LLMS

Inference Optimization for LLMs: Quantization, Batching, KV Cache, and Speculative Decoding

LLM inference is slow: generating token-by-token is sequential. Optimize with quantization (smaller models), batching (throughput), KV cache (latency), and speculative decoding (faster generation).

Soham Sharma

AI Engineer, Botmartz · July 17, 2026 · 1 min read

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Inference Optimization for LLMs: Quantization, Batching, KV Cache, and Speculative Decoding

Quantization KV Cache Speculative Decoding Conclusion

LLM inference is I/O bound: each token generation requires matrix multiplications that are small relative to memory transfer. Optimize with quantization (reduce model size), batching (amortize I/O), KV cache (avoid recomputation), and speculative decoding (predict multiple tokens ahead).

Quantization

Reduce model precision: float32 → float16 → int8. Smaller models fit in smaller memory, reducing latency.

Code Snippet

import torch
from transformers import AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b")

# Quantize to int8
quantized_model = torch.quantization.quantize_dynamic(
    model, {torch.nn.Linear}, dtype=torch.qint8
)

# 4× smaller, faster inference

KV Cache

During generation, attention recomputes all keys/values. Cache them instead.

Code Snippet

Without KV cache: Each token recomputes attention for all previous tokens. O(n²)
With KV cache: Store keys/values, reuse. O(n)

This reduces latency 4-8× for long sequences.

Speculative Decoding

Generate multiple tokens ahead speculatively, then verify.

Code Snippet

1. Guess next 5 tokens using a fast model
2. Verify with main model: which guess is correct?
3. Keep correct tokens, speculate on remainder

This reduces latency by 2-3× with minimal accuracy loss.

Conclusion

Inference optimization is essential for production LLM serving. Each technique (quantization, KV cache, speculative decoding, batching) addresses a specific bottleneck. Combined, they enable real-time, cost-effective inference. Next: deploying LLMs—how to serve models reliably at scale.