How Apache DataFusion Runs SQL In-Process
Apache DataFusion is an embeddable query engine: SQL goes in, runs as columnar Arrow batches inside your own process, and results come out without a separate service.
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How BioNeMo Turns a Target List Into Folded Structures
BioNeMo Inference Runtime runs one pipeline per target: parse, tokenize, generate features, infer on a GPU replica, and write a structure. This diagram traces those stages and the throughput trade-off.
Explanation
BioNeMo Inference Runtime wraps supported structure-prediction models in a single end-to-end processor: each InputRequest moves through parsing, tokenization, feature generation, GPU inference, and PDB or mmCIF writing. A Ray backend places complete model replicas on GPUs so independent targets run concurrently, while a direct PyTorch path stays open for custom code. On a matched 1,000-target human-dimer benchmark, it folded 58.5K residues per allocated GPU-hour versus 20.2K for the tested public implementation, which ran out of memory on 29 targets. That roughly 2.90x gap and the accompanying power estimates are why the pipeline shape, not just the model, decides cost.
FAQ
Why does BioNeMo report residues per allocated GPU-hour instead of raw inference speed?
Because the unit of work is a whole structure prediction ending in a PDB or mmCIF file, not a token stream. Residues per allocated GPU-hour folds parsing, tokenization, features, GPU inference, and write-out into one number that matches how folding batches are billed. It also surfaces failed targets that a per-step rate would hide.
Can I expect the published 2.90x throughput gain on my own targets?
Treat it as a workload-specific reference, not a guarantee. It comes from a matched 1,000-target human-dimer benchmark: 58.5K folded residues per allocated GPU-hour versus 20.2K for the tested public implementation, which ran out of memory on 29 targets. The 11 versus 35 MWh figures are rated-power-equivalent estimates that exclude data-center overhead.
The posts behind it
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Topology-Aware Workload Scheduling with NVIDIA Topograph
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Together AI · · AnnouncementCanary rollouts: upgrade models in production without downtime
Deploying a new model checkpoint in production typically requires a tradeoff: a hard swap exposes all users to regressions at once, while a DIY staged cutover relies on manual traffic shifting. Together AI’s rollout system automates this by moving traffic between source and target deployments through configurable canary steps, such as 5%, 25%, 50%, and 100%. Each step runs health checks before shifting traffic, enforces a propagation wait for routing caches, and drains the source only after traffic has moved. Optional metric gates, like p95 latency, run after each step; if a gate trips, the rollout pauses automatically and waits for human action. In a real canary from Qwen2.5-7B to Qwen3.5-9B, a 137% p95 regression was caught at 10% traffic, allowing reversal with no failed requests.
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High-Throughput Structure Prediction with BioNeMo Inference Runtime
Proteome-scale biomolecular structure prediction requires moving large worklists efficiently, and NVIDIA BioNeMo Inference Runtime (BioIR) accelerates supported models on NVIDIA GPUs while retaining a PyTorch workflow. Its end-to-end processor passes InputRequest objects through parsing, tokenization, feature generation, GPU inference, and PDB or mmCIF writing; a Ray backend places complete model replicas on GPUs for concurrent independent inputs, while direct PyTorch integration supports custom code. In a matched 1,000-target human-dimer benchmark, BioIR completed all targets at 58.5K successfully folded residues per allocated GPU-hour versus 20.2K for the tested public implementation, a 2.90× improvement, which ran out of memory on 29 targets. Rated-power-equivalent estimates for one million comparable targets were 11 versus 35 MWh at 8-GPU TDP and 21 versus 64 MWh at full-node maximum power, excluding data-center overhead and several pipeline costs.
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