OpenAI Inference Cost Halving Deep Dive: How System-Level Optimizations Let Hundreds of GPUs Serve ChatGPT's Massive Traffic

package quantization

import (
	"math"
)

type DynamicQuantizer struct {
	quantMode string
}

type QuantizedTensor struct {
	Data     []byte
	Scale    []float32
	ZeroPt   []int32
	Mode     string
}

func NewDynamicQuantizer(mode string) *DynamicQuantizer {
	return &DynamicQuantizer{quantMode: mode}
}

// QuantizeFP16toINT8 quantizes FP16 weights to INT8 with group-wise scaling
func (q *DynamicQuantizer) QuantizeFP16toINT8(weights []float32, groupSize int) *QuantizedTensor {
	n := len(weights)
	if groupSize <= 0 {
		groupSize = n
	}
	numGroups := (n + groupSize - 1) / groupSize
	scales := make([]float32, numGroups)
	zeroPts := make([]int32, numGroups)
	quantized := make([]byte, n)

	for g := 0; g < numGroups; g++ {
		start := g * groupSize
		end := min(start+groupSize, n)

		// Find group min/max
		minVal, maxVal := float32(math.MaxFloat32), float32(-math.MaxFloat32)
		for i := start; i < end; i++ {
			if weights[i] < minVal { minVal = weights[i] }
			if weights[i] > maxVal { maxVal = weights[i] }
		}

		scale := (maxVal - minVal) / 255.0
		if scale < 1e-10 { scale = 1e-10 }
		zeroPt := int32(math.Round(float64(-minVal / scale)))
		zeroPt = clampInt32(zeroPt, 0, 255)

		scales[g] = scale
		zeroPts[g] = zeroPt

		for i := start; i < end; i++ {
			qVal := int32(math.Round(float64(weights[i]/scale))) + zeroPt
			quantized[i] = byte(clampInt32(qVal, 0, 255))
		}
	}

	return &QuantizedTensor{Data: quantized, Scale: scales, ZeroPt: zeroPts, Mode: "int8"}
}

// DequantizeINT8 restores INT8 weights to float32
func (q *DynamicQuantizer) DequantizeINT8(qt *QuantizedTensor, groupSize int) []float32 {
	result := make([]float32, len(qt.Data))
	for i := range qt.Data {
		g := i / groupSize
		result[i] = (float32(qt.Data[i]) - float32(qt.ZeroPt[g])) * qt.Scale[g]
	}
	return result
}

func clampInt32(v, min, max int32) int32 {
	if v < min { return min }
	if v > max { return max }
	return v
}

func min(a, b int) int { if a < b { return a }; return b }

import numpy as np
from typing import Dict, List, Optional, Tuple

class QuantizedKVCache:
    """FP16→INT8 KV-Cache with 50% memory reduction"""
    
    def quantize(self, tensor: np.ndarray, per_channel=True) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
        if per_channel:
            min_val = tensor.min(axis=(1, 2), keepdims=True)
            max_val = tensor.max(axis=(1, 2), keepdims=True)
        else:
            min_val = tensor.min(axis=(0, 1), keepdims=True)
            max_val = tensor.max(axis=(0, 1), keepdims=True)
        
        scale = (max_val - min_val) / 254.0
        scale = np.maximum(scale, 1e-10)
        zero_point = np.round(-min_val / scale).astype(np.int8)
        
        quantized = np.round(tensor / scale + zero_point).astype(np.int8)
        quantized = np.clip(quantized, -127, 127)
        return quantized, scale.astype(np.float16), zero_point
    
    def dequantize(self, quantized, scale, zero_point):
        return (quantized.astype(np.float16) - zero_point) * scale


class WindowedKVCache:
    """Sliding window + summary compression for efficient long context"""
    
    def __init__(self, window_size=2048, summary_tokens=128):
        self.window_size = window_size
        self.summary_tokens = summary_tokens
        self.window_k, self.window_v = [], []
        self.summary_k = self.summary_v = None
    
    def append(self, key: np.ndarray, value: np.ndarray):
        self.window_k.append(key)
        self.window_v.append(value)
        if len(self.window_k) > self.window_size:
            self._compress()
    
    def _compress(self):
        compress_size = len(self.window_k) // 2
        old_k = np.stack(self.window_k[:compress_size], axis=-1)
        old_v = np.stack(self.window_v[:compress_size], axis=-1)
        
