile system-on-chip by Nvidia that significantly improved performance and power efficiency for smartphones and tablets over its predecessor.
-
C.
Nvidia Tegra X2
Nvidia Tegra X2 is a high-performance system-on-chip by Nvidia that combines ARM CPU cores with Pascal-based GPU architecture, designed primarily for embedded, automotive, and edge AI applications.
-
D.
Nvidia Tegra 3
Nvidia Tegra 3 is a quad-core ARM-based mobile system-on-chip designed by Nvidia for tablets and smartphones, known for integrating CPU, GPU, and memory controller to deliver improved performance and power efficiency.
-
E.
Nvidia Tegra 2
Nvidia Tegra 2 is a dual-core ARM-based system-on-chip by Nvidia that powered many early smartphones and tablets, marking one of the first widely adopted multi-core mobile processors.
- F. None of above. chosen
Statements (45)
| Predicate | Object |
|---|---|
| instanceOf |
ARM-based system-on-chip
ⓘ
mobile system-on-chip ⓘ |
| announcedAt |
Consumer Electronics Show 2014
ⓘ
linked to:
Consumer Electronics Show
|
| announcementYear | 2014 ⓘ |
| applicationDomain |
embedded vision
ⓘ
high-performance mobile computing ⓘ mobile gaming ⓘ robotics ⓘ |
| architecture | ARM ⓘ |
| codename | Logan ⓘ |
| cpuCoreConfiguration | 4+1 core configuration ⓘ |
| cpuCoreCount | 5 ⓘ |
| developer |
Nvidia
ⓘ
linked to:
NVIDIA Corporation
|
| feature |
desktop-class Kepler GPU architecture
ⓘ
high compute performance for mobile devices ⓘ high graphics performance for mobile devices ⓘ |
| gpuArchitecture | Kepler ⓘ |
| gpuBrand |
Nvidia GeForce
ⓘ
linked to:
GeForce
|
| gpuCoreCount | 192 CUDA cores ⓘ |
| hasVariant |
Tegra K1 32-bit variant
ⓘ
Tegra K1 64-bit variant ⓘ |
| manufacturer |
Nvidia
ⓘ
linked to:
NVIDIA Corporation
|
| notableFor |
bringing Kepler GPU architecture to mobile devices
ⓘ
enabling advanced GPGPU compute on mobile platforms ⓘ significantly boosting mobile graphics performance ⓘ |
| predecessor | Nvidia Tegra 4 ⓘ |
| processNode | 28 nm ⓘ |
| successor | Nvidia Tegra X1 ⓘ |
| supports |
4K video output
ⓘ
CUDA ⓘ
linked to:
NVIDIA CUDA
DirectX 11 ⓘ
linked to:
Direct3D 11
OpenCL ⓘ OpenGL 4.4 ⓘ
linked to:
OpenGL
OpenGL ES 3.1 ⓘ
linked to:
OpenGL ES
compute shaders ⓘ hardware-accelerated video decoding ⓘ hardware-accelerated video encoding ⓘ tessellation ⓘ |
| targetDeviceType |
embedded systems
ⓘ
smartphones ⓘ tablets ⓘ |
| usedIn |
Google Nexus 9
ⓘ
linked to:
Nexus 9
Jetson TK1 development board ⓘ
linked to:
NVIDIA Jetson embedded modules
Nvidia Shield Tablet ⓘ
linked to:
NVIDIA Shield tablet
Xiaomi Mi Pad ⓘ |
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 Tegra K1 Description of subject: The Nvidia Tegra K1 is a mobile system-on-chip that introduced desktop-class Kepler GPU architecture to smartphones and tablets, significantly boosting their graphics and compute performance.
Referenced by (4)
Full triples — surface form annotated when it differs from this entity's canonical label.
linked to: Nvidia Tegra K1