Perplexity AI Releases TransferEngine and pplx garden to Run Trillion Parameter LLMs on Existing GPU Clusters
How can groups run trillion parameter language fashions on present combined GPU clusters with out expensive new {hardware} or deep vendor lock in?Perplexity’s analysis workforce has launched TransferEngine and the encompassing pplx garden toolkit as open supply infrastructure for big language mannequin methods. This offers a manner to run fashions with up to 1 trillion parameters throughout combined GPU clusters, with out locking right into a single cloud supplier or shopping for new GB200 class {hardware}.
The actual bottleneck, community materials not FLOPs
Modern deployments of Mixture of Experts fashions akin to DeepSeek V3 with 671 billion parameters and Kimi K2 with 1 trillion parameters now not match on a single 8 GPU server. They should span a number of nodes, so the principle constraint turns into the community cloth between GPUs.
Here the {hardware} panorama is fragmented. NVIDIA ConnectX 7 sometimes makes use of Reliable Connection transport with so as supply. AWS Elastic Fabric Adapter makes use of Scalable Reliable Datagram transport that’s dependable however out of order, and a single GPU may have 4 community adapters at 100 Gbps, or 2 at 200 Gbps, to attain 400 Gbps.
Existing libraries akin to DeepEP, NVSHMEM, MoonCake and NIXL have a tendency to optimize for one vendor and degrade or lack assist on the opposite aspect. Perplexity’s analysis workforce instantly states within the research paper that there was no viable cross supplier resolution for LLM inference earlier than this work.
TransferEngine, a conveyable RDMA layer for LLM methods
TransferEngine addresses this by focusing on solely the intersection of ensures throughout Network Interface Controllers. It assumes that the underlying RDMA transport is dependable, however doesn’t assume any ordering of messages. On high of this, it exposes one sided WriteImm operations and an ImmCounter primitive for completion notification.
The library offers a minimal API in Rust. It gives two sided Send and Recv for management messages, and three fundamental one sided operations, submit_single_write, submit_paged_writes, and submit_scatter, plus a submit_barrier primitive for synchronization throughout a bunch of friends. A NetAddr construction identifies friends and an MrDesc construction describes registered reminiscence areas. An alloc_uvm_watcher name creates a tool aspect watcher for CPU GPU synchronization in superior pipelines.
Internally, TransferEngine spawns one employee thread per GPU and builds a DomainGroup per GPU that coordinates between 1 and 4 RDMA Network Interface Controllers. A single ConnectX 7 offers 400 Gbps. On EFA, the DomainGroup aggregates 4 community adapters at 100 Gbps, or 2 at 200 Gbps, to attain the identical bandwidth. The sharding logic is aware of about all Network Interface Controllers and can cut up a switch throughout them.
Across {hardware}, the analysis workforce reviews peak throughput of 400 Gbps on each NVIDIA ConnectX 7 and AWS EFA. This matches single platform options and confirms that the abstraction layer doesn’t depart massive efficiency on the desk.
pplx garden, the open supply package deal
TransferEngine ships as a part of the pplx garden repository on GitHub beneath an MIT license. The listing construction is simple. fabric-lib comprises the RDMA TransferEngine library, p2p-all-to-all implements a Mixture of Experts all to all kernel, python-ext offers the Python extension module from the Rust core, and python/pplx_garden comprises the Python package deal code.
The system necessities replicate a contemporary GPU cluster. Perplexity analysis workforce recommends Linux kernel 5.12 or newer for DMA BUF assist, CUDA 12.8 or newer, libfabric, libibverbs, GDRCopy, and an RDMA cloth with GPUDirect RDMA enabled. Each GPU ought to have no less than one devoted RDMA Network Interface Controller.
Disaggregated prefill and decode
The first manufacturing use case is disaggregated inference. Prefill and decode run on separate clusters, so the system should stream KvCache from prefill GPUs to decode GPUs at excessive pace.
TransferEngine makes use of alloc_uvm_watcher to observe progress within the mannequin. During prefill, the mannequin increments a watcher worth after every layer’s consideration output projection. When the employee observes a change, it points paged writes for the KvCache pages of that layer, adopted by a single write for the remaining context. This strategy permits layer by layer streaming of cache pages with out fastened world membership, and it avoids the strict ordering constraints of collectives.
Fast weight switch for reinforcement studying
The second system is asynchronous reinforcement studying high-quality tuning, the place coaching and inference run on separate GPU swimming pools. Traditional designs collect up to date parameters to a single rank then broadcast them, which limits throughput to one Network Interface Controller.
