High throughput and low latency are the defining constraints of production LLM infrastructure. Every millisecond of time-to-first-token and each incremental delay in generation directly impacts user experience and infrastructure cost. Yet optimizing inference is not simply a matter of selecting the largest model available. It requires a systematic approach to batching, memory management, quantization, and API design that aligns hardware utilization with the specific patterns of your workload.
Understanding the Inference Lifecycle
LLM inference consists of two distinct phases. The prefill phase processes the input prompt in parallel to construct the key-value cache, determining time-to-first-token. The decode phase generates output tokens autoregressively, one at a time, which governs time-per-output-token and overall perceived speed. Throughput is often measured in output tokens per second across all concurrent users, while latency is the end-to-end duration for a single request. Optimizing for both simultaneously is challenging because the techniques that maximize GPU compute utilization, such as larger batch sizes, often increase queuing delay for individual requests.
Batching Strategies for Higher Throughput
Static batching groups a fixed number of requests before running inference, which wastes GPU cycles if the batch is not full. Dynamic batching improves utilization by grouping requests that arrive within a short time window, though it still leaves gaps when sequences finish at different lengths. Continuous batching, also known as in-flight batching, replaces completed sequences with new requests inside the same GPU batch. This keeps the compute units saturated during the decode phase without forcing fast requests to wait for slow ones. When evaluating an inference provider, verify that the underlying stack supports this level of dynamic scheduling, because it is the single most important factor in serving many concurrent users without catastrophic latency spikes.
Memory Optimization and Quantization
The KV cache is frequently the memory bottleneck in high-throughput serving. For long-context workloads, the cache can exceed model weights in size, limiting batch size and increasing memory bandwidth pressure. Paged attention algorithms and KV cache quantization reduce this footprint by storing attention states more efficiently. Weight quantization, whether INT8, INT4, or FP8, also lowers memory bandwidth requirements and allows larger batches to fit in GPU memory. Architectures like mixture-of-experts further improve efficiency by activating only a subset of parameters per token. Models such as DeepSeek R1 671B MoE, DeepSeek V4 Flash, and GLM 5 available on Oxlo.ai leverage sparse routing to deliver high capability without proportionally increasing compute per forward pass.
Model Selection and Architecture
Inference optimization starts with choosing the right model for the latency and quality target. Dense models like Llama 3.3 70B and Qwen 3 32B offer strong general-purpose performance and predictable throughput. For deep reasoning or complex coding, DeepSeek R1 671B MoE and Kimi K2.6 provide advanced chain-of-thought capabilities with 131K context support. For agentic tool use, Minimax M2.5 and GLM 5 handle long-horizon tasks efficiently. Oxlo.ai hosts 45+ open-source and proprietary models across seven categories, including specialized coders such as Qwen 3 Coder 30B and Oxlo.ai Coder Fast, with no cold starts on popular models. This eliminates the hidden latency cost of waking up idle GPU instances, which is critical for synchronous user-facing applications.
Client-Side Optimization and API Patterns
Even with optimized infrastructure, client behavior affects perceived performance. Streaming responses improve time-to-first-token perception by delivering tokens as they are generated rather than buffering the entire completion. Reusing HTTP connections reduces TCP and TLS handshake overhead on every request. When using structured output modes such as JSON mode or function calling, only enable them when necessary, because constrained decoding can add compute overhead. Below is an example using the OpenAI SDK with Oxlo.ai to stream a
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