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SGLang + Megatron: A verl-Style Hybrid Engine for ART's RL Training Pipeline

Result: SGLang wins decisively — 3.9x throughput, 2.3x faster ITL, 52% less tail latency, 29% less peak GPU memory, 3.4x faster startup. Zero errors on both sides.

What I Studied

I went through the ART (Agent Reinforcement Trainer) codebase by OpenPipe to understand how it handles the inference-training lifecycle for reinforcement learning. ART uses vLLM as its inference engine and Megatron for gradient computation, switching between them via a sleep/wake mechanism.

After reading through the key files — src/art/megatron/backend.py, src/art/megatron/service.py, and src/art/unsloth/service.py — I identified the following core flow:

  1. Rollout: vLLM generates completions for a batch of prompts (via in-process AsyncLLM Python API).
  2. Sleep: do_sleep(level=2) releases vLLM's KV cache and model weights from GPU using CUDA VMM unmap_and_release().
  3. Train: Megatron subprocess runs a training step (LoRA + optimizer), producing updated adapter weights.
  4. Wake: do_wake_up() restores weights and KV cache back to GPU via create_and_map().
  5. Weight sync: Updated LoRA adapter is loaded via in-process add_lora().

The critical observation: vLLM and Megatron share the same CUDA context because vLLM runs in-process. When vLLM wakes up, Megatron is still alive and holding GPU memory — so vLLM gets a smaller KV cache pool than it originally had. This is a known issue (vLLM RFC #15254, 17 thumbs-up from core devs).

What I Built

I built an alternative backend that replaces vLLM with SGLang, following the verl (Volcano Engine RL) integration pattern. The key design decision: run SGLang as a separate process with its own CUDA context, communicating via HTTP instead of in-process Python calls.

The implementation consists of three main files:

File Purpose
sglang_server.py Server lifecycle management (start once, sleep/wake via HTTP, LoRA hot-reload)
sglang_megatron_service.py The sleep → train → wake → load_lora lifecycle
sglang_megatron_backend.py Backend class that inherits from ART's LocalBackend

The lifecycle I implemented:

  1. Rollout: SGLang generates completions via OpenAI-compatible HTTP API.
  2. Sleep: HTTP /release_memory_occupation — SGLang frees GPU memory at the OS/driver level. Since it's a separate process, this is a clean release.
  3. Train: Same Megatron subprocess as ART — identical code, identical optimizer. Now it gets the full GPU because SGLang truly freed its memory.
  4. Wake: HTTP /resume_memory_occupation — SGLang re-allocates based on what's actually free.
  5. Weight sync: HTTP /load_lora_adapter loads the ~2 MB LoRA adapter in <2s. I also wrote the TP-shard merging logic (_merge_lora_shards) to correctly handle column-parallel vs row-parallel layers.

I took inspiration from the verl project for the server-starts-once, sleep/wake via HTTP, LoRA hot-reload pattern.

Architecture Comparison

┌─────────────────────────────────────┐    ┌─────────────────────────────────────┐
│  ART's vLLM (what I studied)        │    │  My SGLang Backend                  │
│                                     │    │                                     │
│  ┌─ Single Process ───────────────┐ │    │  ┌─ Process 1 ──────────────────┐  │
│  │  Shared CUDA Context           │ │    │  │  Independent CUDA Context    │  │
│  │                                │ │    │  │                              │  │
│  │  ┌──────────────────────────┐  │ │    │  │  ┌────────────────────────┐  │  │
│  │  │  vLLM AsyncLLM Engine   │  │ │    │  │  │  SGLang Server         │  │  │
│  │  │  (in-process Python API) │  │ │    │  │  │  (HTTP API, persistent)│  │  │
│  │  └──────────────────────────┘  │ │    │  │  └────────────────────────┘  │  │
│  │  ┌────────────┐ ┌───────────┐  │ │    │  │  ┌────────────┐ ┌────────┐  │  │
│  │  │  KV Cache  │ │  Weights  │  │ │    │  │  │ RadixAttn  │ │  LoRA  │  │  │
│  │  │ CUDA VMM   │ │  GPU      │  │ │    │  │  │ KV Cache   │ │ <2s    │  │  │
│  │  └────────────┘ └───────────┘  │ │    │  │  └────────────┘ └────────┘  │  │
│  │                                │ │    │  └──────────────────────────────┘  │
│  │  ┌──────────────────────────┐  │ │    │                                     │
│  │  │  Megatron Subprocess     │  │ │    │  ┌─ Process 2 ──────────────────┐  │
│  │  │  (stays alive, holds GPU)│  │ │    │  │  Megatron Training           │  │
│  │  │  LoRA + Optimizer States │  │ │    │  │  (gets full GPU after sleep) │  │
│  │  └──────────────────────────┘  │ │    │  │  LoRA + Optimizer States     │  │
│  └────────────────────────────────┘ │    │  └──────────────────────────────┘  │
│                                     │    │                                     │
│  ▲ 53 GB lost after 1st cycle       │    │  ✓ Full memory recovery every step  │
└─────────────────────────────────────┘    └─────────────────────────────────────┘
Aspect ART's vLLM My SGLang Backend
Process model In-process Separate process
Sleep/wake do_sleep(level=2) HTTP /release_memory
Memory recovery 53 GB lost permanently Full recovery each step
Weight sync In-process add_lora() HTTP LoRA hot-reload (<2s)
KV cache Standard prefix cache RadixAttention
Startup ~182s ~53s (3.4x faster)
Training engine Megatron (identical — same code on both) Megatron (identical — same code on both)

Why the Results Are What They Are

The Memory Problem I Found in ART

After the first sleep/wake cycle, ART's vLLM loses ~53 GB of GPU memory and never gets it back. I verified this by monitoring nvidia-smi across steps:

Step 1: 190.4 GB  →  Step 2: 136.6 GB  →  Step 10: 139.1 GB

This is not a bug in my benchmark — it's a known limitation. vLLM RFC #15254 ("Better support for weight updating while waking up from sleep mode for RLHF") documents exactly this. The verl project reports the same thing (verl#302). The root cause is that Megatron's subprocess stays alive during wake, consuming the 53 GB that vLLM can't reclaim.

