refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조

에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게 [공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류. 문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인). - Topic_Programming → Domain_Programming (내부 구조 보존) - Topic_Graphic → Domain_Design - Topic_Business → Domain_Product - Topic_General → Domain_General - _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning), Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing) - 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서) - 빈 폴더 정리 (memory/procedures) - 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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+---
+id: wiki-2026-0508-batch-inference
+title: Batch Inference
+category: 10_Wiki/Topics
+status: verified
+canonical_id: self
+aliases: [batch inference, async inference, dynamic batching, continuous batching, throughput optimization]
+duplicate_of: none
+source_trust_level: A
+confidence_score: 0.93
+verification_status: applied
+tags: [inference, throughput, gpu, optimization, llm-serving, vllm, triton, ray]
+raw_sources: []
+last_reinforced: 2026-05-10
+github_commit: pending
+tech_stack:
+  language: Python
+  framework: vLLM / Triton / Ray Serve / Modal
+---
+
+# Batch Inference
+
+## 📌 한 줄 통찰
+> **"매 GPU 의 공동 구매"**. 매 single request 의 즉시 응답 X — 매 batch 의 throughput 의 maximize. 매 LLM 의 dynamic / continuous batching 의 5-20× throughput. 매 cost / latency trade-off 의 가장 큰 lever.
+
+## 📖 핵심
+
+### 매 inference type
+| 종류 | Latency | Throughput | Cost | 사례 |
+|---|---|---|---|---|
+| Online (sync) | <100ms | 매 low | 매 high | 매 chat, 매 search |
+| Batch (offline) | minute~hour | 매 max | 매 lowest | 매 daily summary, 매 fraud scan |
+| Async / queue | second~min | 매 mid | 매 mid | 매 image gen, 매 transcribe |
+
+### 매 batching 의 종류
+
+#### Static batching (전통)
+- 매 batch size 의 fix.
+- 매 batch 의 fill 의 wait → 매 latency variable.
+
+#### Dynamic batching (Triton)
+- 매 max wait time 의 limit.
+- 매 incoming request 의 group.
+- ✅ 매 latency / throughput balance.
+
+#### Continuous batching (vLLM, TensorRT-LLM)
+- 매 LLM 의 specific.
+- 매 sequence 의 finish 의 다른 sequence 의 immediately fill.
+- 매 GPU 의 idle 매 minimize.
+- 매 5-20× throughput.
+
+#### PagedAttention (vLLM)
+- 매 KV cache 의 page table.
+- 매 memory fragmentation 의 minimize.
+- 매 long context + batch 의 enable.
+
+### 매 batch size 의 effect
+- **Throughput**: 매 batch ↑ → 매 GPU util ↑.
+- **Latency** (per request): 매 wait ↑.
+- **Memory**: 매 batch ↑ → 매 OOM risk.
+- **Sweet spot**: 매 GPU memory + latency SLA 의 fit.
+
+### 매 batch inference 의 적용
+1. **Embedding generation**: 매 100M doc 의 batch.
+2. **Summarization**: 매 daily news.
+3. **Fraud detection**: 매 transaction 의 nightly.
+4. **Recommendation**: 매 user-item score 의 precompute.
+5. **Image classification** (archive): 매 medical image.
+6. **Translation** (corpus): 매 doc bulk.
+
+### Hybrid (modern LLM serving)
+- 매 online (chat) + 매 batch (precompute) 의 mix.
+- 매 priority queue 의 latency-sensitive 의 first.
+- 매 streaming 의 progressive output.
+
+### 매 monitoring
+- **Throughput**: token/s, request/s.
+- **Latency**: p50, p95, p99, TTFT (time to first token).
+- **GPU util**: 매 70-90% target.
+- **Batch size 의 distribution**.
+- **Queue depth**.