        # Uniform sampling pooling
        if old_k.shape[-1] > self.summary_tokens:
            indices = np.linspace(0, old_k.shape[-1]-1, self.summary_tokens, dtype=int)
            pooled_k, pooled_v = old_k[..., indices], old_v[..., indices]
        else:
            pooled_k, pooled_v = old_k, old_v
        
        if self.summary_k is None:
            self.summary_k, self.summary_v = pooled_k, pooled_v
        else:
            combined_k = np.concatenate([self.summary_k, pooled_k], axis=-1)
            combined_v = np.concatenate([self.summary_v, pooled_v], axis=-1)
            idx = np.linspace(0, combined_k.shape[-1]-1, self.summary_tokens, dtype=int)
            self.summary_k, self.summary_v = combined_k[..., idx], combined_v[..., idx]
        
        self.window_k = self.window_k[compress_size:]
        self.window_v = self.window_v[compress_size:]
    
    def get_all(self) -> Tuple[np.ndarray, np.ndarray]:
        if self.summary_k is not None:
            all_k = np.concatenate([self.summary_k] + self.window_k, axis=-1)
            all_v = np.concatenate([self.summary_v] + self.window_v, axis=-1)
        else:
            all_k = np.stack(self.window_k, axis=-1)
            all_v = np.stack(self.window_v, axis=-1)
        return all_k, all_v

package scheduler

import (
	"sync"
	"sync/atomic"
	"time"
)

type Request struct {
	ID           string
	InputTokens  int
	MaxNewTokens int
	Priority     int // 0=realtime, 1=interactive, 2=batch
	ResultChan   chan *Result
}

type Result struct {
	LatencyMs    float64
	TokensPerSec float64
}

type ContinuousBatchScheduler struct {
	mu            sync.Mutex
	queues        [3][]*Request
	activeBatch   map[string]*Request
	maxBatchSize  int
	maxBatchTokens int
	gpuLoad       []int32
	gpuCapacity   int
}

func NewContinuousBatchScheduler(gpuCount int) *ContinuousBatchScheduler {
	return &ContinuousBatchScheduler{
		queues:        [3][]*Request{},
		activeBatch:   make(map[string]*Request),
		maxBatchSize:  64,
		maxBatchTokens: 32000,
		gpuLoad:       make([]int32, gpuCount),
		gpuCapacity:   16000,
	}
}

func (s *ContinuousBatchScheduler) Submit(req *Request) {
	s.mu.Lock()
	defer s.mu.Unlock()
	queue := &s.queues[req.Priority]
	*queue = append(*queue, req)
}

func (s *ContinuousBatchScheduler) popReady() []*Request {
	s.mu.Lock()
	defer s.mu.Unlock()

	var ready []*Request
	totalTokens := 0

	for pri := 0; pri < 3; pri++ {
		queue := &s.queues[pri]
		remaining := make([]*Request, 0, len(*queue))

		for _, req := range *queue {
			tokens := req.InputTokens + req.MaxNewTokens
			if len(ready) >= s.maxBatchSize || totalTokens+tokens > s.maxBatchTokens {
				remaining = append(remaining, req)
				continue
			}
			ready = append(ready, req)
			totalTokens += tokens
			s.activeBatch[req.ID] = req
		}
		*queue = remaining
	}
	return ready
}

func (s *ContinuousBatchScheduler) selectGPU() int {
	minLoad := int32(s.gpuCapacity + 1)
	bestGPU := -1
	for i := 0; i < len(s.gpuLoad); i++ {
		if load := atomic.LoadInt32(&s.gpuLoad[i]); load < minLoad {
			minLoad = load
			bestGPU = i
		}
	}
	if minLoad >= int32(s.gpuCapacity) {
		return -1
	}
	return bestGPU
}

