vLLM vs SGLang Inference Engine Comparison: Optimizing LLM VRAM & Latency with Speculative Decoding & Chunked Prefill

vLLM vs SGLang Inference Engine Comparison: Speculative Decoding & Chunked Prefill

Deploying Large Language Models (LLMs) in production environments presents two primary bottlenecks: GPU VRAM exhaustion and high inference latency. As concurrent user traffic scales, KV Cache memory consumption grows rapidly, causing Time To First Token (TTFT) spikes and potential OOM crashes.

To solve this, two leading open-source serving engines have emerged: vLLM (powered by PagedAttention) and SGLang (powered by RadixAttention).

This guide analyzes the memory architecture differences between vLLM and SGLang, and demonstrates how combining Chunked Prefill with Speculative Decoding dramatically minimizes VRAM footprint and token generation latency.

1. vLLM (PagedAttention) vs SGLang (RadixAttention)

2. 2 Core Latency & VRAM Optimization Techniques

1) Chunked Prefill

When long prompt processing (Prefill phase) coincides with token generation (Decode phase), GPU compute spikes. Chunked Prefill breaks large prompt inputs into fixed chunks (e.g., 512 or 1024 tokens) to balance GPU utilization across concurrent requests.

2) Speculative Decoding

Speculative Decoding leverages a lightweight Draft Model (e.g., Qwen2.5-0.5B) to quickly guess multiple token candidates, which are then verified in a single forward pass by the primary Target Model (e.g., Qwen2.5-72B). This yields 2x–3x decoding speedups without degrading output quality.

3. Production Deployment Code Examples

vLLM Speculative Decoding CLI Command

Since vLLM v0.9.0 (2025-05-28), standalone flags like --speculative-model and --num-speculative-tokens have been deprecated and removed from the source. Running the old-style command now fails with an unrecognized arguments error — pass a single JSON object via --speculative-config instead, as shown below.

# vLLM: Speculative Decoding (Target: Llama-3.1-70B, Draft: Llama-3.1-8B)
# NOTE: --speculative-model / --num-speculative-tokens were removed in vLLM v0.9.0+.
# Passing them now raises "unrecognized arguments"; use the single --speculative-config JSON flag instead.
python3 -m vllm.entrypoints.openai.api_server \
    --model meta-llama/Meta-Llama-3.1-70B-Instruct \
    --speculative-config '{"model": "meta-llama/Meta-Llama-3.1-8B-Instruct", "num_speculative_tokens": 5}' \
    --enable-chunked-prefill \
    --max-num-batched-tokens 2048 \
    --gpu-memory-utilization 0.90 \
    --tensor-parallel-size 4

SGLang Speculative Decoding & Radix Cache Launch (Python)

When using a separate draft model, you must explicitly set speculative_algorithm="STANDALONE". It defaults to None, so omitting it means Speculative Decoding stays off even if you set a draft model path and token count.

import sglang as sgl

@sgl.function
def multi_turn_agent(s, system_prompt, user_query):
    s += sgl.system(system_prompt)
    s += sgl.user(user_query)
    s += sgl.assistant(sgl.gen("response", max_tokens=1024, temperature=0.2))

if __name__ == "__main__":
    runtime = sgl.Runtime(
        model_path="meta-llama/Meta-Llama-3.1-70B-Instruct",
        speculative_algorithm="STANDALONE",
        speculative_draft_model_path="meta-llama/Meta-Llama-3.1-8B-Instruct",
        speculative_num_draft_tokens=5,
        mem_fraction_static=0.88,
        tp_size=4
    )
    sgl.set_default_backend(runtime)

4. Benchmark Summary & Selection Guide

Final Decision Matrix

1. General RAG & Massive Concurrency: Choose vLLM for its ecosystem stability and mature tooling.
2. AI Agents & High Cache-Hit Systems: Choose SGLang for RadixAttention’s superior prompt prefix reuse.
3. Enabling Speculative Decoding on either engine unlocks 2x+ throughput gains with zero quality loss.