Idanwo ti o sopọ mọ itọsọna · Alabọde Ipele

Itọkasi Itọkasi LLM ati Iwontunwosi fifuye

Checks replica and model routing, cache affinity, live telemetry, variable token cost and latency-aware goodput for LLM inference.

Jẹmọ itọsọna awọn ọnaLLM Inference Routing Ati Fifuye Iwontunwonsi
Ibeere 1 ti 8

Kini idi ti iyipo-robin lasan nigbagbogbo jẹ ilana iwọntunwọnsi fifuye ti ko dara fun itọkasi LLM?

Awotẹlẹ gbogbo ibeere8 ibeere

Awọn idahun ati awọn alaye han bi o ṣe n mu ibeere naa.

  1. Kini idi ti iyipo-robin lasan nigbagbogbo jẹ ilana iwọntunwọnsi fifuye ti ko dara fun itọkasi LLM?
    • Awọn ibeere yatọ lọpọlọpọ ni idiyele ati awọn ẹda ẹda di awọn caches KV alalepo
    • O jẹ arufin ni ọpọlọpọ awọn orilẹ-ede
    • Yika-robin nikan ṣiṣẹ fun awọn apoti isura infomesonu
    • O nilo GPU kan lati ṣiṣẹ
  2. Kini 'iṣapejuwe-cache affinity' ti n gbiyanju lati ṣaṣeyọri?
    • Fifiranṣẹ ibeere kọọkan si GPU ID kan
    • Gbigbe ibeere kan si ajọra ti o ti fipamọ tẹlẹ ìpele kiakia
    • Encrypting tọ ṣaaju fifiranṣẹ
    • Compressing awọn iwọn awoṣe
  3. Ni ipa ọna awoṣe ti o da lori iṣoro, kini igbagbogbo n ṣẹlẹ si ibeere ti o rọrun?
    • O ti wa ni kọ
    • O ti firanṣẹ si awoṣe ti o tobi julọ ti o wa
    • O ti wa ni rán si kekere kan, din owo awoṣe
    • O ti wa ni ila fun wakati 24
  4. Eyi ti ifiwe ifihan agbara jẹ julọ wulo fun ohun LLM-mọ fifuye iwontunwonsi?
    • Gigun orukọ olupin olupin naa
    • Awọn awọ ti agbeko
    • Agbegbe aago ti olumulo nikan
    • Ijinle isinyi ati ibugbe KV-cache ti ajọra kọọkan
  5. Kini ọpa bii LiteLLM n pese ni iṣeto olupilẹṣẹ pupọ?
    • Aṣoju kan ṣoṣo ti o ṣe iwọntunwọnsi ati kuna lori kọja ọpọlọpọ awọn olupese awoṣe
    • A titun transformer faaji
    • A GPU itutu eto
    • A rirọpo fun tokenizer
  6. Kini idi ti ibeere igbejade gigun kan le kọja ọpọlọpọ awọn kukuru fun iwọntunwọnsi?
    • Awọn abajade gigun ko lo awọn orisun
    • Ọkọọkan ti o jade ni iye owo iwe-iwọle siwaju ni kikun, nitorinaa lapapọ fifuye àmi ṣe pataki ju kika ibeere lọ
    • Awọn ibeere kukuru nigbagbogbo losokepupo
    • Awọn olulana ko le ri ipari o wu ni gbogbo
  7. A serving system reports goodput rather than raw request count. What does goodput usually emphasize?
    • Lapapọ nọmba ti awọn paramita awoṣe
    • Only the number of HTTP connections opened
    • Requests served while meeting the specified latency or service-level targets
    • The fraction of requests that use the largest model
  8. A gateway first chooses a small or large model, then chooses a replica of that model. What are these two decisions?
    • Tokenization followed by decoding
    • Training followed by fine-tuning
    • Prefill followed by prompt construction
    • Model routing followed by replica load balancing