LLM Text Completion: Latency Under 200ms with Streaming & Speculative Decoding

LLM Text Completion: Latency Under 200ms with Streaming & Speculative Decoding

User types a sentence, the system hangs for a second before showing a suggestion — does it sound familiar? This is the typical latency challenge of large language models in autocomplete: high p99 kills user experience. In production, we've seen users abandon input fields when latency exceeds 500 ms. We solve this by combining streaming, speculative decoding, and prefix caching, delivering suggestions under 200 ms without compromising prediction quality. The result? Users save up to 30% typing time thanks to relevant suggestions.

We deliver turnkey text completion systems — from simple n-grams to full LLM assistants with RAG and context adaptation. Our engineers have over 10 years of combined experience in NLP and MLOps, with hands-on work on OpenAI, Hugging Face, and vLLM. We assess your project in 1–2 days and propose a tailored architecture.

Types of Autocomplete and Their Limitations

The first two types are handled with fastText or small transformers; the third requires an LLM with generation. We help you pick the optimal fit for your scenarios.

Problems We Solve

High latency. In live typing, every millisecond counts. We use streaming via SSE — the first token appears in 100–150 ms, so the user sees the beginning of a suggestion almost instantly. We additionally apply speculative decoding: a small model (e.g., GPT-4o-mini) drafts, and a large model (GPT-4o) verifies. This yields 2–3× speedup. More about speculative decoding can be read on Wikipedia.

Context mismatch. Without context, models produce generic phrases. We feed the prompt with document topic, writing style, previous paragraphs, and key terms. For specialized editors (legal, medical), we use LoRA fine-tuning or a system prompt with a domain vocabulary.

Hallucinations and injections. Models may suggest inaccurate information or execute prompt injections. We block this through output validation and sandbox prompts. Additionally, we implement RAG: suggestions are grounded in your knowledge base, drastically cutting hallucinations.

Comparison of Latency Optimization Methods

# vLLM with speculative decoding
from vllm import LLM, SamplingParams

llm = LLM(model="gpt-4o", speculative_model="gpt-4o-mini", num_speculative_tokens=5)
params = SamplingParams(temperature=0.7, max_tokens=50, n=3)

How We Achieve Sub-200ms Latency

Our strategy includes four layers:

1. Streaming — return tokens via SSE. The user sees the suggestion growing.
2. Speculative decoding — accelerate generation 2–3× without quality loss.
3. Caching — if the prefix hasn't changed, serve a cached result.
4. Debouncing — trigger only after 300–500 ms typing pause.

How We Adapt the Model to Your Domain

Context adaptation is key for relevant suggestions. We use:

• System prompt describing the domain and desired style.
• Few-shot examples from your own data.
• LoRA fine-tuning for continuous adaptation (model updated monthly).
• RAG on ChromaDB or pgvector — suggestions reference current documents.

Why Streaming Is Critical for UX

Streaming lets users see the beginning of a suggestion after 100–150 ms instead of waiting for full generation. This drastically reduces perceived latency. In an A/B test for a legal document editor, switching from batch to streaming increased suggestion acceptance rate by 25%. Users reported a "snappy" feel even though total generation time stayed similar.

Concrete Case: Legal Drafting Assistant

We built an autocomplete assistant for a law firm's internal editor. The original system used a GPT-4 endpoint with batch output: p99 latency was 1.2 seconds, causing frequent user drop-off. After implementing streaming (SSE), speculative decoding (GPT-4o-mini drafts, GPT-4o verifies), and prefix caching, latency dropped to 180 ms p99. The firm saw a 30% reduction in average document drafting time. The system was fine-tuned with LoRA on 10,000 proprietary contracts, plus RAG on the firm's clause database.

Implementation with OpenAI API

from openai import OpenAI

client = OpenAI()

def autocomplete(text_prefix: str, context: str = "") -> list[str]:
    response = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[
            {"role": "system", "content": f"You help write text. Context: {context}"},
            {"role": "user", "content": f"Continue the text in three different ways:\n{text_prefix}"}
        ],
        max_tokens=50,
        n=3,
        temperature=0.7,
    )
    return [choice.message.content for choice in response.choices]

Process of Evaluation and Work

1. Analytics — audit current scenarios, collect data, define acceptable latency.
2. Design — select model (GPT-4o, Claude, LLaMA 3), inference architecture (vLLM, TGI), vectorize context.
3. Implementation — integrate API, set up streaming, caching, debouncing.
4. Testing — A/B tests, measure p99 latency, evaluate quality (relevance, hallucination rate).
5. Deployment — deploy on your infrastructure or in cloud (SageMaker, Vertex AI).

What's Included in the Deliverable

• Complete autocomplete system with latency <200 ms.
• API with documentation (OpenAPI spec).
• Monitoring dashboard (latency, throughput, cache hit rate).
• Maintenance and update instructions.
• Team training (3–5 working days).

Timelines: from 2 weeks for a basic solution to 6 weeks for a system with RAG and fine-tuning. Cost is estimated after an initial audit — contact us to discuss your case and receive an architectural proposal.