Perplexity analysis workforce as a substitute makes use of TransferEngine to carry out level to level weight switch. Each coaching GPU writes its parameter shard instantly into the corresponding inference GPUs utilizing one sided writes. A pipelined execution splits every tensor into levels, host to machine copy when Fully Sharded Data Parallel offloads weights, reconstruction and optionally available quantization, RDMA switch, and a barrier applied by way of scatter and ImmCounter.
In manufacturing, this setup delivers weight updates for fashions akin to Kimi K2 at 1 trillion parameters and DeepSeek V3 at 671 billion parameters in about 1.3 seconds from 256 coaching GPUs to 128 inference GPUs.
Mixture of Experts routing throughout ConnectX and EFA
The third piece in pplx garden is some extent to level Mixture of Experts dispatch and mix kernel. It makes use of NVLink for intra node site visitors and RDMA for inter node site visitors. Dispatch and mix are cut up into separate ship and obtain phases in order that the decoder can micro batch and overlap communication with grouped normal matrix multiply.
A bunch proxy thread polls GPU state and calls TransferEngine when ship buffers are prepared. Routes are exchanged first, then every rank computes contiguous obtain offsets for every skilled and writes tokens into personal buffers that may be reused between dispatch and mix. This reduces reminiscence footprint and retains writes massive sufficient to use the complete hyperlink bandwidth.
On ConnectX 7, Perplexity analysis workforce reviews state-of-the-art decode latency that’s aggressive with DeepEP throughout skilled counts. On AWS EFA, the identical kernel delivers the primary viable MoE decode latencies with greater however nonetheless sensible values.
In multi node exams with DeepSeek V3 and Kimi K2 on AWS H200 situations, distributing the mannequin throughout nodes reduces latency at medium batch sizes, which is the frequent regime for manufacturing serving.
Comparison Table
| Key level | TransferEngine (pplx garden) | DeepEP | NVSHMEM (generic MoE use) | Mooncake |
|---|---|---|---|---|
| Primary function | Portable RDMA level to level for LLM methods | MoE all to all dispatch and mix | General GPU shared reminiscence and collectives | Distributed KV cache for LLM inference |
| Hardware focus | NVIDIA ConnectX 7 and AWS EFA, multi NIC per GPU | NVIDIA ConnectX with GPU initiated RDMA IBGDA | NVIDIA GPUs on RDMA materials together with EFA | RDMA NICs in KV centric serving stacks |
| EFA standing | Full assist, peak 400 Gbps reported | No assist, requires IBGDA on ConnectX | API works however MoE use exhibits extreme degradation on EFA | Paper reviews no EFA assist in its RDMA engine |
| Portability for LLM methods | Cross vendor, single API throughout ConnectX 7 and EFA | Vendor particular and ConnectX centered | NVIDIA centric, not viable for EFA MoE routing | Focused on KV sharing, no cross supplier assist |
Key Takeaways
- TransferEngine offers a single RDMA level to level abstraction that works on each NVIDIA ConnectX 7 and AWS EFA, and manages a number of Network Interface Controllers per GPU transparently.
- The library exposes one sided WriteImm with ImmCounter, and achieves peak 400 Gbps throughput on each NIC households, which lets it match single vendor stacks whereas remaining transportable.
- Perplexity workforce makes use of TransferEngine in three manufacturing methods, disaggregated prefill decode with KvCache streaming, reinforcement studying weight switch that updates trillion parameter fashions in about 1.3 seconds, and Mixture of Experts dispatch mix for big fashions like Kimi K2.
- On ConnectX 7, pplx garden’s MoE kernels present state-of-the-art decode latency and exceed DeepEP on the identical {hardware}, whereas on EFA they ship the primary sensible MoE latencies for trillion parameter workloads.
- Because TransferEngine is open supply in pplx garden beneath an MIT license, groups can run very massive Mixture of Experts and dense fashions on heterogeneous H100 or H200 clusters throughout cloud suppliers, with out rewriting for every vendor particular networking stack.
Editorial Comments
Perplexity’s launch of TransferEngine and pplx garden is a sensible contribution for LLM infra groups who’re blocked by vendor particular networking stacks and costly cloth upgrades. A conveyable RDMA abstraction that reaches peak 400 Gbps on each NVIDIA ConnectX 7 and AWS EFA, helps KvCache streaming, quick reinforcement studying weight switch, and Mixture of Experts routing, instantly addresses trillion parameter serving constraints for actual methods.
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