A developer's framework ShardFlow hits 28 TPS on Qwen2.5-7B.
It runs across two cloud regions with speculative decoding.
This makes WAN distributed inference faster.
What happened
The developer behind ShardFlow, a distributed LLM inference framework that splits HuggingFace transformers across N GPU machines, benchmarked Qwen2.5-7B on two T4 nodes in separate GCP regions (Iowa + Oregon) connected via an AWS EC2 TCP relay in Ohio (~86ms RTT). With neural speculative decoding and CUDA Graphs, peak throughput hit 28.10 TPS, average 20.31 TPS, versus 4.92 TPS non-speculative.
Why it matters
This shows that WAN latency can be turned from a per-token cost into a per-round cost—with K=8 drafting, committing 4.07 tokens per round trip instead of 1—so distributed inference across public internet becomes practical, potentially reducing reliance on single-region or on-prem clusters.
What to watch
The same setup ran Qwen2.5-14B with NF4 4-bit quantization at 14.43 TPS average, and the dev highlighted a v2.1 fix: draft generation was launching ~1500 CUDA kernels per round from a Python loop; CUDA Graphs helped reduce CPU launch overhead, suggesting further optimization potential.
Ask the AI about this article →
ShardFlow's results suggest a viable path for running large language models across geographically dispersed GPUs, turning what is typically a latency bottleneck into a manageable per-round cost. The jump from 4.92 TPS non-speculative to 28.10 TPS peak with speculative decoding and CUDA Graphs highlights the importance of both algorithmic and systems-level optimizations. The fact that the benchmark used modest T4 GPUs and public internet (not dedicated interconnects) underscores the approach's accessibility. The 14.43 TPS average on the larger Qwen2.5-14B with 4-bit quantization indicates that the technique scales to larger models, though with expected trade-offs. The developer's focus on reducing CUDA kernel launch overhead suggests that software efficiency is as critical as model size. This might encourage more experimentation with distributed inference setups, possibly enabling lower-cost deployments across existing cloud instances.
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