Optimizing production LLM inference requires balancing three opposing forces: maximizing throughput to saturate expensive accelerators, minimizing time-to-first-token and inter-token latency for responsiveness, and keeping power draw and thermal load within sustainable limits. Most engineering teams focus on one axis and degrade the others. The most efficient deployments treat inference as a systems problem that spans model architecture, serving software, and API design.
Continuous Batching and Dynamic Scheduling
Static batching leaves GPUs idle when individual requests finish at different times. Continuous batching, sometimes called in-flight batching, allows the inference engine to add new requests to a running GPU batch as soon as slots free up. This increases throughput without proportionally increasing latency for short requests.
At the platform level, cold starts destroy the gains from continuous batching because the GPU must warm up before it accepts traffic. Oxlo.ai eliminates cold starts on popular models, so dynamic schedulers can keep batches full and GPUs saturated from the first request. If you self-host with vLLM or TensorRT-LLM, enable in-flight batching in your model configuration and set max_num_seqs high enough to absorb traffic spikes without queuing.
Quantization and Weight Compression
Moving from FP16 to FP8, INT8, or INT4 quantization reduces memory bandwidth pressure and increases the number of concurrent requests that fit in VRAM. The tradeoff is a small degradation in reasoning quality that must be validated per task.
Oxlo.ai hosts a range of model sizes where quantization strategy directly impacts serving economics. For example, running a 70B parameter model at FP8 can nearly double effective batch size compared to FP16, while a 671B MoE model like DeepSeek R1 relies on sparse expert routing to keep active parameter counts manageable. When evaluating a quantized deployment, benchmark perplexity and downstream accuracy on your own prompts rather than relying on aggregate leaderboard scores.
KV Cache Management and Long Context
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