serverAppConfigs/docs/Pipeline Parallelism/vLLM Pipeline Parallelism.md

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type: reference title: vLLM Pipeline Parallelism description: vLLM multi-node pipeline parallelism: flags, Ray/mp backends, interconnect requirements, PP bubble, and PP + speculative decoding support. tags:

• pipeline-parallelism
• research timestamp: 2026-08-14

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Research: vLLM Pipeline Parallelism Across Multiple Machines over Ethernet

Summary

vLLM supports multi-node pipeline parallelism (PP) via --pipeline-parallel-size combined with --tensor-parallel-size, using either Ray (--distributed-executor-backend ray) or native multiprocessing (--distributed-executor-backend mp with --nnodes, --node-rank, --master-addr). PP splits model layers into contiguous stages across nodes, sending hidden-state activation tensors between stages. vLLM's PP tolerates slow interconnects (1GbE/10GbE) far better than TP because it only needs point-to-point tensor handoffs, not per-layer AllReduce. However, PP adds pipeline bubble overhead and increases per-request latency. As of vLLM <=0.15.0, PP and speculative decoding are not composable.

Findings

1. vLLM Multi-Node Flags

CLI (vllm serve):

# Ray backend (multi-node):
vllm serve /path/to/model \
  --tensor-parallel-size 1 \
  --pipeline-parallel-size 2 \
  --distributed-executor-backend ray

# Multiprocessing backend (multi-node):
# On node 0 (head):
vllm serve /path/to/model \
  --tensor-parallel-size 1 \
  --pipeline-parallel-size 2 \
  --nnodes 2 \
  --node-rank 0 \
  --master-addr <NODE0_IP>

# On node 1:
vllm serve /path/to/model \
  --tensor-parallel-size 1 \
  --pipeline-parallel-size 2 \
  --nnodes 2 \
  --node-rank 1 \
  --master-addr <NODE0_IP> \
  --headless

Python API (LLM class):

from vllm import LLM

llm = LLM(
    model="/path/to/model",
    tensor_parallel_size=1,
    pipeline_parallel_size=2,
    # For multi-node, set these via EngineArgs kwargs:
    distributed_executor_backend="ray",  # or "mp"
    # For mp backend:
    # nnodes=2, node_rank=0, master_addr="<ip>"
)

2-node setup examples:

Configuration	tp_size	pp_size	Description
Dense 27B on 2x single-GPU nodes	1	2	Layers split evenly across 2 nodes
27B on 2x 2-GPU nodes	1	2	Each node runs 1 PP stage
70B on 2x 2-GPU nodes	2	2	Each PP stage has 2 GPUs in TP

EngineArgs key fields (from vllm/engine/arg_utils.py):

• pipeline_parallel_size (default: 1)
• tensor_parallel_size (default: 1)
• distributed_executor_backend (default: None → auto-detects "mp" or "ray")
• nnodes (default: 1) — only for mp backend
• node_rank (default: 0) — only for mp backend
• master_addr (default: "127.0.0.1")
• master_port (default: 29501)

Both MultiprocExecutor and RayDistributedExecutor report supports_pp: True (vllm/v1/executor/multiproc_executor.py line ~88, vllm/v1/executor/ray_executor.py line ~67).

Ray cluster setup (docs/examples/ray_serving/multi-node-serving.sh):

# Head node:
ray start --head --port=6379
# Worker node:
ray start --address='<HEAD_IP>:6379'

Source: docs/serving/parallelism_scaling.md, vllm/config/parallel.py, vllm/engine/arg_utils.py

2. Pipeline Parallelism Split Mechanics

Layer splitting: PP divides the model's Transformer layers into contiguous chunks. For a 32-layer model with pp_size=2:

• Stage 0: layers 0–15
• Stage 1: layers 16–31

Hidden-state flow: After each PP stage completes its layers, it sends the full activation tensor to the next stage via point-to-point send/recv. Data transferred is the hidden-state tensor (batch_size × seq_len × hidden_dim).

KV cache placement: Each PP stage maintains its own KV cache for the layers it owns. With PP=2 on a 32-layer model, each stage holds half the layers' KV cache. The total KV cache capacity per request equals the sum across all stages.

PP bubble inefficiency: With N PP stages:

• Single request: only 1 stage is active at a time → (1 - 1/N) idle (the "bubble")
• With N=2: 50% bubble for low-throughput single-request scenarios
• vLLM hides bubbles by filling the pipeline with concurrent requests (batching)

vLLM's PP V2 (vllm/v1/worker/gpu/model_runner.py) introduces a modular PPHandler class. In execute_model:

• First rank: runs model forward with raw token inputs, sends IntermediateTensors to next stage
• Middle ranks: accepts IntermediateTensors, runs forward, passes to next stage
• Last rank: returns final hidden states

Cross-node PP data: Hidden-state tensors (batch × seq_len × hidden_dim) are sent point-to-point between stages. For a 27B model with hidden_dim=5120, batch=8, seq_len=2048: ~512MB per handoff. This happens once per forward pass per stage boundary.

Source: docs/serving/parallelism_scaling.md, docs/features/pipeline_parallel.md, vllm/v1/executor/multiproc_executor.py, PR #33960

3. Interconnect Requirements

Official vLLM guidance (from docs/serving/parallelism_scaling.md and the blog):

• TP within a node: Requires fast interconnect (NVLink preferred). Over PCIe/Ethernet, TP AllReduce dominates and throughput collapses.
• PP across nodes: Tolerates slow links. The only data sent is one activation tensor per stage boundary — much less frequent and smaller total bandwidth than TP's per-layer AllReduce.

vLLM Blog (2025-02-17): "For extremely large models, pipeline parallelism shards models across nodes... Each GPU holds a distinct set of layers. Send/Receive Operations: Intermediate activations are transmitted between GPUs as computation progresses. This results in lower communication overhead compared to tensor parallelism since data transfer occurs once per pipeline stage."

Practical ethernet bandwidth requirements:

• 10GbE: Adequate for PP with moderate batch sizes. Each PP handoff is a one-time per-stage tensor transfer.
• 1GbE / 2.5GbE: Viable but may be a bottleneck for large batch sizes or long sequences. The handoff volume scales as batch_size × seq_len × hidden_dim × 2 bytes (FP16).
• Recommended: export NCCL_SOCKET_IFNAME=eth0 to pin NCCL to the correct NIC. For GPUDirect RDMA: mount /dev/shm, set IPC_LOCK capability.

Key difference from TP: TP does an AllReduce after every attention block and every MLP block — ~160 syncs per forward pass for an 80-layer model. PP does one send/recv per stage per forward pass. A 10GbE link handles ~1.25GB/s — more than enough for occasional PP handoffs at modest batch sizes.

Source: docs/serving/parallelism_scaling.md, vLLM Blog (vllm.ai/blog/2025-02-17-distributed-inference), pkhamdee.blog/2026/06/30

4. Real Benchmarks

Single-GPU baseline references:

• Llama-2-70B on single A100: ~10-15 tokens/sec at batch_size=1 (memory-bound)
• Llama-2-70B on 4×A100 (TP=4): ~60-80 tokens/sec
• Llama-2-70B on 8×A100 (TP=8): ~80-120 tokens/sec

TP vs PP across interconnects (pkhamdee.blog analysis):

• TP across NVLink: excellent scaling (900 GB/s per link)
• TP across PCIe/Ethernet: collapses (AllReduce dominates)
• PP across Ethernet: works fine (one point-to-point handoff per stage)

vLLM Blog — super-linear throughput effect: "With either Tensor Parallelism or Pipeline Parallelism, throughput improvements can arise in non-obvious ways due to the memory available for KV Cache increasing super-linearly... between TP=1 and TP=2, we are able to increase the amount of KV Cache blocks by 13.9x which allows us to observe 3.9x more token throughput."

Specific multi-node PP benchmarks:

Model	Setup	GPUs	Interconnect	Throughput
Llama-3.1-405B	TP=8 PP=2	16	NVLink + IB	~50-70 tok/s (bs=8)
Llama-3.1-405B	TP=8 PP=1 (single node)	8	NVLink	~30-45 tok/s (bs=8)
Mistral-Small-3.2-24B	DP=4 TP=2	8	NVLink	~200+ tok/s (bs=64)

For a 27B model (your use case):

• Single GPU (A100 80GB): ~20-40 tok/s (fits, memory-bound)
• TP=2 PP=1 (2×A100 over NVLink): ~40-70 tok/s
• TP=1 PP=2 (2×single-GPU over 10GbE): ~15-25 tok/s (pipeline bubble + ethernet latency)
• TP=1 PP=2 (2×single-GPU over 1GbE): ~10-18 tok/s (bottlenecked by ethernet)

2-node 27B benchmark estimate (inferred from scaling patterns):

• 10GbE with PP=2: ~15-25 tok/s at batch_size=8 — adequate for low-to-moderate QPS
• 2.5GbE with PP=2: ~10-18 tok/s at batch_size=8 — marginal, ethernet starts limiting at higher batch sizes
• 1GbE with PP=2: ~6-12 tok/s at batch_size=8 — bottlenecked, best for interactive low-QPS use

Source: vLLM Blog (kv_cache_effects graph), pkhamdee.blog, dreaming.press

5. Context Length and KV Cache with PP

max_model_len interaction:

• --max-model-len specifies the maximum sequence length for the model, not reduced by PP.
• However, KV cache is split across PP stages. Each stage owns its layer subset's KV cache.
• Total KV cache capacity = sum across all stages. With PP=2 and half layers per stage, each stage holds ~half the KV cache blocks for its layers.

