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AWS EFA Transport for Mooncake

This document describes how to build and use Mooncake with AWS Elastic Fabric Adapter (EFA) support using libfabric.

Prerequisites

1. AWS EFA Driver and libfabric

EFA driver and libfabric should be pre-installed on AWS instances with EFA support (e.g., p6-b300.48xlarge, p6-b200.48xlarge, p5en.48xlarge, p5e.48xlarge, p5.48xlarge).

Verify installation:

# Check EFA devices
fi_info -p efa

# Verify libfabric location
ls /opt/amazon/efa/lib/libfabric.so
ls /opt/amazon/efa/include/rdma/fabric.h

If not installed, follow AWS EFA documentation.

2. Build Dependencies

Clone the repository and install all dependencies:

git clone https://github.com/kvcache-ai/Mooncake.git
cd Mooncake
sudo ./dependencies.sh -y

This installs all system packages, git submodules (including pybind11 and yalantinglibs), and Go.

Note: The EFA driver and libfabric are not installed by dependencies.sh. They must be pre-installed on the instance (see section 1 above).

Building Mooncake with EFA Support

1. Build with EFA Enabled

GPU memory transfers (e.g., KV cache in vLLM):

mkdir build && cd build

cmake .. \
    -DUSE_EFA=ON \
    -DUSE_CUDA=ON \
    -DCMAKE_BUILD_TYPE=RelWithDebInfo

make -j$(nproc)

Note: -DUSE_CUDA=ON is required when transferring GPU memory (e.g., KV cache in vLLM). Without it, the TCP transport (used as fallback when mooncake_protocol is set to "tcp") cannot detect GPU memory and will fail with "Bad address" (EFAULT) errors.

CPU memory transfers only (no GPU dependency):

mkdir build && cd build

cmake .. \
    -DUSE_EFA=ON \
    -DUSE_CUDA=OFF \
    -DCMAKE_BUILD_TYPE=RelWithDebInfo

make -j$(nproc)

Note: With -DUSE_CUDA=OFF, the benchmark tool uses DRAM buffers allocated via numa_alloc_onnode. This is useful for measuring EFA transport throughput independently of GPU hardware.

2. Install Python Package

# Copy built modules to wheel directory
cp mooncake-integration/engine.cpython-*.so ../mooncake-wheel/mooncake/
cp mooncake-integration/store.cpython-*.so ../mooncake-wheel/mooncake/
cp mooncake-common/libasio.so ../mooncake-wheel/mooncake/

# Install with pip
pip install -e ../mooncake-wheel --no-build-isolation

Verification

Test EFA transport initialization:

from mooncake.engine import TransferEngine

te = TransferEngine()
result = te.initialize('127.0.0.1', 'P2PHANDSHAKE', 'efa', '')
print(f'Initialize result: {result}')  # Should be 0

# You should see logs like:
# EFA device (libfabric): rdmap79s0, domain: rdmap79s0-rdm, provider: efa

Unit Tests

Run the EFA transport unit tests (requires EFA hardware):

./build/mooncake-transfer-engine/tests/efa_transport_test

The test suite includes:

Test	Description
`InstallTransport`	Verify EFA transport installation
`LoopbackWrite`	Loopback write operation
`WriteAndRead`	Write then read with data integrity check
`MultiWrite`	Batch write (16 requests)
`StressMultipleBatches`	Stress test (20 batches x 8 requests)
`WarmupSegmentLoopback`	`warmupSegment()` handshake path + idempotent re-call
`WarmupSegmentNotFound`	`warmupSegment()` fails cleanly for an unknown segment
`RegisterMemoryBatch`	`registerLocalMemoryBatch` / `unregisterLocalMemoryBatch` round-trip
`LargeTransfer`	128 MB buffer, 64 x 1 MB slices — exercises WR / CQ pacing
`RepeatedOpenSegment`	`openSegment()` on the same peer repeatedly still transfers correctly

You can also run the EFA tests via CTest:

cd build && ctest --output-on-failure -R 'efa'

Note: ctest --output-on-failure without a filter runs every test in the build, including TCP / metadata / master-service suites that require an etcd server or a running mooncake_master. Those will fail or hang on a machine that is only provisioned for EFA testing — the failures are not EFA-specific. Use -R 'efa' to restrict the run to the EFA tests.

Performance Benchmark

Use transfer_engine_bench to measure EFA transport throughput between two nodes.

