What is Inference Optimization?

Inference Optimization encompasses techniques for making language model predictions faster, more memory-efficient, and cost-effective. As models scale to hundreds of billions of parameters, optimization becomes critical for practical deployment.

Key Optimization Categories

1. Quantization

Reduce numerical precision to decrease memory and compute.

Precision	Memory	Speed	Quality
FP32	4 bytes	Baseline	Best
FP16	2 bytes	~2x	Very Good
INT8	1 byte	~4x	Good
INT4	0.5 bytes	~8x	Acceptable

See Quantization for details.

2. KV Cache

Cache key-value pairs to avoid recomputation during autoregressive generation. Critical for long sequences.

3. Batching Strategies

• Static Batching: Fixed batch size
• Dynamic/Continuous Batching: Add/remove requests dynamically
• PagedAttention: Memory-efficient KV cache management (vLLM)

4. Speculative Decoding

Use a smaller “draft” model to propose multiple tokens, then verify with the main model in parallel.

• Speedup: 2-3x for suitable model pairs
• Quality: Identical to base model (verified)

5. Model Architecture Optimizations

• Flash Attention: Efficient attention computation
• Sliding Window Attention: Limit attention span
• Grouped Query Attention (GQA): Fewer KV heads

Inference Frameworks

Framework	Specialty
vLLM	High-throughput serving
TensorRT-LLM	NVIDIA optimization
Ollama	Local ease-of-use
llama.cpp	CPU inference, GGUF
text-generation-inference	Production serving

Hardware Considerations

Hardware	Best For
H100/A100	Maximum throughput
RTX 4090	Cost-effective high-end
M1/M2/M3	Apple ecosystem
CPU	Accessibility

Optimization Trade-offs

Technique	Speed Gain	Quality Impact	Complexity
Quantization	High	Low-Medium	Low
Batching	High	None	Medium
Speculative	Medium	None	High
Flash Attention	Medium	None	Low

Related Concepts

• Quantization - Weight compression
• Flash Attention - Attention optimization
• VRAM - Memory requirements
• GGUF - Quantized model format