+
+## 💻 패턴
+
+### vLLM offline batch
+```python
+from vllm import LLM, SamplingParams
+
+llm = LLM(model='meta-llama/Llama-3-8B-Instruct')
+sampling = SamplingParams(temperature=0.7, max_tokens=512)
+
+prompts = [...]  # 매 10K
+outputs = llm.generate(prompts, sampling)
+# 매 continuous batching 의 self-managed
+```
+
+### vLLM online server (continuous batching)
+```bash
+python -m vllm.entrypoints.openai.api_server \
+  --model meta-llama/Llama-3-8B-Instruct \
+  --max-num-seqs 256 \
+  --gpu-memory-utilization 0.9
+```
+
+### Ray Batch inference
+```python
+import ray
+import ray.data
+
+ds = ray.data.read_parquet('s3://my-bucket/data/')
+
+class Predictor:
+    def __init__(self):
+        self.model = load_model()
+    def __call__(self, batch):
+        return {'pred': self.model(batch['features'])}
+
+predictions = ds.map_batches(Predictor, batch_size=64, num_gpus=1, concurrency=4)
+predictions.write_parquet('s3://my-bucket/predictions/')
+```
+
+### Triton dynamic batching
+```protobuf
+# config.pbtxt
+name: "my_model"
+platform: "onnxruntime_onnx"
+max_batch_size: 64
+dynamic_batching {
+  max_queue_delay_microseconds: 5000  # 5ms
+  preferred_batch_size: [16, 32, 64]
+}
+```
+
+### Custom batching (asyncio queue)
+```python
+import asyncio
+from collections import deque
+
+class BatchQueue:
+    def __init__(self, model, max_batch=32, max_wait_ms=10):
+        self.model = model
+        self.max_batch = max_batch
+        self.max_wait_ms = max_wait_ms
+        self.queue: deque = deque()
+        asyncio.create_task(self._loop())
+    
+    async def predict(self, x):
+        future = asyncio.Future()
+        self.queue.append((x, future))
+        return await future
+    
+    async def _loop(self):
+        while True:
+            if not self.queue:
+                await asyncio.sleep(0.001)
+                continue
+            await asyncio.sleep(self.max_wait_ms / 1000)
+            batch = []
+            while self.queue and len(batch) < self.max_batch:
+                batch.append(self.queue.popleft())
+            xs = [b[0] for b in batch]
+            preds = self.model(xs)
+            for (_, future), p in zip(batch, preds):
+                future.set_result(p)
+```
+
+### Cost optimization (spot + batch)
+```python
+# 매 batch job 의 spot instance OK
+# 매 1 hour SLA → 매 spot interrupt OK
+config = {
+    'instance_type': 'g5.2xlarge',
+    'pricing': 'spot',  # ~70% cheaper
+    'max_runtime_min': 60,
+    'retry_on_interrupt': True,
+}
+```
+
+## 🤔 결정 기준
+| 상황 | Strategy |
+|---|---|
+| Chat / search | Continuous batching (vLLM) |
+| Daily summary | Offline batch + spot |
+| Embedding 100M doc | Ray + GPU batch |
+| Image generation | Async queue + webhook |
+| Fraud nightly | Batch + cheap GPU |
+| RT API + bulk | Hybrid (priority queue) |
+
+**기본값**: vLLM (LLM) / Triton (general) / Ray (distributed).
+
+## 🔗 Graph
+- 변형: [[Continuous-Batching]] · [[Dynamic-Batching]] · [[Static-Batching]]
+- 응용: [[LLM_Optimization_and_Deployment_Strategies|vLLM]] · [[LLM_Optimization_and_Deployment_Strategies|PagedAttention]]
+- Adjacent: [[KV-Cache]] · [[LLM_Optimization_and_Deployment_Strategies|Inference-Optimization]]
+
+## 🤖 LLM 활용
+**언제**: 매 cost optimization. 매 throughput 우선 task. 매 LLM serving infra design.
+**언제 X**: 매 strict <100ms latency. 매 online interactive (single request).
+
+## ❌ 안티패턴
+- **Online 의 batch 의 force**: 매 latency violate.
+- **Static batch (LLM)**: 매 GPU idle.
+- **Batch size 의 max 의 OOM**: 매 retry storm.
+- **No max wait**: 매 indefinite delay.
+- **No monitoring**: 매 GPU util 의 모름.
+- **Spot 의 stateful job**: 매 interrupt 의 lose.
+
+## 🧪 검증 / 중복
+- Verified (vLLM paper, NVIDIA Triton, Ray).
+- 신뢰도 A.
+- Related: [[LLM_Optimization_and_Deployment_Strategies|vLLM]] · [[Continuous-Batching]] · [[GPU-Utilization]] · [[ML-Inference]].
+
+## 🕓 Changelog
+| 날짜 | 변경 |
+|---|---|
+| 2026-05-08 | Phase 1 |
+| 2026-05-10 | Manual cleanup — batching 종류 + vLLM + Triton + Ray + PagedAttention |