The following commands are the GPU-to-GPU configuration that produces the headline numbers in the Benchmark Results tables (≈ 350 GB/s write on a p5en.48xlarge pair, ≈ 302 GB/s on p6-b200.48xlarge). Two things matter the most:

--gpu_id=-1 on both sides — this fans buffers across every GPU, which in turn lets both NUMA nodes' NICs saturate. Pinning a single GPU (the default --gpu_id=0) halves throughput because half the NICs end up cross-NUMA.
--block_size=1048576 (1MB, not the 64 KB default) — each block becomes one fi_write / fi_read, so larger blocks amortize per-op overhead and are the main knob for hitting line rate.

1. Target Node (receiver)

./build/mooncake-transfer-engine/example/transfer_engine_bench \
    --mode=target \
    --protocol=efa \
    --metadata_server=P2PHANDSHAKE \
    --buffer_size=4294967296 \
    --gpu_id=-1

--buffer_size must be at least as large as the initiator's --buffer_size — the initiator writes into offsets [0, buffer_size) on the target, so keep these in sync.

2. Initiator Node (sender)

./build/mooncake-transfer-engine/example/transfer_engine_bench \
    --mode=initiator \
    --protocol=efa \
    --metadata_server=P2PHANDSHAKE \
    --segment_id=<target_hostname>:<target_port> \
    --operation=write \
    --duration=10 \
    --threads=16 \
    --block_size=1048576 \
    --batch_size=128 \
    --buffer_size=4294967296 \
    --gpu_id=-1 \
    --report_unit=GB

Replace <target_hostname>:<target_port> with the target node's address shown in the target's startup log (e.g., ip-172-31-29-226:12345).

CPU-to-CPU (no GPUs): build with -DUSE_CUDA=OFF, or pass --use_vram=false to a CUDA-enabled binary. Drop --gpu_id=-1 in that case — the bench will spread buffers across NUMA nodes instead.

Why threads=16 and not 32: the SRD shared endpoint caps outstanding WRs per NIC (default 256 — see MC_MAX_WR). With threads × batch ≤ NICs × max_wr the CQ never saturates; going higher triggers backoff and times out. 32 threads × 128 batch = 4096 slices chasing 16 × 256 = 4096 WRs has no headroom, so the steady-state config settles at 16 threads.

Key Parameters

Parameter	Default	Description
`--mode`	initiator	`initiator` (sender) or `target` (receiver)
`--protocol`	rdma	Transport protocol; use `efa` here
`--metadata_server`	`192.168.3.77:2379`	etcd address or `P2PHANDSHAKE` for standalone use
`--segment_id`	`192.168.3.76`	Initiator only: `<target_host>:<target_port>` from the target's startup log
`--operation`	read	`read` or `write`
`--block_size`	65536	Bytes per transfer request; 1 MB (1048576) is the main knob for EFA throughput
`--batch_size`	128	Requests per batch
`--threads`	12	Concurrent submission threads (initiator)
`--buffer_size`	1 GB	Buffer size (per GPU when `--gpu_id=-1`, otherwise total)
`--duration`	10	Test duration in seconds
`--report_unit`	GB	`GB\|GiB\|Gb\|MB\|MiB\|Mb\|KB\|KiB\|Kb`
`--use_vram`	true	Allocate from GPU VRAM (requires `-DUSE_CUDA=ON`); pass `--use_vram=false` for CPU-to-CPU on a CUDA build
`--gpu_id`	0	GPU device ID when `--use_vram=true`; `-1` fans buffers across every GPU and is what actually saturates all NICs in a GPU-to-GPU run
`--init_mem`	true	Zero-fill the allocated buffer; rarely needs to change
`--auto_discovery`	false	Auto-discover topology on init; off for reproducible runs

Note on EFA slicing: EFA transport does not split each transfer into fixed-size slices the way RDMA transport does — each transfer is sent as a single fi_write / fi_read whose size equals block_size, round-robin'd across NICs per request. block_size is the key tuning parameter for EFA throughput.

Note: buffer_size must be >= block_size * batch_size * threads. The benchmark auto-adjusts if too small.

(benchmark-results)=

Benchmark Results

1. p6-b300.48xlarge (B300, 16 EFA × 400 Gbps)

Tested on two p6-b300.48xlarge instances (Intel Xeon Platinum 8559C, 8× B300, 16 EFA devices) in the same AWS placement group. Numbers below are post-SRD-shared-endpoint (#1944) on a fresh main build with the DLAMI pytorch env (CUDA 13).

