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shashank ms
shashank ms

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Optimizing LLM Inference for Low Latency: Techniques and Strategies

#costoptimization #oxlo #ai

Latency is the silent killer of interactive AI applications. When a user waits more than a few hundred milliseconds for the first token, engagement drops. For agentic workflows that chain multiple LLM calls, slow inference compounds into unacceptable round-trip delays. Optimizing LLM inference requires attacking both the prefill phase, where the model processes your prompt, and the decode phase, where it generates tokens serially. Platforms like Oxlo.ai remove cold-start overhead and offer request-based pricing, but the choices you make in model selection, prompt engineering, and client configuration still determine the latency your users experience.

Understand the Latency Budget

Two metrics dominate the latency profile. Time to First Token (TTFT) measures prefill speed. Time Per Output Token (TPOT) measures decode speed. Prefill is parallelizable but compute-heavy for long contexts. Decode is memory-bandwidth-bound because each new token depends on the full key-value cache. You cannot optimize what you do not measure, so instrument both.

Oxlo.ai keeps TTFT predictable by eliminating cold starts on popular models. Because pricing is request-based rather than token-based, you can optimize for latency by sending long prompts or extensive tool contexts without scaling costs forcing you to trim input length.

Model Selection and Quantization

Bigger models are not always slower, but they are rarely faster. Use the smallest model that meets your quality bar. Mixture-of-Experts architectures such as DeepSeek R1 671B or DeepSeek V4 Flash activate only a subset of parameters per token, delivering large-model reasoning with improved throughput. Oxlo.ai hosts both, alongside dense options like Qwen 3 32B and Llama 3.3 70B.

Quantization reduces weight precision from FP16 to INT8 or INT4. This shrinks memory bandwidth pressure during decode and increases feasible batch size. On Oxlo.ai, models are served with production-ready quantization that preserves accuracy without requiring you to manage calibration datasets.

import openai

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

# For low-latency coding assistance, DeepSeek V4 Flash offers 1M context
# and efficient MoE inference.
response = client.chat.completions.create(
    model="deepseek-v4-flash",
    messages=[{"role": "user", "content": "Refactor this function to O(n)."}],
    stream=True,
    max_tokens=512
)

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

Batching Strategies

Dynamic batching increases GPU utilization by grouping independent requests. Continuous batching, also called in-flight batching, inserts new requests into the GPU as soon as others hit a stopping condition. This keeps compute units saturated and reduces queueing delay.

As a client, you cannot control the scheduler directly, but you can avoid head-of-line blocking. Keep prompts concise and use streaming so the first token returns immediately rather than waiting for the full completion. Oxlo.ai supports streaming on all chat models, which lets you start rendering text while the model is still decoding.

KV Cache Optimization

The KV cache is the dominant memory consumer during decode. PagedAttention-style managers reduce fragmentation by storing cache in non-contiguous blocks. Prefix

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