ShardFlow: 28 TPS on Qwen2.5-7B over WAN via speculative decoding
katua_bkl · reddit · 2026-08-23
The author presented ShardFlow, a distributed LLM inference framework achieving high performance over WAN.
- Architecture: Splits HF models across N GPU nodes and uses neural speculative decoding to offset WAN latency.
- Performance: Achieved 28.1 TPS peak (20.31 avg) on Qwen2.5-7B across two GCP regions (86ms RTT), 5x improvement over baseline.
- Key Optimization: Using CUDA Graphs to reduce 1500 kernel launches per round to a single driver call, cutting draft latency from 112ms to 25ms.
- Tech Stack: Zero-copy Rust TCP relay, StaticCache, in-place KV rewind, and meta-device model slicing.
Open source repository available.
More from Infra
- Fluidstack hires Electrical Engineer for Modular R&D to build frontier AI infrastructure — MxMnr · 2026-10-16
- Jensen Huang joins BG2 podcast: does AI revenue justify capex, '27 bottlenecks — elonmusk · 2026-10-16
- Mistral's on-device infra rack revealed: DGX Spark, Mac Studio and more — gregmushen · 2026-10-16
- NVIDIA and Sweden's Wallenberg launch Sferical One, a shared AI supercomputer — nvidia · 2026-10-16
- Next infra frontier: self-evolving systems that adapt and self-verify — _shreya_s · 2026-10-16
- Xiaomi's MiMo RL dashboard reveals $3.47M spent on just 30 training steps — bycloud · 2026-10-16