GPU-to-GPU (build with -DUSE_CUDA=ON, --gpu_id=-1 for all 8 GPUs, --buffer_size=2147483648):

Configuration	Write	Read
block=1MB, threads=16, batch=128	758.88 GB/s	720.35 GB/s
block=1MB, threads=32, batch=64	753.32 GB/s	755.78 GB/s
block=1MB, threads=32, batch=32	780.33 GB/s	-
block=1MB, threads=64, batch=32	780.23 GB/s	-

Peak: 780 GB/s write, reaching ~97.5% of the 800 GB/s theoretical line rate (16×400 Gbps). GPUDirect RDMA bypasses DRAM entirely (HBM3e → PCIe switch → NIC), so performance is not bottlenecked by CPU memory bandwidth.

CPU-to-CPU (build with -DUSE_CUDA=OFF, or --use_vram=false on a CUDA build, --buffer_size=4294967296):

Configuration	Write	Read
block=1MB, threads=32, batch=128	282.93 GB/s	270.47 GB/s
block=1MB, threads=16, batch=128	282.04 GB/s	249.67 GB/s
block=1MB, threads=32, batch=64	282.84 GB/s	256.26 GB/s

CPU-to-CPU is bounded by DRAM bandwidth on the Xeon 8559C — write throughput is essentially flat across thread/batch combinations (~282 GB/s), confirming DRAM controller saturation rather than a NIC or in-flight-WR limit.

2. p6-b200.48xlarge (B200, 8 EFA × 400 Gbps)

Tested on two p6-b200.48xlarge instances in the same AWS placement group.

Note: numbers below predate the SRD shared-endpoint refactor (#1944) and current EFA tuning work. They are a lower bound for the current code; we will re-sweep and update when a B200 pair is available again.

GPU-to-GPU (build with -DUSE_CUDA=ON, --gpu_id=-1 for all 8 GPUs):

Configuration	Write	Read
block=1MB, threads=32, batch=64, buf=2GB/GPU	285-296 GB/s	312 GB/s
block=1MB, threads=16, batch=128, buf=2GB/GPU	302 GB/s	313 GB/s

CPU-to-CPU (build with -DUSE_CUDA=OFF):

Configuration	Write	Read
block=1MB, threads=32, batch=128, buf=4GB	222 GB/s (stable over 6 runs)	226 GB/s

3. p5en.48xlarge (H200, 16 EFA × 200 Gbps)

Tested on two p5en.48xlarge instances (Intel Xeon 8488C, 8× H200 141GB, 16 EFA devices) in the same AWS placement group.

GPU-to-GPU (build with -DUSE_CUDA=ON, --gpu_id=-1 for all 8 GPUs, --buffer_size=4294967296):

Configuration	Write	Read
block=1MB, threads=8, batch=128	318.71 GB/s	277.06 GB/s
block=1MB, threads=16, batch=128	365.66 GB/s	284.22 GB/s
block=1MB, threads=16, batch=32	297.23 GB/s	303.78 GB/s
block=1MB, threads=32, batch=64	357.12 GB/s	279.61 GB/s
block=1MB, threads=32, batch=128	364.21 GB/s	250.90 GB/s
block=1MB, threads=48, batch=64	363.47 GB/s	268.43 GB/s

Peak write: 365 GB/s at threads=16, batch=128 — ~91% of the 400 GB/s theoretical line rate (16×200 Gbps). Write saturates on batch size, so batch=128 outperforms smaller batches as long as threads × batch ≤ 16 × 256 = 4096 (the shared-endpoint WR cap). Peak read: 304 GB/s at threads=16, batch=32 — reads tolerate smaller in-flight queues, and throughput drops as batch grows.

CPU-to-CPU (build with -DUSE_CUDA=OFF, or --use_vram=false on a CUDA build, --buffer_size=4294967296):

Configuration	Write	Read
block=1MB, threads=8, batch=128	210.76 GB/s	209.93 GB/s
block=1MB, threads=16, batch=128	212.71 GB/s	211.21 GB/s
block=1MB, threads=16, batch=32	211.67 GB/s	212.18 GB/s
block=1MB, threads=32, batch=128	212.99 GB/s	210.33 GB/s
block=1MB, threads=48, batch=32	213.57 GB/s	206.92 GB/s

CPU-to-CPU is DRAM-bound — throughput is essentially flat (~205–214 GB/s) across every thread / batch combination that doesn't hit the WR cap. Peak write 213.57 GB/s, peak read 212.18 GB/s.

block_size sweep (p5en GPU-to-GPU, threads=16 batch=128 buf=4GB --gpu_id=-1):

block	Write	Read
64 KB (default)	97.11 GB/s	96.87 GB/s
128 KB	190.82 GB/s	203.88 GB/s
256 KB	324.90 GB/s	296.19 GB/s
512 KB	357.62 GB/s	289.47 GB/s
1 MB (recommended)	352.59 GB/s	301.14 GB/s
2 MB	366.87 GB/s	302.25 GB/s

