LoopyLuci Large Language Model Optimization
Optimizing LLMs for production deployment — from quantization (GPTQ, AWQ, GGUF) through inference optimization (vLLM, FlashAttention), speculative decoding, and KV-cache management.
When to Use
- Deploying LLMs for production inference
- Reducing LLM latency and cost per request
- Serving LLMs at scale with batching
- Quantizing models for lower resource usage
- Implementing speculative decoding for speed
Optimization Techniques
LLM_OPTIMIZATIONS = {
'quantization': 'GPTQ (4-bit), AWQ (4-bit), GGUF (2-8 bit), bitsandbytes — reduce memory 2-4x',
'batching': 'Continuous batching (vLLM) — dynamic request coalescing for throughput',
'kv_cache': 'PagedAttention (vLLM), Prefix caching, sliding window — manage KV cache memory',
'speculative_decoding': 'Draft model generates candidates, target model verifies — 2x+ speedup',
'flash_attention': 'IO-aware exact attention — 2-4x faster, lower memory',
}
def estimate_model_memory(model_size_b: int, quantization_bits: int = 16) -> Dict:
"""Estimate GPU memory required for an LLM."""
params = model_size_b * 1e9
weights_memory = params * quantization_bits / 8 / 1e9 # GB
kv_cache_per_token = model_size_b * 2 * 2 / 1e9 # 2 bytes, 2 for K+V
overhead = 0.1 * weights_memory # Activations, optimizer states
return {
'weights_gb': round(weights_memory, 2),
'kv_cache_per_token_gb': round(kv_cache_per_token, 6),
'total_estimate_gb': round(weights_memory + overhead, 2)
}
Verification Checklist
- Quantization method chosen (GPTQ, AWQ, GGUF) matches deployment target
- Inference server selected (vLLM, llama.cpp, TGI) with continuous batching
- KV cache optimization (PagedAttention, prefix caching)
- Speculative decoding (if latency critical)
- FlashAttention enabled (Ampere+ GPUs)
- Throughput and latency benchmarked
- Accuracy validated post-quantization