Ampere Altra server processors
E802943
Ampere Altra server processors are 64-bit ARM-based data center CPUs designed for high core counts, energy efficiency, and scalable cloud-native workloads.
All labels observed (2)
| Label | Occurrences |
|---|---|
| Ampere Altra processors | 1 |
| Ampere Altra server processors canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T9499076 — 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: Ampere Altra server processors Context triple: [ARMv8-A, usedIn, Ampere Altra server processors]
-
A.
Fujitsu SPARC servers
Fujitsu SPARC servers are enterprise-grade computer systems built around SPARC RISC processors, designed for high reliability, scalability, and performance in mission-critical workloads.
-
B.
AMD Opteron
AMD Opteron is a family of 64-bit x86 server and workstation processors from AMD designed for high-performance, multi-core, and multi-processor computing environments.
-
C.
Habana Gaudi processor
The Habana Gaudi processor is a specialized AI training accelerator designed by Habana Labs (an Intel company) to deliver high-performance, scalable deep learning computation in data centers.
-
D.
AMD EPYC
AMD EPYC is a family of high-performance server processors from AMD designed for data centers, cloud computing, and enterprise workloads.
-
E.
Intel Xeon
Intel Xeon is a family of high-performance x86 processors designed by Intel for servers, workstations, and data center applications requiring reliability, scalability, and advanced multi-core processing.
- 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: Ampere Altra server processors Target entity description: Ampere Altra server processors are 64-bit ARM-based data center CPUs designed for high core counts, energy efficiency, and scalable cloud-native workloads.
-
A.
Fujitsu SPARC servers
Fujitsu SPARC servers are enterprise-grade computer systems built around SPARC RISC processors, designed for high reliability, scalability, and performance in mission-critical workloads.
-
B.
AMD Opteron
AMD Opteron is a family of 64-bit x86 server and workstation processors from AMD designed for high-performance, multi-core, and multi-processor computing environments.
-
C.
Habana Gaudi processor
The Habana Gaudi processor is a specialized AI training accelerator designed by Habana Labs (an Intel company) to deliver high-performance, scalable deep learning computation in data centers.
-
D.
AMD EPYC
AMD EPYC is a family of high-performance server processors from AMD designed for data centers, cloud computing, and enterprise workloads.
-
E.
Intel Xeon
Intel Xeon is a family of high-performance x86 processors designed by Intel for servers, workstations, and data center applications requiring reliability, scalability, and advanced multi-core processing.
- F. None of above. chosen
Statements (49)
| Predicate | Object |
|---|---|
| instanceOf |
64-bit processor
ⓘ
ARM-based CPU ⓘ server processor ⓘ server processor family ⓘ |
| announced | 2020 ⓘ |
| architecture | ARMv8.2-A NERFINISHED ⓘ |
| basedOn | Ampere Altra architecture NERFINISHED ⓘ |
| cache |
per-core private L2 cache
ⓘ
shared L3 cache ⓘ |
| competesWith |
AMD EPYC
NERFINISHED
ⓘ
Intel Xeon Scalable NERFINISHED ⓘ |
| coreCount |
up to 128 cores
ⓘ
up to 80 cores ⓘ |
| designedFor |
cloud-native workloads
ⓘ
data center servers ⓘ scale-out workloads ⓘ |
| designGoal |
deterministic performance under load
ⓘ
linear performance scaling with cores ⓘ |
| focus |
energy efficiency
ⓘ
high core density ⓘ predictable performance ⓘ |
| instructionSet | ARMv8.2-A with server extensions ⓘ |
| manufacturer | Ampere Computing NERFINISHED ⓘ |
| powerEfficiency | optimized for performance per watt ⓘ |
| predecessor | Ampere eMAG NERFINISHED ⓘ |
| processNode | 7 nm ⓘ |
| segment | server CPUs ⓘ |
| socketType |
dual-socket capable
ⓘ
single-socket ⓘ |
| successor | AmpereOne NERFINISHED ⓘ |
| supports |
8-channel DDR4 memory
ⓘ
ARM Neoverse N1 cores NERFINISHED ⓘ ARMv8.2-A cryptography extensions NERFINISHED ⓘ ARMv8.2-A virtualization extensions ⓘ Kubernetes-based cloud-native stacks ⓘ Linux operating systems NERFINISHED ⓘ PCIe Gen4 ⓘ SVE-like SIMD via NEON (128-bit) ⓘ containerized workloads ⓘ virtual machines ⓘ |
| targetMarket |
cloud service providers
ⓘ
enterprise data centers ⓘ hyperscale data centers ⓘ |
| TDPRange | roughly 150–250 W depending on SKU ⓘ |
| useCase |
database workloads
ⓘ
edge and cloud infrastructure ⓘ microservices ⓘ web front-end services ⓘ |
| wordSize | 64-bit ⓘ |
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: Ampere Altra server processors Description of subject: Ampere Altra server processors are 64-bit ARM-based data center CPUs designed for high core counts, energy efficiency, and scalable cloud-native workloads.
Referenced by (2)
Full triples — surface form annotated when it differs from this entity's canonical label.