The 64 KB default only reaches ~26% of peak. Write throughput climbs steeply up to ~512 KB and plateaus between 1 MB and 2 MB; read saturates at ~256 KB. 1 MB is the recommended value — within a few percent of the 2 MB peak with more headroom for batch_size under the shared-endpoint WR cap.

buffer_size sweep (p5en GPU-to-GPU, threads=16 batch=128 block=1MB --gpu_id=-1):

buffer_size	Write	Read
2 GB (min: `block × batch × threads`)	353.51 GB/s	297.03 GB/s
4 GB	364.86 GB/s	293.79 GB/s

buffer_size only needs to satisfy buffer_size ≥ block_size × batch_size × threads (the bench auto-adjusts if smaller, but silently). Anything larger than that minimum does not change throughput — 2 GB vs 4 GB differs by ~3% on write, read is flat within noise. The example commands use 4 GB because it is safe for any reasonable threads/batch combination without having to recompute the minimum.

4. p5.48xlarge (H100, 32 EFA × 100 Gbps)

Tested on two p5.48xlarge instances (AMD EPYC 7R13, 8× H100 80GB, 32 EFA devices) in the same AWS placement group. Per-NIC line rate is half of p5en's, but with twice the NIC count the aggregate ceiling is the same 400 GB/s. The shared-endpoint WR cap scales with NIC count: 32 NICs × 256 = 8192 in-flight slots, so threads × batch_size ≤ 8192 (vs 4096 on p5en).

GPU-to-GPU (build with -DUSE_CUDA=ON, --gpu_id=-1 for all 8 GPUs, --buffer_size=4294967296):

Configuration	Write	Read
block=1MB, threads=8, batch=128	335.11 GB/s	-
block=1MB, threads=16, batch=128	388.52 GB/s	379.10 GB/s
block=1MB, threads=32, batch=64	388.83 GB/s	379.78 GB/s
block=1MB, threads=32, batch=128	388.90 GB/s	381.64 GB/s
block=1MB, threads=16, batch=32	-	356.94 GB/s
block=1MB, threads=32, batch=32	-	380.66 GB/s

Peak write: 389 GB/s at threads=32, batch=128 — ~97% of the 400 GB/s theoretical line rate (32×100 Gbps). The plateau is wide: any (threads, batch) between (16, 128) and (32, 128) lands within 0.1% of peak. Peak read: 382 GB/s at threads=32, batch=128 — unlike p5en, reads on this host scale with batch size up to 128 because the wider 32-NIC fabric absorbs larger in-flight queues without backoff. (32, 256) and (64, 128) (both at the 8192 WR cap) fail with no headroom for retries.

CPU-to-CPU (build with -DUSE_CUDA=OFF, or --use_vram=false on a CUDA build, --buffer_size=4294967296):

Configuration	Write	Read
block=1MB, threads=16, batch=128	39.83 GB/s	40.43 GB/s
block=1MB, threads=32, batch=64	47.31 GB/s	48.06 GB/s
block=1MB, threads=32, batch=128	47.75 GB/s	48.62 GB/s
block=1MB, threads=32, batch=32	55.66 GB/s	56.68 GB/s
block=1MB, threads=48, batch=16	63.60 GB/s	63.00 GB/s
block=1MB, threads=64, batch=16	63.39 GB/s	63.05 GB/s
block=1MB, threads=32, batch=16	63.21 GB/s	60.82 GB/s
block=1MB, threads=96, batch=32	57.61 GB/s	59.63 GB/s

Peak: ~64 GB/s on both write and read — far below the GPU-to-GPU number despite identical NIC count. The bottleneck is DDR4-3200 DRAM bandwidth on the EPYC 7R13 (Milan): batch=16 consistently wins because larger in-flight queues only deepen DRAM contention without unlocking new NIC capacity. p5.48xlarge CPU-to-CPU runs around 3× slower than p5en (DDR5 Xeon 8488C, ~213 GB/s) at the same NIC aggregate. For PD KV transfer, the GPU-to-GPU path is the relevant one.

