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1. سامبا نووا اپنی چپس کے لیے کون سا بنیادی فن تعمیر استعمال کرتا ہے؟
   • ایک قابل ترتیب ڈیٹا فلو ڈیزائن
   • ایک معیاری اسمارٹ فون پروسیسر
   • ایک مکمل طور پر مکینیکل کمپیوٹر
   • ایک کوانٹم صرف پروسیسر
2. 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
3. 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
4. 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
5. 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
6. Which person does SambaNova identify as a co-founder and chief technologist?
   • جیفری ہنٹن
   • Kunle Olukotun
   • فی-فی لی
   • ڈیمس حسابیس
7. 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
8. 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