Idanwo ti o sopọ mọ itọsọna · Lile Ipele

Tesiwaju Batching adanwo

Apply iteration-level LLM batching, capacity limits, and prefill tradeoffs, including why admitting requests continuously does not guarantee zero waiting.

Jẹmọ itọsọna awọn ọnaTesiwaju Batching
Ibeere 1 ti 8

Kini ailera akọkọ ti batching aimi (ti o wa titi) fun iṣẹ LLM?

Awotẹlẹ gbogbo ibeere8 ibeere

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

  1. Kini ailera akọkọ ti batching aimi (ti o wa titi) fun iṣẹ LLM?
    • O le nikan ṣiṣe awọn lori CPUs
    • O nilo fifi aami si gbogbo data ni akọkọ
    • Awọn ibeere kukuru pari ni kutukutu ṣugbọn awọn iho wọn laišišẹ lakoko ti ipele n duro de ọkan ti o gunjulo
    • O mu ki awọn awoṣe kere deede
  2. Ni granularity wo ni batching lemọlemọfún ṣafikun ati yọ awọn ibeere kuro?
    • Lẹẹkan fun ọjọ kan
    • Ni igbese iyipada kọọkan (aami kan ni akoko kan)
    • Fun kikun ibeere nikan
    • Fun akoko ikẹkọ
  3. A continuous-batching request finishes and frees capacity. What can the scheduler do next?
    • Restart every remaining request from its first token
    • Guarantee every queued request runs immediately
    • Discard the finished answer without returning it
    • Admit eligible queued work if its resource budgets allow
  4. Ilana wo ni o so pọ nipa ti ara pẹlu batching lemọlemọfún lati jẹ ki fifi sii / yiyọ awọn ilana jẹ olowo poku?
    • Kaṣe KV ti o da lori Àkọsílẹ PagedAttention
    • FP32 ė konge
    • Disiki yiyipada
    • Afọwọṣe data isamisi
  5. Iwe iwadi wo ni o jẹ olokiki pẹlu ipele aṣetunṣe olokiki (tẹsiwaju) batching?
    • Awọn atilẹba Amunawa iwe
    • Iwe Orca
    • A 1980 database iwe
    • Iwe ImageNet
  6. What utilization problem can continuous batching reduce compared with a fixed request batch?
    • Every model accuracy error
    • The need to store any KV cache
    • All queueing at any traffic rate
    • Waiting for the longest request before admitting new eligible work
  7. A continuous-batching server has reached its memory budget. Must a newly arrived request enter the next iteration?
    • No; admission can wait until suitable capacity is available
    • Yes; the name guarantees unlimited request admission
    • Yes; the server removes all active outputs automatically
    • Only if the request has no punctuation
  8. A long prompt’s prefill delays tokens for already decoding requests. Which serving objective exposes that tradeoff?
    • Measure only the weight-file name
    • Assume more admitted prompts always improve every latency
    • Measure time between decode tokens alongside throughput
    • Count only the number of model layers