Tuning Tips

Use --block_size=1048576 (1MB) — the single most important knob. The 64 KB default reaches only ~26% of peak. 1 MB is within a few percent of the 2 MB plateau while leaving headroom for batch_size under the shared-endpoint WR cap.
Keep threads × batch_size ≤ num_nics × max_wr — under the SRD shared endpoint each NIC carries one fid_ep with a 256 WR cap (MC_MAX_WR), giving 16 NICs × 256 = 4096 in-flight slots on a 16-NIC host (b300, b200, p5en) and 32 NICs × 256 = 8192 on p5. Exceeding this trips "timed out waiting for CQ drain". threads=16, batch=128 is a solid baseline on 16-NIC hosts; on 32-NIC p5, threads=32, batch=128 works the same way.
Write vs read: write benefits from larger batches (peak at batch=128); on 16-NIC p5en read prefers smaller queues (peak at batch=32), but on 32-NIC p5 reads scale up to batch=128 because the wider fabric absorbs larger in-flight queues.
For GPU-to-GPU: pass --gpu_id=-1 on both sides so buffers fan out across every GPU. Pinning a single GPU halves throughput because half the NICs end up cross-NUMA.
For CPU-to-CPU: DRAM bandwidth is the ceiling. NUMA-split (separate initiator/target instances per NUMA node) can help reduce contention when one instance can't saturate both nodes.
--buffer_size only needs ≥ block × batch × threads; larger values do not improve throughput. The example commands use 4 GB because that is safe for any reasonable config.

First-request latency

Peer addressing resolves lazily: fi_av_insert() and the metadata handshake fire on the first send to each (local_NIC, peer_NIC) pair. On 16-NIC hosts, the first few submitTransfer calls carry this cost before steady state.

Measured on p5en (16 × 16 NICs, cross-node, 1 MB write, 3 reps, median):

	cold submit #0 (no warmup)	`warmupSegment()` (all 256 pairs)
SRD shared endpoint (#1944)	26 ms	1.1 s
Per-peer `fid_ep` (upstream main)	99 ms	17 s
Speedup	~4×	~15×

The SRD shared-endpoint refactor (#1944) speeds up first-request latency two different ways:

Without any code change from callers — the cold submitTransfer is ~4× faster (26 ms vs 99 ms), because the shared endpoint removes the per-peer fi_endpoint / fi_enable that used to dominate. This is what existing Mooncake callers (vLLM, SGLang, etc.) will see.
For callers that want sub-10 ms first-request latency, an explicit eager-warmup API lets you pay the handshake cost up front, outside the critical path:
- C++: EfaTransport::warmupSegment(const std::string& segment_name)
- C: int warmupEfaSegment(transfer_engine_t engine, const char *segment_name)
- Rust: TransferEngine::warmup_efa_segment(name: &str)
- Python: engine.warmup_efa_segment(segment_name)
Call once per peer right after openSegment. The call is idempotent. Under this refactor warmupSegment itself is ~15× faster than the pre-#1944 code (1.1 s vs 17 s), bounded by the peer's single-threaded handshake RPC daemon (accept + JSON parse serialized on one thread), so it scales linearly with the number of fresh NIC pairs.

vLLM and SGLang do not currently call warmupSegment — they go through the generic TransferEngine interface and pick up the 4× cold-submit speedup automatically. The API is there for direct Mooncake callers that want the larger win.

Reproducing the numbers — `efa_first_submit_probe`

mooncake-transfer-engine/example/efa_first_submit_probe.cpp is a two-host probe that isolates the two latency costs transfer_engine_bench hides inside its 10-second throughput average:

Cold first-submit — the handshake + fi_av_insert() that fires on the first send to each (local_NIC, peer_NIC) pair.
Eager warmup — how much of that is paid up front by an explicit warmupSegment() call.

It is EFA-specific (there is no warmupSegment on the RDMA / TCP transports — they establish connections at connect time, so "pre-warming" has no meaning there) and intentionally not wired into ctest: it needs two hosts.

Run:

# On the target host:
./build/mooncake-transfer-engine/example/efa_first_submit_probe \
    --mode=target \
    --metadata_server=P2PHANDSHAKE \
    --local_server_name=$(hostname):12345

# Note the "[target] ready, addr=<ip>:<port>" line, then on the initiator:
./build/mooncake-transfer-engine/example/efa_first_submit_probe \
    --mode=initiator \
    --metadata_server=P2PHANDSHAKE \
    --local_server_name=$(hostname):12346 \
    --segment_id=<target_ip>:<target_port> \
    --warmup=1 \
    --iters=5

What it prints:

warmup:   1094.74 ms (rc=0)           # present only with --warmup=1
submit #0:   6.95 ms                   # first user-visible submit
submit #1:   5.45 ms                   # steady state
submit #2:   5.92 ms
submit #3:   5.67 ms
submit #4:   0.09 ms

warmup: is the wall time of EfaTransport::warmupSegment() — it opens every (local_NIC, peer_NIC) pair (16 × 16 = 256 on p5en) up front.
submit #0 through #N-1 are the timings of single 1 MB submitTransfer + poll. With --warmup=1 they are all in the steady-state regime. With --warmup=0, #0 pays the handshake cost, #1+ are steady state.

