가이드 연계 퀴즈 · 중간 레벨
SambaNova 시스템 퀴즈
재구성 가능한 데이터 흐름, SN40L 메모리 계층 구조, 모델 번들링 및 고객별 배포 선택의 역할을 다룹니다.
질문 1 의 8
SambaNova는 칩에 어떤 핵심 아키텍처를 사용합니까?
모든 질문 미리보기8 질문
퀴즈를 풀면 정답과 설명이 나타납니다.
- SambaNova는 칩에 어떤 핵심 아키텍처를 사용합니까?
- 재구성 가능한 데이터 흐름 디자인
- 표준 스마트폰 프로세서
- 순수 기계식 컴퓨터
- 양자 전용 프로세서
- How does SambaNova describe data movement in its RDU dataflow architecture?
- Each operation must be manually converted into a database query
- Model operations are mapped into a graph, and data flows from one operation to the next as a pipeline
- The design removes every memory access
- A CPU analogy proves all workloads run faster
- What does SambaNova’s SambaStack product combine?
- Only a consumer chatbot subscription
- A food traceability database and camera
- Hardware and software as an integrated platform for AI inference
- A general-purpose laptop operating system
- How does the documented SN50 memory hierarchy place model data?
- All model weights remain on on-chip SRAM, so every model stays resident
- DDR replaces HBM for all active weights and hot KV cache
- Tiering eliminates all data movement and guarantees faster inference for every workload
- The documented SN50 setup uses HBM for active weights and hot KV cache, and DDR for larger model pools or colder KV cache, subject to configuration
- What can an on-premises SambaStack deployment offer an organization?
- An option to operate the platform in its own environment, subject to its deployment and contract terms
- A guarantee that no data can ever leave the environment
- Automatic compliance with every privacy law
- A promise that models cannot produce unsafe output
- Which person does SambaNova identify as a co-founder and chief technologist?
- 제프리 힌튼
- Kunle Olukotun
- 페이 페이 리
- 데미스 허사비스
- Which memory tiers does SambaNova list for its SN40L platform?
- Only CPU cache and hard disk
- GDDR and flash storage with no on-chip memory
- On-chip SRAM, HBM, and DDR
- A single tier of persistent memory
- What platform capability does SambaNova describe for multi-model workflows?
- It guarantees every model fits on one chip
- It removes the need to evaluate model compatibility
- It turns every model into a training dataset
- Model bundling can support multiple models in an inference or agent workflow