LLM Inference Optimization Techniques: Speed & Cost Guide 2025

LLM inference optimization is critical for production AI systems. Without optimization, running a 70B parameter model like Llama-2 requires 140GB of GPU memory at FP16 precision, costing $3-5 per million tokens on cloud platforms. Users expect sub-second response times, but naive implementations often take 5-15 seconds per response.

The challenge stems from three factors: memory bandwidth bottlenecks (GPUs spend most time moving weights from memory), sequential token generation (each token depends on previous tokens), and massive model sizes (requiring expensive hardware). Optimization techniques address these fundamental constraints.

Companies like OpenAI report 60-80% cost reductions through optimization, while maintaining comparable quality. For enterprises deploying LLMs at scale, optimization often determines project feasibility.

Quantization reduces numerical precision from 16-bit (FP16) to 8-bit (INT8) or 4-bit (INT4), dramatically reducing memory usage and increasing inference speed. Modern quantization techniques maintain 95-99% of original model quality while achieving 2-4x memory reductions.

• Post-Training Quantization (PTQ): Convert trained models without retraining. Tools like GPTQ and AWQ achieve excellent results on Llama and GPT models
• Quantization-Aware Training (QAT): Train models with quantization in mind. More accurate but requires full training pipeline
• Dynamic Quantization: Quantize activations at runtime while keeping weights in higher precision. Good balance of speed and accuracy

Popular quantization frameworks include HuggingFace Optimum for ONNX models, GPTQ for transformer models, and NVIDIA's TensorRT for production deployment. The choice depends on your hardware target and accuracy requirements.

Key-Value (KV) caching stores computed attention keys and values from previous tokens, eliminating redundant computations in multi-turn conversations. Without KV caching, each new token requires recomputing attention for the entire conversation history.

KV caching provides dramatic speedups for conversational AI and document QA applications. A 20-turn conversation sees 10-20x speedup compared to recomputing from scratch. However, KV cache memory grows linearly with sequence length, requiring careful memory management for long conversations.

# Example: KV caching with Transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

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

# Enable KV caching
model.config.use_cache = True

# First turn - cache is built
input_ids = tokenizer("Hello, how are you?", return_tensors="pt").input_ids
with torch.no_grad():
    outputs = model.generate(input_ids, max_new_tokens=50, use_cache=True)
    
# Subsequent turns reuse cache for massive speedup
next_input = tokenizer("What's the weather like?", return_tensors="pt").input_ids
with torch.no_grad():
    # This is much faster due to cached computations
    outputs = model.generate(next_input, max_new_tokens=50, use_cache=True)

Batching processes multiple requests simultaneously, dramatically improving GPU utilization. While single requests may underutilize GPU compute, batched inference can achieve 10-50x higher throughput depending on batch size and sequence length.

• Static Batching: Fixed batch sizes with padding. Simple but wastes compute on short sequences
• Dynamic Batching: Variable batch sizes based on sequence length. More complex but better utilization
• Continuous Batching: Add/remove requests from batches as they complete. Maximizes throughput for production systems

Production systems like vLLM and TensorRT-LLM implement advanced batching strategies. The optimal batch size depends on GPU memory, model size, and latency requirements. Start with batch sizes of 4-16 and measure throughput vs latency trade-offs.

Model parallelism splits large models across multiple GPUs when they don't fit on a single device. Two main strategies exist: tensor parallelism (split individual layers) and pipeline parallelism (split model vertically across layers).

Tensor parallelism distributes matrix multiplications across GPUs, requiring high-bandwidth interconnects (NVLink, InfiniBand) for efficiency. Pipeline parallelism processes different parts of the model on different GPUs sequentially, better for lower bandwidth connections.

• Tensor Parallelism: Best for 2-8 GPUs with high bandwidth. Linear scaling but requires fast interconnects
• Pipeline Parallelism: Good for 4+ GPUs. Lower bandwidth requirements but introduces pipeline bubbles
• Hybrid Approaches: Combine both strategies for very large models (70B+ parameters) across many GPUs

Frameworks like DeepSpeed, FairScale, and Megatron-LM provide production-ready model parallelism implementations. Choose based on your hardware configuration and model size.

Deployment strategy significantly impacts inference performance and costs. Cloud deployment offers powerful hardware but introduces network latency. Edge deployment provides low latency but requires model optimization for resource-constrained devices.

Cloud deployment leverages services like AWS Bedrock, Google Vertex AI, or Azure OpenAI. Benefits include managed infrastructure, auto-scaling, and access to latest GPUs. Costs range from $0.0015-0.12 per 1K tokens depending on model size and provider.

Edge deployment runs optimized models on user devices or edge servers. Apple's Core ML, Google's MediaPipe, and ONNX Runtime enable mobile deployment. Quantized 7B models can run on high-end smartphones with 100-500ms latency.

Hybrid approaches use small models for simple queries and route complex requests to cloud models. This balances cost, latency, and capability. Implement routing logic based on query complexity, user context, or confidence scores.

LLM inference costs can quickly spiral without proper optimization. A single A100 GPU costs $3-5/hour on major cloud platforms. Serving 1 million requests per day with a 70B model can cost $50,000-100,000 monthly without optimization.

• Model Size Optimization: Use smallest model that meets quality requirements. 7B models often perform 80% as well as 70B models for many tasks
• Efficient Serving: Implement continuous batching and dynamic scaling to maximize GPU utilization
• Caching: Cache common responses and use semantic similarity to serve similar queries
• Spot Instances: Use preemptible GPUs for batch workloads. Can reduce costs by 60-80% with proper fault tolerance

Monitor key cost metrics: tokens per dollar, GPU utilization percentage, and requests per GPU-hour. Set up alerting for unusual cost spikes and regularly audit your serving infrastructure for optimization opportunities.