Optimizing LLM Model Performance for Inference

Inference optimization is the boundary between a prototype and a production-grade LLM application. While training receives the majority of research attention, the economics of serving are defined by throughput, latency, and memory efficiency at inference time. Every layer of the stack, from weight precision to request scheduling, offers leverage for reducing cost and improving user experience.

Quantization and Weight Compression

Post-training quantization reduces model weights from FP32 or BF16 to INT8, INT4, or FP8 formats. The goal is to shrink memory bandwidth pressure and increase the number of concurrent requests a GPU can service. INT8 quantization with scale maps often preserves perplexity within a single digit percentage of the original, while INT4 group-wise quantization can cut memory usage by nearly 75% at the cost of minor degradation in reasoning-heavy tasks.

When you self-host, you must validate accuracy on your own eval suite after quantization. Platforms like Oxlo.ai abstract this by serving optimized, pre-quantized variants of models such as DeepSeek R1 671B MoE and Llama 3.3 70B. Because Oxlo.ai handles the serving infrastructure, you avoid the engineering cycles required to maintain custom quantization pipelines.

# Example: Loading a quantized model locally with transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype="bfloat16"
)

model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-3.3-70B-Instruct",
    quantization_config=bnb_config,
    device_map="auto"
)

Continuous Batching and Request Scheduling

Static batching forces requests to wait until a fixed batch size is reached, which introduces unnecessary latency. Continuous batching, also called in-flight batching, dynamically inserts new requests into the GPU as soon as slots free up. This keeps compute units saturated and reduces time-to-first-token for incoming requests.

Implementing a production-grade continuous batching scheduler requires a custom inference engine such as vLLM or TensorRT-LLM. If you are not running your own GPU cluster, this complexity is externalized to your provider. Oxlo.ai schedules requests across its fleet with no cold starts on popular models, so you receive the latency benefits of advanced batching without managing the orchestration layer.

KV Cache Management

The KV cache is the dominant memory consumer during autoregressive generation. For long-context models, it can exceed the size of the weights themselves. Optimizations include: