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arxiv: 2604.15522 · v1 · submitted 2026-04-16 · 💻 cs.AR · cs.SY· eess.SY

EasyRider: Mitigating Power Transients in Datacenter-Scale Training Workloads

Dillon Jensen , Obi Nnorom Jr. , Grant Wilkins , Hugo Budd , Ram Rajagopal , Juan Rivas-Davila , Phil Levis This is my paper

Pith reviewed 2026-05-10 09:22 UTC · model grok-4.3

classification 💻 cs.AR cs.SYeess.SY
keywords power transientsdatacenter AI trainingGPU power managementauxiliary energy storagegrid stabilitysynchronous workloadsrack-level filtering
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The pith

EasyRider uses rack-level auxiliary energy storage and passive components to keep GPU power swings within grid safety limits without software changes or energy waste.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Large AI training jobs run thousands of GPUs in tight synchrony, so power draw can jump from peak to idle in milliseconds during collective communication, startup, shutdown, and checkpointing. These swings create steep ramps, voltage shifts, and reactive transients that threaten transformers and grid equipment. The paper introduces EasyRider, a rack-scale power architecture that adds passive filtering plus actively controlled auxiliary storage to smooth those transients at the hardware level. A monitoring layer manages the storage to extend its life under repeated cycling. The result is that rack power variations stay inside published grid safety bounds while the training frameworks themselves remain untouched.

Core claim

EasyRider attenuates rack-level power transients from synchronized GPU workloads to levels that satisfy grid infrastructure requirements by combining passive components with actively controlled auxiliary energy storage, while a software monitor maximizes storage lifetime and no modifications are made to training frameworks or energy is dissipated.

What carries the argument

EasyRider rack power architecture: passive filters plus actively controlled auxiliary energy storage whose charge/discharge is governed by a lifetime-maximizing software monitor.

If this is right

  • Datacenters could deploy the same rack hardware across mixed GPU generations and workload profiles without rewriting training code.
  • Grid operators would see reduced risk of equipment stress from AI clusters even as training jobs scale to larger synchronized groups.
  • Energy storage sizing can be chosen to cover the worst-case millisecond transients observed in published traces and testbed runs.
  • No extra energy is lost to resistive dissipation because the storage buffers rather than dumps the excess power.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the approach works at rack scale, operators could avoid costly grid upgrades when adding more AI capacity.
  • The same storage layer might later support brief ride-through during utility outages if sized and controlled appropriately.
  • Heterogeneous clusters mixing training and inference jobs would still benefit because the hardware acts on measured power regardless of job type.

Load-bearing premise

The auxiliary energy storage can survive the frequent charge and discharge cycles created by real AI training patterns without wearing out quickly, and hardware control alone is enough to hold power within grid limits.

What would settle it

A multi-week run on a production-scale rack where either the storage capacity falls below usable levels from cycle wear or measured power ramp rates still exceed grid safety thresholds.

Figures

Figures reproduced from arXiv: 2604.15522 by Dillon Jensen, Grant Wilkins, Hugo Budd, Juan Rivas-Davila, Obi Nnorom Jr., Phil Levis, Ram Rajagopal.