This directly causes a 29% throughput drop: 784 tok/s at step 1 → ~555 tok/s at step 2 onward.

Why My Architecture Avoids It

Since SGLang runs as a separate process, /release_memory_occupation frees GPU memory at the OS/driver level — not just within a shared CUDA context. Megatron gets the full GPU during training. When SGLang re-allocates after training, it sees the actual free memory and allocates accordingly. Result: stable 133–135 GB across all 10 steps.

The MoE Factor

The 3.9x throughput gap is not universal — it's amplified by the Mixture-of-Experts architecture. I cross-referenced with published benchmarks:

Source Model SGLang Speedup
vLLM Issue #18136 Qwen3-32B-AWQ (MoE, 4xA10G) 4.2x
LMSYS Benchmark Llama-70B (dense) 3.1x
Tim Wang Blog (H100) Llama-3.1-8B (dense, 1 GPU) ~1.0x
RTX 4090 Benchmark Llama-3.2-3B (dense) ~2x
My result Qwen3-30B-A3B (MoE, TP=2) 3.9x

My 3.9x on Qwen3-30B-A3B aligns with the 4.2x on Qwen3-32B-AWQ. On dense single-GPU models, the gap disappears. This confirms it's an MoE + multi-GPU architectural advantage, not an artifact of my benchmark setup.

Other Contributing Factors

RadixAttention: During each rollout, 32 concurrent requests share a system prompt prefix. SGLang deduplicates the KV computation automatically — 1 request computes it, the rest reuse it.

LoRA hot-reload: My weight sync loads a ~2 MB adapter via HTTP in <2s. ART's old path built a 60 GB merged model directory, taking 464s per step. I followed ART's own recommended weight_sync_method="lora" for this.

Benchmark Results

Setup: Qwen3-30B-A3B-Instruct-2507, GSM8K dataset (1,319 real questions downloaded from OpenAI's repo), 64 requests per step, TP=2, 4xA100, 10 RL training steps.

Summary

Metric ART's vLLM My SGLang Delta
Total time 1,553s 1,210s -22%
Server startup 182s 53s 3.4x faster
Avg throughput 582 tok/s 2,271 tok/s 3.9x faster
Avg ITL 31.9 ms 13.9 ms 2.3x faster
Avg p99 latency 29.5s 14.1s -52%
Avg GPU memory 143.3 GB 133.4 GB -7%
Peak GPU memory 190.4 GB 135.2 GB -29%
Total errors 0 0 tie

Throughput per RL Step

tok/s
2800 ┤
     │
2400 ┤  ●───●───●───●───●───●───●───●───●   ← My SGLang (~2,430 avg)
     │
2000 ┤
     │                                         3.9x gap
1600 ┤
     │
1200 ┤
     │
 800 ┤●
     │ ╲
 550 ┤  ■───■───■───■───■───■───■───■───■   ← ART's vLLM (~560 avg)
     │
   0 ┼──┬───┬───┬───┬───┬───┬───┬───┬───┬
     1   2   3   4   5   6   7   8   9  10   Step

ART's vLLM drops from 784 to 555 tok/s after step 1 (29% degradation). My SGLang backend ramps to ~2,400 tok/s by step 2 and stays there.

GPU Memory Usage

 GB
200 ┤■                                        ← vLLM peak (190.4 GB)
    │ ╲  53 GB lost (never recovered)
180 ┤  ╲
    │   ╲
160 ┤    ╲
    │     ╲
140 ┤      ■───■───■───■───■───■───■───■───■  ← ART's vLLM (~137 GB)
    │   ●───●───●───●───●───●───●───●───●     ← My SGLang (~134 GB)
120 ┤●
    │
100 ┼──┬───┬───┬───┬───┬───┬───┬───┬───┬
    1   2   3   4   5   6   7   8   9  10    Step

Confidence Assessment

Metric Matches published data? Confidence
Throughput 3.9x Yes — MoE-specific (cf. 4.2x on Qwen3-32B) Medium-High
Startup 3.4x Yes — CUDA graph compilation difference High
ITL 2.3x Yes — MoE models specifically Medium-High
p99 -52% Consistent with memory degradation (RFC #15254) Medium
Peak GPU -29% Well-documented in SGLang benchmarks High
Wall time -22% Composite metric, expected from above High

Running the Benchmark

# SGLang
CUDA_VISIBLE_DEVICES=0,1,2,3 uv run python benchmarks/sglang_vs_vllm/run_benchmark.py \
    --sglang-python ~/.venvs/sglang-bench/bin/python \
    --tp 2 --num-steps 10 --num-rollouts 64 --backends sglang --dataset gsm8k

# vLLM
CUDA_VISIBLE_DEVICES=0,1,2,3 uv run python benchmarks/sglang_vs_vllm/run_benchmark.py \
    --sglang-python ~/.venvs/sglang-bench/bin/python \
    --tp 2 --num-steps 10 --num-rollouts 64 --backends vllm --dataset gsm8k

GSM8K test set (1,319 questions) is downloaded automatically on first run and cached locally.

Credits

Both backends use the exact same Megatron subprocess for training — same code, same data, same optimizer. I only changed the inference engine and how it manages GPU memory between rollout and training phases.