Batching a ɛkɔ so fi Nnyinasosɛm a Edi Kan (2025)

Continuous batching yɛ dynamic inference scheduling technique a ɛma hardware throughput yɛ kɛseɛ denam abisadeɛ foforɔ a wɔde bɛhyɛ active processing batch mu berɛ a slot bi free, yi idle compute cycles wɔ nnwuma ntam. Sɛ yɛte aseɛ firi nnyinasosɛm a ɛdi kan no mu a, ɛkyerɛ deɛ enti a abɛyɛ fapem nhyehyɛɛ ama AI som nhyehyɛeɛ biara a ɛyɛ adwuma yie a wɔde adi dwuma wɔ scale mu wɔ afe 2025.

Dɛn Pɛpɛɛpɛ ne Continuous Batching na Dɛn Nti na Static Batching dii nkogu?

Sɛ wopɛ sɛ wokyerɛ batching a ɛkɔ so ho anisɔ a, ɛsɛ sɛ wudi kan te nea ɛde sii ananmu no ase. Amanneɛ kwan so static batching boaboa abisadeɛ dodoɔ bi a wɔahyɛ ato hɔ ano, ɛyɛ ho adwuma sɛ unit baako, na ɛgye abisadeɛ foforɔ berɛ a batch no nyinaa awie nko ara. Mfomsoɔ a ɛho hia ne sɛ kasa nhwɛsoɔ akɛseɛ ma token a ne tenten sesa — abisadeɛ baako bɛtumi awie wɔ token 20 akyi berɛ a foforɔ a ɛwɔ batch korɔ no ara mu no tu mmirika 2,000. GPU biara a ɛwɔ cluster no mu no tena hɔ kwa twɛn sɛ ntoatoasoɔ a ɛware sen biara no bɛwie ansa na adwuma foforɔ biara ahyɛ aseɛ.

Batching a ɛkɔ so, a ɛyɛ akwampaefoɔ wɔ 2022 krataa a ɛyɛ nwonwa "Orca: A Distributed Serving System for Transformer-Based Generative Models," no bu saa anohyetoɔ yi so koraa. Ɛyɛ adwuma wɔ iteration level sen sɛ ɛbɛyɛ abisade level. Bere a ɛkɔ anim biako biara a ɛfa model no mu akyi no, nhyehyɛefo no hwɛ sɛ ebia ntoatoaso biara adu ne ntoatoaso awiei token no ho anaa. Sɛ ayɛ saa a, wɔsan gye saa slot no ntɛm ara na wɔde ma adesrɛ a ɛwɔ ntonto mu — twɛn biara nni hɔ, ɔsɛe biara nni hɔ. Batch composition no sesa fluidly wɔ decode anammɔn biara mu, na ɛma hardware dwumadie bɛn theoretical maximum bere nyinaa.

Ɔkwan bɛn so na KV Cache no ne Batching a ɛkɔ so wɔ System Level no di nkitaho?

Key-value cache no yɛ memory nhyehyeɛ a ɛma transformer inference yɛ tractable. Wɔ token biara a wɔayɛ ho adwuma no, model no bu attention keys ne values ​​a ɛsɛ sɛ wɔkora so sɛnea ɛbɛyɛ a tokens a edi hɔ no rennyɛ redundant computation bio. Wɔ static batching nhyehyɛe mu no, KV cache kyekyɛ yɛ tẽẽ: sie memory a ɛne sequence tenten a ɛsen biara hyia ma abisade biara a ɛwɔ batch no mu.

Batching a ɛkɔ so no ma eyi yɛ den fɛfɛɛfɛ. Esiane sɛ abisade ahorow hyɛn na wofi batch no mu wɔ mmere a wontumi nhu mu nti, nhyehyɛe no ntumi nni kan nkyekyɛ memory blocks a ɛtoatoa so a wɔasiesie. Eyi nti pɛpɛɛpɛ na vLLM PagedAttention — a wɔde bae wɔ afe 2023 mu — bɛyɛɛ nea wontumi ntetew mu mfi batching a ɛkɔ so wɔ production deployments mu. PagedAttention fɛm virtual memory paging model no fi operating systems mu, kyekyɛ KV cache mu ma ɛyɛ blocks a ɛnyɛ nea ɛtoatoa so a ne kɛse yɛ pɛ. Secence bi cache nkratafa betumi apete wɔ GPU memory so sɛnea virtual memory nkratafa apete wɔ physical RAM so no. Nea afi mu aba ne memory waste a ɛkame ayɛ sɛ zero a efi fragmentation mu, a ɛkyerɛ tẽẽ kɔ batch sizes a ɛkorɔn ne throughput a ɛkorɔn a hardware sika foforo nka ho.

Dɛn ne Nhyehyɛeɛ Titiriw Akwan a Ɛma Batching a Ɛkɔ So Yɛ Adwuma?

