LLM latency is not a single metric. It is the sum of network transit, queue time, prompt processing, and token generation. In production, every millisecond affects user retention and system stability. Reducing latency requires attacking the full inference lifecycle, from the shape of your prompt to the hardware scheduling your request. This guide covers the concrete techniques that work, and where Oxlo.ai fits into a low-latency strategy.
Understanding LLM Latency Components
Latency breaks into two measurable phases. Time to First Token (TTFT) covers the entire prefill stage, where the model processes your input prompt in parallel. Time Per Output Token (TPOT) covers the decode stage, where the model generates each new token autoregressively. Total latency equals TTFT plus the product of TPOT and the number of generated tokens.
Prefill is compute-bound and scales with input length. Decode is memory-bandwidth-bound and scales with output length. Optimizing one without the other leaves performance on the table. You need to shrink prefill through prompt engineering and model selection, and shrink decode through quantization, batching, and efficient inference engines.
Optimize Input Prompts to Shrink Prefill
Long contexts drive up TTFT. Even with providers that charge flat rates, the silicon still processes every token. Start by removing redundant system instructions and summarizing conversation history instead of appending full message logs. Use structured formats like JSON over verbose XML, and avoid repetitive few-shot examples when a compressed prompt achieves the same accuracy.
Because Oxlo.ai uses request-based pricing rather than token-based billing, you can experiment with context length without watching metered costs rise. That freedom lets you find the exact truncation point that balances accuracy and speed. You can send long prompts when necessary, but you are not penalized for iterating on shorter variants. See https://oxlo.ai/pricing for details on the flat per-request structure.
Model Selection, Quantization, and Architecture
Not every task requires the largest model. Routing simple queries to a smaller, faster model cuts both TTFT and TPOT. Oxlo.ai offers a spectrum of options: Qwen 3 32B for multilingual reasoning and agent workflows, Oxlo.ai Coder Fast for low-latency code completion, and DeepSeek V4 Flash, an efficient MoE with a one-million-token context window. For deep reasoning, DeepSeek R1 671B MoE or GLM 5 activate only a subset of parameters per forward pass, delivering higher throughput than dense counterparts at comparable quality.
Quantization also reduces memory bandwidth pressure. Where available, INT8 or INT4 weights lower TPOT by increasing cache efficiency and enabling higher batch sizes. Evaluate whether a quantized variant of your target model meets your quality bar before defaulting to full precision.
Inference Infrastructure Optimizations
Server-side mechanics matter. Continuous batching, also called in-flight batching, keeps the GPU saturated by dynamically grouping decode steps from multiple requests. Speculative decoding uses a small draft model to predict future tokens, then verifies them in parallel with the target model, reducing steps for the main forward pass.
Oxlo.ai runs popular models with no cold starts,
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