LLM Inference Optimization Techniques

Running LLMs in production is expensive. Here’s how to cut costs by 70%.

1. Quantization

from transformers import AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-2-7b-hf",
    load_in_4bit=True,
    device_map="auto"
)

Precision	Memory	Speed	Quality
FP16	14GB	1x	100%
INT8	7GB	1.2x	99.5%
INT4	4GB	1.5x	98%

2. vLLM for Serving

from vllm import LLM, SamplingParams

llm = LLM(model="meta-llama/Llama-2-7b-hf")
params = SamplingParams(temperature=0.7, max_tokens=512)

outputs = llm.generate(prompts, params)

vLLM uses PagedAttention for 24x higher throughput.

3. Batching Strategies

async def batch_inference(requests, max_wait=50):
    batch = []
    deadline = time.time() + max_wait/1000
    
    while time.time() < deadline and len(batch) < 32:
        if requests:
            batch.append(requests.pop())
    
    return model.generate(batch)

4. KV Cache Optimization

llm = LLM(
    model="llama-7b",
    enable_prefix_caching=True
)

Results

Optimization	Latency	Cost
Baseline	500ms	$1.00
+ Quantization	350ms	$0.50
+ vLLM	150ms	$0.30
+ Batching	80ms	$0.15