Test chunk

future = asyncio.Future() self.pending_requests.append((request, future)) if len(self.pending_requests) >= self.max_batch_size: await self.flush() else: asyncio.ensure_future(self._timed_flush()) return future async def flush(self): if not self.pending_requests: return batch = self.pending_requests[:self.max_batch_size] self.pending_requests = self.pending_requests[self.max_batch_size:] requests = [r for r, _ in batch] futures = [f for _, f in batch] try: responses = await self.model.batch_generate(requests) for future, resp in zip(futures, responses): future.set_result(resp) except Exception as e: for future in futures: future.set_exception(e)

5.2 Speculative Decoding Acceleration

# Speculative Decoding:
# Step 1: Draft Model (small) generates K token candidates quickly
# Step 2: Target Model (large) validates all K tokens in parallel
# Step 3: Accept matches, regenerate at divergence point

speculative_config = {
    "draft_model": "claude-haiku-4-5",
    "target_model": "claude-opus-4",
    "num_speculative_tokens": 5,
    "acceptance_rate_target": 0.8,
    # Result: 2.5-3x throughput improvement
}

5.3 KV Cache Reuse and PagedAttention

# vLLM Deployment Optimization
paged_attention:
  enable: true
  block_size: 16
  gpu_memory_utilization: 0.90

cache_management:
  prefix_caching: true
  cache_dtype: fp16

scheduling:
  max_num_seqs: 256
  max_num_batched_tokens: 8192
  scheduler_delay_factor: 0.5

6. Cost Observability and Attribution System

6.1 Three-Layer Cost Measurement Model

# Layer 1: Infrastructure Costs
infra_costs = {
    "gpu_compute": "$2.50/hour A100-80GB",
    "load_