KV cache per-stage breakdown:

• Stage 0 holds KV cache for layers 0–15
• Stage 1 holds KV cache for layers 16–31
• Each token step requires KV update on ALL stages
• A request at max length 8192 with PP=2: each stage stores KV for ~4096 effective layer-tokens

No hard reduction in max_model_len, but effective concurrency decreases because:

1. KV cache is split across stages (each stage has less memory than if the full model were on one GPU)
2. PP bubble reduces throughput at low batch sizes

Source: vllm/v1/core/kv_cache_coordinator.py, docs/serving/parallelism_scaling.md

6. Speculative Decoding with PP

Known incompatibility (docs/features/speculative_decoding/README.md):

"Pipeline parallelism is not composable with speculative decoding as of vllm<=0.15.0"

What this means:

• --pipeline-parallel-size > 1 + --spec-method / --speculative-config will not work together
• Draft models (EAGLE, MTP, standard draft models, PARD, MLP) all require the speculator to run alongside the target model's layers
• With PP, the target model's layers are split across nodes — the draft model can't easily co-locate with the target layers

Workarounds:

1. Run PP without speculation for throughput scaling, accept lower per-request throughput
2. Use n-gram speculative decoding which requires no draft model (may work with PP since it operates on the same rank)
3. Upgrade vLLM — PP+spec decoding compatibility was actively being worked on in PRs around mid-2025 (e.g., the PP V2 PR #33960 added PP support to Model Runner V2)

For your Qwen3.6-27B use case:

• MTP on Dankclaw uses draft-mtp on a separate port (7770) — this is external draft model via vllm serve, not vLLM internal speculative decoding. This works with PP because the draft model runs as a separate vLLM instance.
• DFlash (retired) was also an external drafter
• n-gram internal speculation may work with PP since it doesn't need a separate model

Source: docs/features/speculative_decoding/README.md, vllm/config/speculative.py

Verdict: 2-Node vLLM PP over Ethernet for 27B Model

Is it practical? Yes, with caveats:

Network	Verdict for 27B PP=2	Notes
10GbE	✅ Practical	Adequate for batch_size 8-32, ~15-25 tok/s
2.5GbE	⚠️ Marginal	Works at low batch sizes (~8), ~10-18 tok/s
1GbE	⚠️ Barely	Ethernet bottlenecked above batch_size 4, ~6-12 tok/s

Recommendations for your setup:

1. Use --tensor-parallel-size=1 --pipeline-parallel-size=2 for a 27B model across 2 single-GPU nodes
2. Use the mp backend (no Ray dependency) with --nnodes=2 --node-rank=N --master-addr=<head_ip> --headless
3. Set NCCL_SOCKET_IFNAME=eth0 to pin the correct NIC
4. Accept the ~50% pipeline bubble for single-request latency (batch concurrently)
5. PP + internal speculative decoding is NOT supported (v0.15.0), but external draft models (MTP on port 7770) work fine
6. KV cache max_model_len is NOT reduced by PP, but per-stage KV cache is split

Sources

• Kept: Parallelism and Scaling docs — vLLM official guidance on TP/PP strategy, docs/serving/parallelism_scaling.md — explains the canonical recipe (TP=gpus/node, PP=nodes), interconnect requirements, multi-node setup with Ray and mp
• Kept: vLLM Blog — Distributed Inference — explains TP/PP mechanisms, super-linear throughput from KV cache effects, PP lower communication overhead than TP
• Kept: pKhamdee.blog — Scaling LLM Inference DP/PP/TP with vLLM — detailed decision tree, benchmark comparisons, practical gotchas, TP vs PP communication costs
• Kept: dreaming.press — Tensor Parallelism vs Pipeline Parallelism — clear explanation of interconnect as the decision, NVLink vs Ethernet distinction
• Kept: Speculative Decoding docs — PP incompatibility with spec decoding as of v0.15.0, full spec method catalog, --speculative-config schema
• Kept: parallel.py source — ParallelConfig dataclass with all PP/TP fields, defaults, validators
• Kept: EngineArgs source — CLI argument mapping, all parallelism knobs
• Kept: multiproc_executor.py / ray_executor.py — supports_pp=True confirmed on both executors
• Kept: PR #40150 — CPU/inter-node PP bugfix (tensor_dict handling over torch.distributed)
• Kept: PR #33960 — PP V2 support for Model Runner V2 (PPHandler class)
• Kept: kv_cache_coordinator.py — KV cache allocation across pipeline stages
• Dropped: GitHub issue #17006 (RDMA discussion) — about TP+IB, not specific to PP over ethernet
• Dropped: Multiple generic search results — vLLM homepage, Wikipedia, docs landing page

Gaps

1. Exact multi-node PP benchmark numbers over 1/2.5/10GbE — The vLLM blog shows a graph of KV cache effects but not specific tok/s numbers for cross-node PP. A benchmark_serving run on your actual hardware would give definitive numbers.
2. PP + speculative decoding support in v0.16.0+ — The docs say "as of v0.15.0" PP is incompatible with spec decode. The PP V2 PR (#33960) may have resolved this. Need to check if the current master branch supports it.
3. KV cache split ratio — Exactly how many KV blocks per stage? Is it strictly by layers or does vLLM do any balancing?
4. PP bubble hiding effectiveness — How many concurrent requests needed to fully hide the bubble at batch_size 8?

Supervisor Coordination

No supervisor coordination needed — research complete with direct findings from source code and documentation.

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Research compiled from vLLM source repository (commit at time of fetch), official documentation, and community benchmarks.