func (s *ContinuousBatchScheduler) RunScheduler(stop <-chan struct{}) {
	ticker := time.NewTicker(10 * time.Millisecond)
	defer ticker.Stop()

	for {
		select {
		case <-stop:
			return
		case <-ticker.C:
			requests := s.popReady()
			if len(requests) == 0 {
				continue
			}
			gpuIdx := s.selectGPU()
			if gpuIdx < 0 {
				for _, req := range requests {
					s.Submit(req)
				}
				continue
			}
			go s.executeBatch(gpuIdx, requests)
		}
	}
}

func (s *ContinuousBatchScheduler) executeBatch(gpuIdx int, requests []*Request) {
	start := time.Now()
	batchTokens := 0
	for _, req := range requests {
		batchTokens += req.InputTokens + req.MaxNewTokens
	}
	atomic.AddInt32(&s.gpuLoad[gpuIdx], int32(batchTokens))

	// Simulated inference
	time.Sleep(50 * time.Millisecond)

	for _, req := range requests {
		result := &Result{
			LatencyMs: float64(time.Since(start).Milliseconds()),
			TokensPerSec: float64(req.MaxNewTokens) / time.Since(start).Seconds(),
		}
		select {
		case req.ResultChan <- result:
		default:
		}
		s.mu.Lock()
		delete(s.activeBatch, req.ID)
		s.mu.Unlock()
	}
	atomic.AddInt32(&s.gpuLoad[gpuIdx], -int32(batchTokens))
}

import numpy as np
from typing import Callable, List, Tuple

class SpeculativeDecoder:
    def __init__(self, target: Callable, draft: Callable, 
                 draft_steps=5, temperature=0.7):
        self.target = target
        self.draft = draft
        self.draft_steps = draft_steps
        self.temperature = temperature
        self.stats = {"accepted": 0, "rejected": 0, "steps": 0}
    
    def _sample(self, logits):
        logits = logits / self.temperature
        probs = np.exp(logits - logits.max())
        probs /= probs.sum()
        return int(np.random.choice(len(probs), p=probs)), float(probs.max())
    
    def _rejection_sample(self, draft_token, draft_prob, target_logits):
        target_probs = np.exp(target_logits - target_logits.max())
        target_probs /= target_probs.sum()
        target_prob = target_probs[draft_token]
        
        ratio = target_prob / (draft_prob + 1e-10)
        if ratio >= 1.0 or np.random.random() < ratio:
            return draft_token, True
        
        corrected = np.maximum(target_logits - draft_prob, 0)
        if corrected.sum() == 0:
            self.stats["rejected"] += 1
            return int(np.argmax(target_logits)), False
        
        corrected /= corrected.sum()
        self.stats["rejected"] += 1
        return int(np.random.choice(len(corrected), p=corrected)), False
    
    def generate(self, input_ids: List[int], max_new=256) -> List[int]:
        output = list(input_ids)
        k = self.draft_steps
        
        while len(output) - len(input_ids) < max_new:
            prefix = output[-4096:]
            
            # Phase 1: Draft K candidates
            draft_tokens, draft_probs = [], []
            draft_input = list(prefix)
            
            for _ in range(k):
                logits = self.draft(draft_input)
                token, prob = self._sample(logits[-1])
                draft_tokens.append(token)
                draft_probs.append(prob)
                draft_input = draft_input + [token]
            
            # Phase 2: Target parallel verification
            target_logits = self.target(prefix + draft_tokens)
            
            accepted = 0
            for i in range(k):
                token, ok = self._rejection_sample(
                    draft_tokens[i], draft_probs[i],
                    target_logits[-(k-i)]
                )
                output.append(token)
                if ok:
                    accepted += 1
                    self.stats["accepted"] += 1
                else:
                    break
            
            # If all accepted, generate one more
            if accepted == k:
                logits = self.target(output[-4096:])
                token, _ = self._sample(logits[-1])
                output.append(token)
            
            self.stats["steps"] += 1
        
        return output[len(input_ids):]