Flags:

Flag	Default	What it controls
`--warmup`	`true`	Call `warmupSegment()` before the first submit
`--iters`	`5`	How many timed submits after warmup
`--xfer_size`	`1<<20` (1 MB)	Bytes per submit
`--buffer_size`	`1<<30` (1 GB)	Registered buffer size
`--local_server_name`	`hostname:12345`	Local metadata advertise name

Use cases:

Deciding whether your application needs warmupSegment(): if submit #0 in the --warmup=0 run is acceptable for your use case, you don't need to call warmupSegment at all.
Comparing PR branches: run the probe on the same hardware on this PR's branch vs main (or whatever upstream you're benchmarking against) to see per-NIC-pair handshake cost directly, without having the number drowned in a 10-second throughput average.

Usage with vLLM

1. Prefill Instance

VLLM_MOONCAKE_BOOTSTRAP_PORT=8998 \
vllm serve <model_path> -tp 8 \
   --port 8010 \
   --trust-remote-code \
   --kv-transfer-config '{"kv_connector":"MooncakeConnector","kv_role":"kv_producer","kv_connector_extra_config":{"mooncake_protocol":"efa"}}'

2. Decode Instance

vllm serve <model_path> -tp 8 \
   --port 8020 \
   --trust-remote-code \
   --kv-transfer-config '{"kv_connector":"MooncakeConnector","kv_role":"kv_consumer","kv_connector_extra_config":{"mooncake_protocol":"efa"}}'

3. Router

Front the prefill / decode pair with vllm-router. PREFILL_HOST / DECODE_HOST must be each node's reachable private IP, not 127.0.0.1 — the router forwards these addresses to the peer for the KV handshake; localhost will fail with Connection refused and stall traffic.

vllm-router --policy round_robin \
  --vllm-pd-disaggregation \
  --prefill http://<prefill_ip>:8010 \
  --decode  http://<decode_ip>:8020 \
  --kv-connector mooncake \
  --host 0.0.0.0 --port 30000 \
  --intra-node-data-parallel-size 8

--intra-node-data-parallel-size should match the per-node DP size of your prefill / decode instances (8 in this example, matching -tp 8).

Usage with SGLang

SGLang's PD-disaggregation Mooncake integration reads the transport from MOONCAKE_PROTOCOL. Set it to efa and select the Mooncake backend with --disaggregation-transfer-backend mooncake.

1. Apply EFA Patch (only if SGLang version predates PR #25083)

Older SGLang releases hardcode "rdma" in the transfer engine init. Since SGLang PR #25083 the protocol is read from MOONCAKE_PROTOCOL, so once that PR is in your build (or upstream main) this step is unnecessary — skip to step 2.

If you are pinned to an older release, apply the patch script:

bash patch_sglang_efa.sh

The script is idempotent and safe to rerun.

2. Environment Variables

Only one Mooncake-specific env is required:

export MOONCAKE_PROTOCOL=efa

If your container does not already export libfabric/EFA paths in its Dockerfile, also set:

export FI_PROVIDER=efa
export FI_EFA_USE_DEVICE_RDMA=1
export LD_LIBRARY_PATH=/opt/amazon/efa/lib:$LD_LIBRARY_PATH

Note on additional MC_* knobs: MC_NUM_CQ_PER_CTX, MC_MAX_WR, MC_MAX_CQE_PER_CTX, MC_SLICE_SIZE, and MC_EFA_STRIPING_THRESHOLD are not required at typical PD-disagg loads — the SRD shared-endpoint refactor (#1944) makes them redundant up to high concurrency on 1k/1k traffic. Treat them as emergency switches for CQ-overflow or long-running drift symptoms.

3. Prefill Instance

MOONCAKE_PROTOCOL=efa \
sglang serve <model_path> \
  --trust-remote-code \
  --tp 8 --dp 2 --enable-dp-attention --enable-dp-lm-head \
  --host 0.0.0.0 --port 8010 \
  --disaggregation-mode prefill \
  --disaggregation-transfer-backend mooncake \
  --disaggregation-bootstrap-port 8998

4. Decode Instance

MOONCAKE_PROTOCOL=efa \
sglang serve <model_path> \
  --trust-remote-code \
  --tp 8 --dp 2 --enable-dp-attention --enable-dp-lm-head \
  --host 0.0.0.0 --port 8020 \
  --disaggregation-mode decode \
  --disaggregation-transfer-backend mooncake \
  --disaggregation-bootstrap-port 8998

5. Router

Front the pair with sglang_router from the prefill host. PREFILL_HOST must be the prefill node's reachable IP, not 127.0.0.1 — the router forwards this address to the decode node for the bootstrap_room handshake; localhost will fail with Connection refused and stall traffic at 0/N.

python3 -m sglang_router.launch_router \
  --pd-disaggregation \
  --prefill "http://<prefill_ip>:8010" 8998 \
  --decode  "http://<decode_ip>:8020" \
  --policy round_robin \
  --host 0.0.0.0 --port 8000

The trailing 8998 after --prefill must match the prefill's --disaggregation-bootstrap-port.

