Source
Same Cluster, 33 Points More Utilization: What Changed Was the Order
Dharma-AI developed a constraint-aware GPU allocator that increases hardware utilization and priority-weighted output by optimizing the order of job allocation.
Why it matters
Organizations can maximize their existing expensive GPU investments to process more AI workloads without purchasing additional hardware.
The details
- Utilization increased by up to 33 percentage points compared to FIFO scheduling.
- Priority-weighted output rose by as much as 105% in benchmark scenarios.
- The allocator treats real-time inference demand as a curve instead of fixed reservations.
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In this article
Key connections
Constraint-Aware GPU Allocator competes with FIFO Scheduler
The constraint-aware GPU allocator is benchmarked against the baseline FIFO scheduler across seven scenarios.
Constraint-Aware GPU Allocator is built with Combinatorial Optimization
The allocator uses formal NP-hard combinatorial optimization models and structural constraint heuristics to schedule jobs.
Constraint-Aware GPU Allocator is related to LoRA
The allocator's training forecaster conditions on training strategies such as parameter-efficient LoRA.
Constraint-Aware GPU Allocator is related to SFT
The allocator's workload forecaster handles training techniques including SFT.
Constraint-Aware GPU Allocator is related to DPO
The allocator's workload forecaster handles training optimization loops including DPO.
Constraint-Aware GPU Allocator is related to RLHF
The allocator accounts for training runs using RLHF with sampling loops and reward models.
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Constraint-Aware GPU Allocator is related to RLVR
The allocator distinguishes training workloads utilizing RLVR.
Constraint-Aware GPU Allocator is related to CPT
The allocator's training forecaster incorporates Continued Pre-Training workloads.
Constraint-Aware GPU Allocator is related to AWQ
The allocator's quantization forecast provides distinct handling for the AWQ algorithm.
Constraint-Aware GPU Allocator is related to GPTQ
The allocator's quantization forecast provides distinct handling for the GPTQ algorithm.
Constraint-Aware GPU Allocator is related to bitsandbytes
The allocator's quantization forecast provides distinct handling for the bitsandbytes algorithm.
DPO removes both the reward model and sampling loop required by RLHF.
AWQ and GPTQ represent alternative model quantization algorithms evaluated by the scheduler forecaster.
AWQ competes with bitsandbytes
AWQ and bitsandbytes are alternative quantization algorithms handled by the allocator.
GPTQ competes with bitsandbytes
GPTQ and bitsandbytes are alternative quantization algorithms handled by the allocator.
Related events
Benchmarking Constraint-Aware GPU Allocation Against FIFO Scheduling
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