Nhyehyɛe ho gyinaesi abiɛsa a egyina wɔn ho wɔn ho so na ɛkyerɛ batching nhyehyɛe biara a ɛkɔ so:

• Preemption policy: Sɛ memory nhyɛsoɔ yɛ kɛseɛ na abisadeɛ foforɔ a ɛho hia kɛseɛ ba a, ɛsɛ sɛ scheduler no si gyinaeɛ sɛ ɔbɛdi kan ayɛ low-priority sequence a ɛretu mmirika, sesa ne KV cache akɔ CPU RAM, anaasɛ ɔbɛsan abɔ ho akontaa afiri mfitiaseɛ akyiri yi. Swap-based preemption kora akontabuo so nanso ɛdi PCIe bandwidth; recomputation sɛe GPU cycles nanso ɛma memory ho tew.
• Admission control: Ɛsɛ sɛ scheduler no kyerɛ sɛ ebia abisadeɛ foforɔ bi KV cache bɛfata wɔ memory a ɛwɔ hɔ mu wɔ n’awoɔ ntoatoasoɔ nkwa nna nyinaa mu anaa. Sɛ wobu no adewa a, ɛde afiri a ɛnyɛ nea wɔkae no ba wɔ mfinimfini nnidiso nnidiso; sɛ wobu akontaa boro so a, ɛma ɔkɔm de wɔn a wɔto ntonto no a ɛho nhia. Nnɛyi nhyehyɛe ahorow de profiled length distributions ne reservation buffers di dwuma de kari pɛ wɔ asiane ahorow yi mu.
• Chunked prefill: Prefill phase — a ɛdi ɔdefoɔ no input prompt ho dwuma — yɛ compute-bound na ɛbɛtumi ayɛ GPU no monopolize, atwe decode anammɔn a ɛwɔ ntoatoasoɔ a ɛrekɔ so dedaw no ase. Chunked prefill kyekyɛ nsɛm a wɔka kyerɛ atenten mu yɛ no chunks a ne kɛse yɛ pintinn a wɔde decode iterations abɔ mu, na ɛtew bere a wɔde kɔ token a edi kan no so ma wɔn a wɔde di dwuma bere koro mu no wɔ ɛka a wɔbɔ wɔ raw prefill throughput a ɛba fam kakra ho.
• Priority queuing: Enterprise deployments nkyekyɛmu abisadeɛ denam SLA tier so. API frɛ a ɛyɛ latency-sensitive di batch nnwuma a wɔbɔ mmɔden sen biara no anim. Sɛ saa layer yi nni hɔ a, nwoma tiawa adwuma tenten baako betumi asɛe nkitahodi a ɔde di dwuma no osuahu ama nhyiam ɔhaha pii a ɛkɔ so bere koro mu.

a wɔde ahyɛ mu

"Batching a ɛkɔ so no mma throughput ntu mpɔn kɛkɛ — ɛsan hyehyɛ sikasɛm mu nhwɛsoɔ a ɛfa AI inference ho. Ɛdenam GPUs a wɔma ɛkɔ so tra iteration granularity so sene sɛ wɔbɛbisa granularity so no, adwumayɛfoɔ nya 5–10× a ɛkorɔn a wɔde di dwuma yie firi hardware a ɛyɛ pɛ, a ɛyɛ lever kɛseɛ baako a ɛwɔ hɔ a ɛbɛtew per-token som ho ka so wɔ 2025 mu."

na ɛkyerɛ sɛ woayɛ

Ɛbɛyɛ dɛn na Wiase Ankasa Deployments Sua Adwumayɛ mu Mfaso?

| Mfasoɔ no da adi kɛseɛ berɛ a abisadeɛ tenten mu nsonsonoeɛ yɛ kɛseɛ — tebea a ɛkyerɛ nnwumayɛ nkɔmmɔbɔ AI adwuma mu adesoa pɛpɛɛpɛ a ɔdefoɔ nsɛmmisa firi nsɛmfua mmiɛnsa a wɔde bɛka akyerɛ kɔsi nkratafa pii nkrataa a wɔde mena.

Latency ka asɛm a ɛyɛ nuanced kɛse. Time-to-first-token tu mpɔn kɛse efisɛ nhyehyɛe no ntwɛn bio sɛ ​​static batch a edi mũ bɛboaboa ano ansa na afi ase ahyɛ prefill. Inter-token latency kɔ so yɛ stable wɔ moderate load ase nanso degrade gracefully under saturation mmom sen sɛ ɛbɛhwe ase, efisɛ scheduler no kɔ so nya nkɔso kɔ n’anim wɔ active sequences nyinaa mpo bere mpo queue no nyin kɔ akyiri. Wɔ nnwuma a wɔkyekye bere ankasa AI nneɛma no, saa graceful degradation curve yi taa yɛ nea ɛho hia kɛse wɔ aguadi mu sen peak throughput numbers.

Ɔkwan Bɛn so na Nnwuma Betumi De Batching Nnyinasosɛm a Ɛkɔ So Di Dwuma Asen AI Inference?