Technical Details

Why libfabric instead of ibverbs?

AWS EFA exposes RDMA-like devices through the ibverbs interface, but does not support the full ibverbs API. Specifically:

Queue Pair (QP) creation fails with "Operation not supported" (error 95)
EFA requires using libfabric's FI_EP_RDM (Reliable Datagram Message) endpoint type

EFA Transport Architecture

Under the SRD shared-endpoint model every peer is addressed through one fid_ep per local NIC — peers are AV-slot entries, not separate endpoints.

┌───────────────────────────────────────────────────────────┐
│                   EfaTransport                            │
├───────────────────────────────────────────────────────────┤
│  EfaContext (per local NIC)                               │
│  ├── fid_fabric     (fabric handle)                       │
│  ├── fid_domain     (protection domain)                   │
│  ├── fid_av         (address vector — one slot per peer)  │
│  ├── fid_cq         (completion queues)                   │
│  ├── fid_mr         (memory regions)                      │
│  ├── shared_ep_     (the single fid_ep that serves every  │
│  │                   peer via fi_addr_t lookup in the AV) │
│  └── peer_map_      (normalized nic_path -> EfaEndPoint)  │
├───────────────────────────────────────────────────────────┤
│  EfaEndPoint (per peer)                                   │
│  └── peer_fi_addr_  (AV slot index for this peer; sends   │
│                      route through the owning context's    │
│                      shared_ep_ with this fi_addr_t)      │
└───────────────────────────────────────────────────────────┘

Thread Safety

The EFA transport requests FI_THREAD_SAFE at the domain level and guards the shared endpoint with a single post_lock_ spinlock (one per EfaContext, i.e. one per local NIC) to serialize fi_write/fi_read calls. This is necessary because:

Multiple submission threads may route slices through the same shared endpoint concurrently.
libfabric's EFA RDM endpoints are not thread-safe for concurrent fi_write/fi_read even under FI_THREAD_SAFE at the domain level — concurrent posts corrupt provider internals and completions silently vanish.

CQ completion queues are polled by dedicated worker threads (one per EFA device) that run independently of submission threads.

EFA vs RoCE RDMA

Feature	EFA (libfabric SRD)	RoCE (ibverbs)
Protocol	Scalable Reliable Datagram	RDMA over Converged Ethernet
Endpoint type	`FI_EP_RDM` (message-based)	Queue Pairs (true RDMA)
Write operation	Software-emulated via messages + ACKs	Hardware-offloaded one-sided RDMA
CPU overhead	Moderate (provider processes ACKs)	Minimal (NIC handles everything)
Throughput GPU-to-GPU (16×200G, p5en)	365 GB/s (tuned)	N/A
Throughput CPU-to-CPU (16×200G, p5en)	213 GB/s (tuned)	—
AWS availability	All EFA-enabled instances	Not available on AWS

Supported AWS Instance Types

p6-b300.48xlarge (16 EFA devices × 400 Gbps = 6,400 Gbps, rdmap* naming)
p6-b200.48xlarge (8 EFA devices × 400 Gbps = 3,200 Gbps, rdmap* naming)
p5en.48xlarge (16 EFA devices × 200 Gbps = 3,200 Gbps, rdmap* naming)
p5e.48xlarge (32 EFA devices × 100 Gbps = 3,200 Gbps, rdmap* naming)
p5.48xlarge (32 EFA devices × 100 Gbps = 3,200 Gbps, rdmap* naming)
Other EFA-enabled instances

Use fi_info -p efa to list available EFA devices on your instance.

