Streaming Response Implementation
Deliver LLM responses in real-time with streaming. Reduce perceived latency by 90% and improve user experience with progressive response rendering.
Server-Sent Events Implementation
// Client-side streaming handler const streamCompletion = async (prompt) => { const response = await fetch('https://api.parrotrouter.com/v1/chat/completions', { method: 'POST', headers: { 'Content-Type': 'application/json', 'Authorization': 'Bearer YOUR_API_KEY' }, body: JSON.stringify({ model: 'gpt-4', messages: [{ role: 'user', content: prompt }], stream: true }) }); const reader = response.body.getReader(); const decoder = new TextDecoder(); while (true) { const { done, value } = await reader.read(); if (done) break; const chunk = decoder.decode(value); const lines = chunk.split('\n'); for (const line of lines) { if (line.startsWith('data: ')) { const data = JSON.parse(line.slice(6)); if (data.choices[0].delta?.content) { updateUI(data.choices[0].delta.content); } } } } }
First Token
50ms
Time to first response
Perceived Speed
90%
Faster perception
User Satisfaction
85%
Improvement score
References
- [1] arXiv. "Efficient LLM Inference" (2024)
- [2] Hugging Face. "GPU Inference Optimization" (2024)
- [3] NVIDIA. "LLM Inference Optimization" (2024)