Figure 1
Figure 1. Figure 1: The EasyRider prototype is able to smooth the rack power draw to within grid ramp rate limits. While rack power drops rapidly by 80%, the grid observes a gradual power draw change over tens of seconds. near-instantaneous 80% power reduction, as shown in Fig￾ure 1 [12, 37]. At the scale of modern training jobs in dat￾acenters, these swings create a major problem. A modern training job that uses 50,000 GPUs … view at source ↗
Figure 2
Figure 2. Figure 2: Modern data center power hierarchy and where EasyRider fits in. This particular design shows disaggregated power from the rack with a connection to busbar distribution per-row. Design variations may include in-rack UPSes or other power conversion components. denied because of the instability that training can bring to the grid [44]. 2.3 Datacenter Power Hierarchy [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Time- and frequency-domain representation of a power trace based on [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: EasyRider architecture. Software components are shown in white, hardware in gray. EasyRider is agnostic to the training workload and can be integrated into existing datacenter power hierarchies with appropriate conversions and component sizing. the highest significant frequency in the spectrum determines how steeply the signal can change in time. From this perspective, training racks need a low-pass filter… view at source ↗
Figure 5
Figure 5. Figure 5: The hardware system architecture consists of three main components: (1) an input filter to buffer the power grid against high-frequency power fluctuations„ (2) a DC-DC converter to maintain constant rack voltage, and (3) an auxiliary battery system to store or dispatch energy during transients. This configuration allows the power grid to gradually transition between different load conditions while the rack… view at source ↗
Figure 7
Figure 7. Figure 7: EasyRider’s frequency response, showing the com￾bined effect of the input filter and controlled energy stor￾age system. The input filter attenuates fluctuations above 𝑓𝑓 , while the auxiliary energy compensates for fluctuations above 𝑓𝑏. Together, they ensure the rack meets grid speci￾fications. “Relative Magnitude” indicates the magnitude of fluctuations seen by the DC distribution grid relative to those … view at source ↗
Figure 8
Figure 8. Figure 8: Photo of the built EasyRider prototype system. that would result in sudden drops or jumps in current to the battery). The controller applies only the first action from each solve and re-optimizes at the next interval with a fresh SoC reading from the BMS. A narrow margin of error around the target brings the current to zero so that the battery avoids unnecessary current fluctuations near 𝑆 ∗ . The resultin… view at source ↗
Figure 9
Figure 9. Figure 9: (a) Conditioned power trace using EasyRider to power a DC load with a jittery training power trace. (b) Corresponding ramp rate of power drawn from the grid compared to the unconditioned ramp rate as a function of time. The EasyRider prototype is able to constrain the rack’s ramp rate to less than ±10% of its rated power per second. 10 2 10 1 10 0 10 1 Frequency (Hz) 10 5 10 4 10 3 10 2 10 1 10 0 Power (p.… view at source ↗
Figure 10
Figure 10. Figure 10: The filtering effect of EasyRider keeps harmonic content below a grid-imposed limit 𝛼 for frequencies above 𝑓𝑐 = 2 Hz, even though the rack power trace contains signif￾icant energy in this band. rack presents a power waveform with | 𝑑𝑃/𝑑𝑡 |≤ 𝛽, the ag￾gregate datacenter ramp rate is likewise constrained.3 This allows operators to reason about campus-wide limits in terms of per-rack design rather than per-… view at source ↗
Figure 11
Figure 11. Figure 11: shows the resulting normalized power traces for the raw Titan X workload, EasyRider, and software burn. We delay the start of the Titan X trace by approximately 41 s to account for the warm-up period required by software burn, and normalize all traces to the Titan X blade’s TDP. While observing [PITH_FULL_IMAGE:figures/full_fig_p010_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Battery adjustment for an SoC that is over the desired setpoint. Our control system updates the corrective current every 5 seconds to return to 𝑆mid = 0.5. Without this correction, the battery would drift slowly towards the upper bound. 7.4 Energy Storage Stability and Lifetime As discussed in Section 6, over hours of training, our system produces a monotonic SoC drift. Our software controller exists to c… view at source ↗
Figure 13
Figure 13. Figure 13: Expected smoothing behavior of a 40 MW training cluster where every rack is equipped with an EasyRider power supply, vs. the unfiltered case. 𝛽 represents the EasyRider-enforced maximum rack power ramp rate (see Section 3), as a proportion of maximum rated rack power per second. The IT trace (red) is scaled from actual measurements from running a training job on H100 GPUs. The highest ramp rate recorded i… view at source ↗
read the original abstract

Large-scale AI model training workloads use thousands of GPUs operating in tightly synchronized loops. During synchronous communication, start-up, shut-down, and checkpointing, GPU power consumption can swing from peak to idle within milliseconds. These large and rapid load swings endanger grid infrastructure as they induce steep power ramp rates, voltage and frequency shifts, and reactive power transients that can damage transformers, converters, and protection equipment. To solve this problem, we introduce EasyRider, a power architecture to mitigate power fluctuations at the rack level. EasyRider uses passive components and actively-controlled auxiliary energy storage to attenuate rack power swings. A software system continually monitors the energy storage system to maximize its lifetime in the presence of frequent charge/discharge cycles. EasyRider filters rack power variations to be within grid safety requirements without requiring software modifications to AI training frameworks or wasting energy. We evaluate EasyRider on a 400VDC-rated prototype system against published workload traces and our own GPU testbed, demonstrating its effectiveness across heterogeneous power levels and workload power profiles.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 3 minor

Summary. The paper introduces EasyRider, a rack-level power architecture that combines passive components with actively controlled auxiliary energy storage and a monitoring software layer to attenuate millisecond-scale power transients from synchronized GPU training workloads. The design aims to keep rack power variations, voltage excursions, and dP/dt within grid safety limits without modifying AI frameworks or dissipating energy. Effectiveness is asserted via evaluation on a 400 VDC prototype against published traces and a small GPU testbed across heterogeneous power levels.

