Optimizing LLM Inference for Low Latency

#aiinfrastructure #oxlo #ai

Low latency is the difference between an AI product that feels instant and one that feels broken. For interactive agents, coding assistants, and real-time chat, every millisecond of Time to First Token (TTFT) and Time Per Output Token (TPOT) directly impacts user retention. Optimizing LLM inference requires attacking latency across the full stack, from prompt construction and model selection to the inference provider’s serving infrastructure.

Understanding the Latency Stack

LLM inference latency is not a single number. It breaks down into at least three components:

Prefill (TTFT): The time to process the input prompt and generate the first token. This grows with prompt length and model size.
Decode (TPOT): The time to generate each subsequent token. Autoregressive models generate one token at a time, so this determines how fast text streams to the user.
Network overhead: TLS handshake, round trips, and payload serialization between your server and the inference provider.

You cannot optimize what you do not measure. Start by logging TTFT and inter-token latency in your client. If TTFT is high but TPOT is low, your bottleneck is prompt processing or queuing. If both are high, you may need a smaller model or a faster provider.

Model Selection and Quantization

The simplest way to reduce latency is to use a model that is fast enough for your quality threshold. Not every task requires a 400B+ parameter frontier model.

Use task-specific models. For coding, a model like Qwen 3 Coder 30B or Oxlo.ai Coder Fast on Oxlo.ai can outperform larger generalist models while cutting prefill and decode times dramatically.
Consider Mixture-of-Experts (MoE) architectures. Models like DeepSeek V4 Flash route each token to a subset of parameters, enabling fast inference with large effective capacity. Oxlo.ai serves DeepSeek V4 Flash with up to 1M context and near state-of-the-art open-source reasoning.
Evaluate vision and agentic needs separately. If your pipeline does not need vision, avoid multimodal models. When you do need vision, smaller vision-language models such as Kimi VL A3B or Gemma 3 27B on Oxlo.ai keep latency manageable.

Oxlo.ai hosts 45+ models across seven categories, so you can A/B test latency versus quality without rewriting integration code. Because the platform is fully OpenAI SDK compatible, switching from Llama 3.3 70B to Qwen 3 32B is a one-line parameter change.

Request Shaping and Batching

Latency often hides in the prompt. Long system prompts, repeated context, and verbose JSON schemas inflate TTFT.

Cache static context. If your system prompt and tool definitions do not change per request, store them in a way that your provider can reuse cached prefix computations. This directly reduces prefill time on repeated calls.
Truncate aggressively. Remove irrelevant conversation history. For multi-turn conversations, keep only the turns that affect the current task.
Use structured output and tool calling efficiently. Oxlo.ai supports JSON mode and function calling. Define compact schemas with fewer required fields. Smaller output spaces can sometimes speed up decoding because the model commits to valid tokens faster.
Stream responses. Always enable streaming for user-facing applications. It does not reduce total generation time, but it improves perceived latency by 50-90 percent.

Here is a minimal Python example showing streaming and JSON mode against Oxlo.ai:

from openai import OpenAI

client = OpenAI(
    base_url="https://api.oxlo.ai/v1",
    api_key="YOUR_OXLO_API_KEY"
)

response = client.chat.completions.create(
    model="Qwen 3 32B",
    messages=[
        {"role": "system", "content": "You are a concise coding assistant."},
        {"role": "user", "content": "Generate a Python function to validate an email address."}
    ],
    stream=True,
    response_format={"type": "json_object"}
)

for chunk in response:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="", flush=True)

Streaming with stream=True returns tokens as they are generated, which keeps the UI responsive even if total TPOT is moderate.

Infrastructure-Level Optimization

Your inference provider’s serving stack matters as much as your prompt. Key infrastructure factors include:

Cold starts. If a platform spins up GPUs on demand, the first request after idle time can take seconds. Oxlo.ai eliminates cold starts on popular models, meaning TTFT is consistent from the first request of the day to the thousandth.
Request-based pricing. Token-based billing creates a tension between latency and cost: shortening prompts saves money, but adding structured instructions or examples improves accuracy. Oxlo.ai uses flat per-request pricing, so you can send longer system prompts or include few-shot examples to improve quality without watching token costs scale. See https://oxlo.ai/pricing for plan details.
API compatibility. Oxlo.ai is a drop-in replacement for the OpenAI SDK. You do not need to refactor your client code to test it against your current provider. Change the base_url to https://api.oxlo.ai/v1 and compare TTFT side by side.

For agentic workloads that chain multiple tool calls, per-request pricing is especially relevant. Agents often send large context windows repeatedly; with token-based providers, those loops become expensive quickly. On Oxlo.ai, the cost stays flat per step, so you can prioritize latency and accuracy over token minimization.

Measuring and Benchmarking

Build a latency dashboard before you optimize. Track these metrics in production:

TTFT: End-to-end time from request dispatch to first token received.
TPOT: Average time between consecutive tokens.
Total request duration: Wall-clock time for the full response.
Queue time: Time spent waiting for an available GPU slot, visible as TTFT variance.

Run controlled tests with identical prompts across providers. Use a script that disables streaming and measures raw server performance. Keep the test prompt length close to your production median

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