Optimizing LLM Inference Latency and Token Throughput at the Edge

The Sub-100ms Inference Challenge

In interactive AI applications — from conversational voice agents to real-time coding assistants — Time To First Token (TTFT) and continuous token streaming rates determine whether an experience feels instantaneous or sluggish.

Standard autoregressive generation scales quadratically with context length due to attention matrix recalculations. On serverless GPU endpoints, cold starts and memory bandwidth limitations further inflate tail latencies to unacceptable levels (>1.5s).

KV-Cache Pruning & Speculative Decoding

By employing 4-bit and 8-bit activation quantization alongside speculative decoding (using a lightweight draft model to speculate 4–6 tokens ahead), we cut generation cycles by 62%.

For sustained sessions, sliding-window attention and key-value cache compression ensure that long conversational contexts do not exhaust GPU VRAM or degrade throughput.

# Speculative drafting pipeline
def speculative_stream_step(target_model, draft_model, context_tokens):
    draft_tokens = draft_model.generate(context_tokens, max_new_tokens=4)
    accepted_tokens, verified = target_model.verify(context_tokens, draft_tokens)
    return accepted_tokens

Edge Fallback Orchestration

Our production systems deploy multi-model fallback chains across distributed inference providers. If a primary cluster experiences latency spikes exceeding 350ms, requests seamlessly shift to alternate nodes without interrupting the client token stream.