Significance. If the quantitative claims hold, the work addresses a timely infrastructure bottleneck for hyperscale AI training: rapid load swings that threaten grid equipment. The combination of hardware filtering with lifetime-aware software control, without framework hooks or energy waste, would be a practical contribution to datacenter power management.

major comments (3)
  1. [Evaluation] Evaluation section: the manuscript asserts that the prototype demonstrates effectiveness against traces and the testbed, yet supplies no quantitative metrics (e.g., achieved dP/dt reduction, peak voltage deviation, or fraction of transients kept inside grid limits), error bars, or statistical analysis of the results. This absence leaves the central effectiveness claim without supporting evidence.
  2. [System Design / Software Monitoring] Auxiliary storage and lifetime monitoring: the design relies on the storage surviving thousands of high-rate charge/discharge cycles per day and on the software successfully extending its lifetime, but no cycle-life data, degradation model, or closed-loop lifetime measurements are presented. These assumptions are load-bearing for practicality.
  3. [Control Architecture] Reactive control analysis: the paper claims passive elements plus real-time active control suffice without advance knowledge of collective GPU events (barriers, checkpoints), yet provides no response-time measurements, rack-scale simulation, or worst-case transient response data to confirm excursions remain within limits.
minor comments (3)
  1. [Hardware Architecture] Specify the exact chemistry or technology of the auxiliary storage (supercapacitor, lithium-ion, etc.) and its key ratings (ESR, cycle life at the observed C-rates).
  2. [Prototype Implementation] Add component values, schematic details, and measured efficiency of the passive filter network in the prototype description.
  3. [Figures] Ensure all figures include quantitative axes, legends, and clear comparison between baseline and EasyRider traces.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed review. The comments identify key areas where additional evidence and analysis would strengthen the manuscript. We address each major comment below and will revise the paper to incorporate the requested quantitative data, models, and measurements.

read point-by-point responses

  1. Referee: [Evaluation] Evaluation section: the manuscript asserts that the prototype demonstrates effectiveness against traces and the testbed, yet supplies no quantitative metrics (e.g., achieved dP/dt reduction, peak voltage deviation, or fraction of transients kept inside grid limits), error bars, or statistical analysis of the results. This absence leaves the central effectiveness claim without supporting evidence.

    Authors: We agree that the evaluation relies primarily on visual comparisons in figures without accompanying numerical summaries or statistical support. In the revised manuscript we will add explicit metrics including achieved dP/dt reductions (with before/after values), peak voltage deviations, the fraction of transients remaining inside grid limits, error bars from repeated runs, and basic statistical analysis across the workload traces and testbed experiments. revision: yes

  2. Referee: [System Design / Software Monitoring] Auxiliary storage and lifetime monitoring: the design relies on the storage surviving thousands of high-rate charge/discharge cycles per day and on the software successfully extending its lifetime, but no cycle-life data, degradation model, or closed-loop lifetime measurements are presented. These assumptions are load-bearing for practicality.

    Authors: The software layer applies conservative limits on state-of-charge, temperature, and cycle counts using standard degradation models from the literature. We acknowledge that the manuscript does not present the explicit model or projected lifetime numbers. We will add a new subsection describing the degradation model employed, the cycle-life projections under the observed high-rate cycling, and how the monitoring policy extends usable lifetime, supported by references to established battery models. revision: yes

  3. Referee: [Control Architecture] Reactive control analysis: the paper claims passive elements plus real-time active control suffice without advance knowledge of collective GPU events (barriers, checkpoints), yet provides no response-time measurements, rack-scale simulation, or worst-case transient response data to confirm excursions remain within limits.

    Authors: The architecture description includes the time constants of the passive elements and the sub-millisecond sampling rate of the active controller. We recognize that explicit worst-case response data and simulation results are missing. In the revision we will include measured response times from the 400 VDC prototype, results from rack-scale transient simulations of synchronized GPU workloads, and analysis demonstrating that voltage and dP/dt excursions remain within grid limits under reactive control alone. revision: yes

Circularity Check

0 steps flagged

No circularity: design proposal with empirical evaluation, no derivations or self-referential reductions.

full rationale

The paper presents a hardware-software architecture for attenuating rack-level power transients using passive components and auxiliary storage, with a monitoring system for lifetime management. Evaluation relies on prototype measurements against workload traces and a GPU testbed. No equations, fitted parameters, predictions, or first-principles derivations appear in the provided text or abstract. No self-citations are invoked as load-bearing uniqueness theorems or ansatzes. The central claims rest on the proposed design's measured performance rather than any reduction to prior inputs by construction. This is a standard systems paper with no mathematical chain to inspect for circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim depends on unstated assumptions about storage durability under AI workload cycling patterns and the sufficiency of rack-level passive-plus-active filtering; no free parameters or invented physical entities are explicitly introduced beyond the system name itself.

axioms (1)
  • domain assumption Rapid power swings from synchronized GPU training can be attenuated to grid-safe levels by rack-level auxiliary energy storage without software changes to training frameworks.
    Invoked as the basis for the EasyRider design and its claimed effectiveness.
invented entities (1)
  • EasyRider power architecture no independent evidence
    purpose: Mitigate rack-level power transients in AI training
    The proposed integrated hardware-software system; no independent falsifiable evidence provided beyond the prototype description.

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