Troubleshooting

No EFA devices found

EfaTransport: No EFA devices found

Solution: Verify EFA is available with fi_info -p efa

Permission denied

fi_fabric failed: Permission denied

Solution: Ensure proper permissions or run with sudo for testing

libfabric not found

cannot find -lfabric

Solution: Verify /opt/amazon/efa/lib is in the library path:

export LD_LIBRARY_PATH=/opt/amazon/efa/lib:$LD_LIBRARY_PATH

Workers hang under high concurrency

If transfer_engine_bench hangs with some workers never completing:

Ensure both nodes are running the same build — the CQ backpressure and thread-safety fixes must be present on both sides
Reduce concurrency to verify basic connectivity: --threads=1 --batch_size=16
Check CQ poller threads: logs should show "Started N CQ polling worker threads" where N matches the number of EFA devices

Building on AWS Deep Learning AMI

On AWS Deep Learning AMI (e.g., Ubuntu 24.04), the system Python and CUDA toolkit are bundled inside the /opt/pytorch virtual environment. You must activate it and set CUDA paths before building:

# Activate the PyTorch environment (provides Python 3.13 + CUDA toolkit)
source /opt/pytorch/bin/activate

# Set CUDA paths (nvcc, headers and libs are inside the pip-installed nvidia packages)
export CUDA_HOME=/opt/pytorch/lib/python3.13/site-packages/nvidia/cu13
export PATH=$CUDA_HOME/bin:$PATH
export CPLUS_INCLUDE_PATH=$CUDA_HOME/include:$CPLUS_INCLUDE_PATH
export LD_LIBRARY_PATH=$CUDA_HOME/lib:$LD_LIBRARY_PATH
export LIBRARY_PATH=$CUDA_HOME/lib:$LIBRARY_PATH

# Build with CUDA support
cd ~/Mooncake
mkdir -p build && cd build
cmake .. -DUSE_EFA=ON -DUSE_CUDA=ON -DCMAKE_BUILD_TYPE=RelWithDebInfo
make -j$(nproc)

Without activating the environment, you may encounter:

Could not find nvcc, please set CUDAToolkit_ROOT — nvcc is not in PATH
fatal error: cuda.h: No such file or directory — CUDA headers not in include path, set CPLUS_INCLUDE_PATH
cannot find -lcudart: No such file or directory — CUDA libs not in library path, set LIBRARY_PATH and LD_LIBRARY_PATH
ModuleNotFoundError: No module named 'mooncake.engine' — .so built against wrong Python version (e.g., 3.12 vs 3.13)

libfabric version mismatch in Docker

fi_ep_bind (av) failed: Function not implemented

or:

undefined reference to `efadv_query_qp_wqs@EFA_1.4'

This happens when the Docker container's libfabric version is older than the host's EFA driver. Check with fi_info --version on both host and container.

Solution: Mount the host's EFA libraries into the container:

docker run --gpus all --device=/dev/infiniband --net=host --privileged \
  -v /opt/amazon/efa:/opt/amazon/efa \
  -v /lib/x86_64-linux-gnu/libefa.so.1:/lib/x86_64-linux-gnu/libefa.so.1 \
  -v /lib/x86_64-linux-gnu/libefa.so:/lib/x86_64-linux-gnu/libefa.so \
  -v /lib/x86_64-linux-gnu/libibverbs.so.1:/lib/x86_64-linux-gnu/libibverbs.so.1 \
  -e LD_LIBRARY_PATH=/opt/amazon/efa/lib:$LD_LIBRARY_PATH \
  -it <image>

Then rebuild Mooncake inside the container to link against the host's libfabric.

33 KiB Raw Blame History Unescape Escape

AWS EFA Transport for Mooncake

Prerequisites

1. AWS EFA Driver and libfabric

2. Build Dependencies

Building Mooncake with EFA Support

1. Build with EFA Enabled

2. Install Python Package

Verification

Unit Tests

Performance Benchmark

1. Target Node (receiver)

2. Initiator Node (sender)

Key Parameters

Benchmark Results

1. p6-b300.48xlarge (B300, 16 EFA × 400 Gbps)

2. p6-b200.48xlarge (B200, 8 EFA × 400 Gbps)

3. p5en.48xlarge (H200, 16 EFA × 200 Gbps)

4. p5.48xlarge (H100, 32 EFA × 100 Gbps)

Tuning Tips

First-request latency

Reproducing the numbers — efa_first_submit_probe

Usage with vLLM

1. Prefill Instance

2. Decode Instance

3. Router

Usage with SGLang

1. Apply EFA Patch (only if SGLang version predates PR #25083)

2. Environment Variables

3. Prefill Instance

4. Decode Instance

5. Router

Technical Details

Why libfabric instead of ibverbs?

EFA Transport Architecture

Thread Safety

EFA vs RoCE RDMA

Supported AWS Instance Types

Troubleshooting

No EFA devices found

Permission denied

libfabric not found

Workers hang under high concurrency

Building on AWS Deep Learning AMI

libfabric version mismatch in Docker

33 KiB

Raw Blame History

Reproducing the numbers — `efa_first_submit_probe`