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ARXIV:2605.21427 · LLM SERVING · SUBMITTED 21 MAY · 20:28 UTC · FRESHNESS STALE
ARXIV:2605.21427LLM SERVINGSUBMITTED 21 MAY · 20:28 UTCFRESHNESS STALECan Hankendi · Rana Shahout · Minlan Yu · Ayse K. Coskun · arXiv
PALS is a power-aware runtime for LLM serving that optimizes GPU power caps with software parameters to improve energy efficiency and QoS.
Opportunity summary
Pain PALS is a power-aware runtime for LLM serving that optimizes GPU power caps with software parameters to improve energy efficiency and QoS.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
PALS is a power-aware runtime for LLM serving that optimizes GPU power caps with software parameters to improve energy efficiency and QoS. While prior systems optimize throughput and latency by batching, scheduling, and parallelism,…
Large language model (LLM) inference has become a dominant workload in modern data centers, driving significant GPU utilization and energy consumption. While prior systems optimize throughput and latency by batching, scheduling, and parallelism, they…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Across multi-GPU systems and both dense and mixture-of-experts (MoE) models, PALS improves energy efficiency by up to 26.3%, reduces QoS violations by 4x to…
LLM Serving moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
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PALS is a power-aware runtime for LLM serving that optimizes GPU power caps with software parameters to improve energy efficiency and QoS.
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Paper Pack
10.48550/arXiv.2605.21427PALS is a power-aware runtime for LLM serving that optimizes GPU power caps with software parameters to improve energy efficiency and QoS.
Abstract
Large language model (LLM) inference has become a dominant workload in modern data centers, driving significant GPU utilization and energy consumption. While prior systems optimize throughput and latency by batching, scheduling, and parallelism, they largely treat GPU power as a static constraint rather than a controllable resource. In this paper, we present a power-aware runtime for LLM serving, PALS, that treats GPU power caps as a first-class control knob and jointly optimizes them with software parameters such as batch size. The system combines lightweight offline power-performance models with a feedback-driven controller to select configurations that satisfy throughput targets while maximizing energy efficiency. We implement PALS within an existing LLM serving framework, vLLM, demonstrating that it requires no model retraining or API changes. Across multi-GPU systems and both dense and mixture-of-experts (MoE) models, PALS improves energy efficiency by up to 26.3%, reduces QoS violations by 4x to 7x under power constraints, and tracks dynamic power budgets. These results highlight the potential of integrating power control directly into LLM inference runtimes, enabling energy-proportional and grid-interactive AI systems.
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Proof status
unverified0 refs; 3 sources; 50% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
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Dimensions overall score 7.0
PROBLEM
PALS is a power-aware runtime for LLM serving that optimizes GPU power caps with software parameters to improve energy efficiency and QoS. While prior systems optimize throughput and latency by batching, scheduling, and parallelism, they largely treat GPU power as a static const...
METHOD
Large language model (LLM) inference has become a dominant workload in modern data centers, driving significant GPU utilization and energy consumption. While prior systems optimize throughput and latency by batching, scheduling, and parallelism, they largely treat GPU power as a...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Across multi-GPU systems and both dense and mixture-of-experts (MoE) models, PALS improves energy efficiency by up to 26.3%, reduces QoS violations by 4x to 7x under power constraints, and tracks dynamic...
WHY NOW
LLM Serving moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
PALS is a power-aware runtime for LLM serving that optimizes GPU power caps with software parameters to improve energy efficiency and QoS. While prior systems optimize throughput and latency by batching, scheduling, and parallelism, they largely treat GPU power as a static constraint rather than a controllable resource.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Large language model (LLM) inference has become a dominant workload in modern data centers, driving significant GPU utilization and energy consumption. While prior systems optimize throughput and latency by batching, scheduling, and parallelism, they largely treat GPU power as a static constraint rather than a controllable resource.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Across multi-GPU systems and both dense and mixture-of-experts (MoE) models, PALS improves energy efficiency by up to 26.3%, reduces QoS violations by 4x to 7x under power constraints, and tracks dynamic power budgets. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
LLM Serving moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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PALS is a power-aware runtime for LLM serving that optimizes GPU power caps with software parameters to improve energy efficiency and QoS.
Segment
LLM Serving
Adoption evidence
No public code link in the paper record yet
Commercial read
7.0/10 public viability
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Substitute
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Build Passport
Build passport pending - Proof Lab budget No verified cost estimate / $7.00 cap
status
missing
reason
passport_row_missing
proof status
unverified
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No verified cost estimate
confidence low
next verification path
Build brief missing until Build Passport data exists.
Source missing: Build Passport payload.
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No prototype path attached.
Validation checklist missing until required assets, cost, and regulatory flags are verified.
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Evidence coverage
OpportunityKernel evidence_receipt
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stale
Verify missing sources before using this as buyer proof. verified:false
Build readiness
BuildPassport EvidenceState
passport absent
stale
Run Proof Lab or inspect typed missing state. verified:false
Artifact maturity
GitHub and Hugging Face maturity payloads
No public artifact surface observed
stale
Open source artifacts or mark the gap as missing. verified:false
Technical feasibility
partial
Current read
Runnable path is not fully verified.
Evidence
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Gaps
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Run minimal reproduction from the Build Passport prototype path.
Market urgency
missing
Current read
Buyer urgency is not verified from source.
Evidence
0 references, 3 sources, 50% evidence coverage.
Gaps
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Buyer clarity
missing
Current read
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Defensibility
missing
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Defensibility signals are missing.
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Refresh defensibility bars with source receipts.
Integration burden
missing
Current read
No public implementation surface observed.
Evidence
No GitHub or Hugging Face payload attached.
Gaps
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Write integration checklist from prototype path and target workflow.
Capital intensity
missing
Current read
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Regulatory load
missing
Current read
No regulatory classification is attached.
Evidence
Build Passport ledger does not include regulatory flags.
Gaps
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Classify regulatory flags before commercialization planning.
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Paper authors are not treated as operators without consent.
People
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Gaps
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Prototype owner missing.
Build Passport does not name an implementer.
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Operator workflow not sourced.
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People
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Regulatory need unclassified.
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ARTIFACTS
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DEFENSIBILITY
Defensibility and confidence evidence pending.
WATCHTOWER
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FORESIGHT
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OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
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