Adansi ho nhumu a ɛwɔ batching a ɛkɔ so akyi — san gye nneɛma wɔ granularity a eye sen biara mu na wɔsan de ma ntɛm ara sen sɛ wɔbɛtwɛn sɛ adwuma a ɛyɛ coarse-grained unit awie — yɛ nnyinasosɛm titiriw ma nhyehyɛe biara a ɛhwɛ adwuma a ɛsono emu biara so. Adwumayɛ nhyehyɛe ahorow hyia asɛnnennen koro no ara: nnwuma a ɛsono bere tenten a ɛne wɔn ho wɔn ho di akan wɔ tumi a wɔde di dwuma a wɔkyɛ wɔ CRM adwumayɛ mu, aguadi a wɔde di dwuma ankasa, nhwehwɛmu nsu afiri, ne e-commerce adwumayɛ.

Mewayz de saa nyansapɛ yi di dwuma wɔ ne 207-module adwumayɛ OS nyinaa mu, de ahoɔden fa adwumayɛ adwuma adesoa so wɔ nkabom kwan a nnwuma 138,000 de di dwuma wɔ wiase nyinaa so. Sɛ́ anka wɔbɛhyɛ akuw ma wɔatwɛn batch amanneɛbɔ kyinhyia, pene a wɔpene so nnidiso nnidiso, anaasɛ siled adwinnade handoffs, Mewayz di adwumayɛ mu nsɛm a esisi ho dwuma daa — ɛde outputs a wɔawie no ma ntɛm ara kɔ downstream modules mu ɔkwan a batching scheduler a ɛkɔ so de GPU slots a wɔade wɔn ho no san kɔ abisade ntonto no mu. Nea efi mu ba ne nkɔso a wotumi susuw wɔ throughput mu wɔ adwumayɛ dwumadi ankasa mu, ɛnyɛ benchmarks nko.

Nsɛmmisa a Wɔtaa Bisa

So batching a ɛkɔ so ne dynamic batching a ɛwɔ TensorFlow Serving mu no yɛ pɛ?

Dabi. TensorFlow Serving no dynamic batching no boaboa abisadeɛ ano ma ɛyɛ batches a ne kɛseɛ sesa a egyina berɛ mfɛnsere ne queue depth so, nanso ɛda so ara di batch biara ho dwuma wɔ atom kwan so firi mfitiaseɛ kɔsi awieeɛ. Batching a ɛkɔ so yɛ adwuma wɔ ankorankoro token awo ntoatoaso anammɔn no so, na ɛma batch composition sesa forward pass biara. Nsonsonoe a ɛwɔ granularity mu ne nea enti a batching a ɛkɔ so no nya throughput a ɛkorɔn kɛse ma autoregressive awo ntoatoaso adwumayɛ pɔtee.

So batching a ɛkɔ so hwehwɛ sɛ wɔsesa model architecture?

Standard transformer architectures nhia nsakraeɛ biara. Wɔde batching a ɛkɔ so di dwuma koraa wɔ serving layer no so denam nsakrae a ɛba inference scheduler, memory manager, ne attention kernel no so. Nanso, optimizations binom — titiriw PagedAttention — hwehwɛ CUDA kernels a wɔahyɛ da ayɛ a ɛsesa standard attention implementations, ɛno nti na production-grade continuous batching frameworks te sɛ vLLM ne TensorRT-LLM nyɛ drop-in replacements ma general-purpose inference servers.

Hardware anohyeto bɛn na ɛto batching a ɛkɔ so yiyedi ano hye?

GPU HBM bandwidth ne VRAM tumi nyinaa ne anohyeto titiriw. KV caches akɛse hwehwɛ memory pii, na ɛto concurrency a ɛsen biara ano hye. High-bandwidth interconnects (NVLink, Infiniband) bɛyɛ nea ɛho hia ma multi-GPU deployments a ɛsɛ sɛ wɔkyekyɛ KV cache wɔ mfiri ahorow so. Wɔ mmeae a memory-constrained no, aggressive quantization of KV cache values (efi FP16 kosi INT8 anaa INT4) san nya tumi wɔ ɛka a wɔbɔ wɔ pɛpɛɛpɛyɛ a wɔsɛe no ketewaa bi a wogye tom ma aguadi dwumadie dodoɔ no ara.

---

Sɛ́ ebia worekyekye nneɛma a AI na ɛyɛ adwuma anaasɛ worehyehyɛ adwumayɛ dwumadi a ɛyɛ den wɔ w’ahyehyɛde no nyinaa mu no, nnyinasosɛm a ɛwɔ ase no yɛ pɛ: yi bere a ɛnyɛ hwee fi hɔ, san nya tumi bere nyinaa, na fa nneɛma a wowɔ dedaw no di adwuma pii ho dwuma. Mewayz de saa nnyinasosɛm no di dwuma wɔ module ahorow 207 a wɔaka abom so — efi CRM ne e-commerce so kosi nhwehwɛmu ne kuw biakoyɛ so — efi ase fi $19 ɔsram biara.

Woasiesie wo ho sɛ wobɛma w’adwuma no ayɛ adwuma wɔ ahoɔden a edi mũ mu? Fi ase sɔ wo sɔhwɛ a wontua hwee wɔ app.mewayz.com na hwɛ sɛnea nnwuma 138,000 reyɛ adwuma nyansam wɔ Mewayz.