Scaling LLM Inference at Enterprise Scale: Lessons from Production

Introduction

Deploying an LLM is easy. Deploying it at 100K+ requests/day with p99 latency under 500ms is where the real engineering begins. This post covers the techniques we use to scale LLM inference for production fintech workloads.

The Latency-Cost Tradeoff

Every optimization in LLM inference involves a tradeoff between three variables:

You can rarely improve all three simultaneously. The key is understanding which tradeoffs your use case can tolerate.

Technique 1: Quantization

Reducing model precision from FP16 to INT8/INT4 can cut memory usage by 2-4x with minimal quality loss:

from transformers import AutoModelForCausalLM, BitsAndBytesConfig
 
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype="float16",
    bnb_4bit_quant_type="nf4",
    bnb_4bit_use_double_quant=True,
)
 
model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-3-70B",
    quantization_config=quantization_config,
    device_map="auto",
)

Impact: 70B model fits on a single A100 (80GB) with ~1-2% quality degradation on benchmarks.

Technique 2: Continuous Batching

Traditional static batching wastes GPU cycles waiting for the longest sequence. Continuous batching dynamically adds/removes requests from the batch:

Throughput improvement: 2-5x over static batching
Tools: vLLM, TensorRT-LLM, TGI

Technique 3: KV-Cache Optimization

The key-value cache grows linearly with sequence length and batch size. Techniques to manage it:

• PagedAttention (vLLM): Manages KV-cache like virtual memory pages
• Sliding window: Fixed-size attention window for long contexts
• Prefix caching: Share KV-cache across requests with common system prompts

Technique 4: Speculative Decoding

Use a small "draft" model to generate $k$ candidate tokens, then verify in parallel with the large model:

Where $\alpha$ is the acceptance rate of the draft model's predictions.

Key Takeaways

1. Start with quantization — it's the highest ROI optimization
2. Use vLLM or TGI for serving — they handle continuous batching and PagedAttention out of the box
3. Profile before optimizing — use tools like torch.profiler to identify actual bottlenecks
4. Cache aggressively — system prompts, common queries, and embeddings

References

• Kwon et al., "Efficient Memory Management for Large Language Model Serving with PagedAttention" (2023)
• Leviathan et al., "Fast Inference from Transformers via Speculative Decoding" (2023)