Hardware load and Large Language Model (LLM) inference performance

Research Question

How does hardware resource load, Central Processing Unit (CPU), Graphics Processing Unit (GPU), and memory pressure, affect Large Language Model (LLM) inference performance, specifically latency, throughput, and output quality consistency, and what are the practical implications for Artificial Intelligence (AI) agent reliability in production deployments?

Findings

(Populated from §6 Synthesis above.)

Executive Summary

Hardware load does affect LLM inference reliability, but it does so mainly by pushing serving systems across batching, memory, and numerical-precision thresholds that inflate latency and sometimes change generated tokens, not by making models semantically worse merely because GPU utilization is high.

The consulted evidence shows that throughput and latency degrade in threshold-like steps when schedulers rebalance prompt-processing prefill work against token-by-token decode work, when key-value (KV) cache space becomes scarce, or when CPU coordination remains the bottleneck despite abundant GPU capacity.

Output consistency risk is real, but the consulted evidence ties it to batch- or hardware-dependent numerical divergence, including changed batch composition, changed precision mode, or changed accelerator path, rather than to utilization telemetry by itself.

For production agents, the practical answer is to monitor queue depth, preemption, batch shape, context growth, precision mode, and model pinning as first-class reliability signals, while keeping deterministic policy enforcement outside raw model outputs for consequential actions.

Key Findings

1. Under load, Large Language Model serving slows in threshold-like jumps that follow scheduler changes between prompt-processing prefill work and token-by-token decode work, not a smooth linear decline tied to Graphics Processing Unit utilization percentages.
2. Key-value (KV) cache pressure becomes a major concurrent-serving bottleneck once batch demand outruns available memory, and vLLM responds by preempting and recomputing requests after space is freed.
3. Even GPU-heavy inference stacks stay sensitive to Central Processing Unit launch and placement overhead, so low-batch latency can remain CPU-bound long after accelerator capacity appears available.
4. TGI, Ollama, vLLM, and llama.cpp all expose contention as explicit queueing, batching, and concurrency settings, which makes operator tuning part of reliable serving rather than an optional optimization.
5. Load-sensitive output drift appears when batch shape or hardware path changes the numerical execution path, and the reviewed evidence documents changed sampled tokens plus measurable accuracy shifts under those conditions.
6. The consulted evidence does not isolate raw utilization percentages alone as a proven cause of semantic degradation once the numerical path is held fixed; the documented quality risk instead comes from changed precision modes, quantized caches, or altered live batching.
7. Reliable agent deployments need workload-class isolation, bounded concurrency, warm model residency, and deterministic external checks, since latency spikes and occasional token drift can compound across multi-step workflows.

Assumptions

• Assumption: Agent reliability is defined here as staying inside bounded latency and bounded decision-drift envelopes rather than reproducing byte-identical text. Justification: multi-step production agents usually fail first through missed orchestration deadlines or changed action choices.

Analysis

The consulted support for queueing, batching, memory pressure, and hardware bottlenecks comes mainly from official serving documentation and system papers that describe exposed control surfaces and measured serving behaviour in the systems under study.

For output-consistency claims, the most direct consulted evidence comes from batch- or hardware-sensitive divergence results, because Yuan et al. and the vLLM frequently asked questions page both tie changed execution paths to changed tokens or task outcomes.

The main interpretive choice was to separate "high hardware load" from "changed numerical path," because the consulted sources strongly support the second as a mechanism for output drift and only indirectly support the first.

Hypothesis: hardware load is only a service-speed problem and not an output-quality problem.

I reject that broader claim because vLLM and Yuan et al. both tie changed execution paths to changed sampled tokens or measured task accuracy, even though the evidence remains strongest when load is expressed as changed batch or hardware state rather than as utilization alone.

That separation narrows the recommendation to concrete operational controls: operators should monitor queue depth, preemption count, batch size, context growth, and precision mode instead of treating a single utilization percentage as a sufficient reliability diagnosis.

Risks, Gaps, and Uncertainties

• Few consulted primary sources measure complete multi-step agent task success under controlled hardware-load sweeps, so most direct evidence stops at latency, throughput, token divergence, or single-task accuracy variance.
• TGI documentation describes its control surfaces, but the consulted official pages provide fewer public head-to-head benchmark numbers than the vLLM and Sarathi-Serve sources, so cross-system performance ranking remains partial.
• Ollama and llama.cpp official sources document concurrency and memory controls but do not provide equally strong primary studies of output drift under production load, so cross-stack conclusions about quality variance remain medium confidence.
• The numerical-drift evidence is strongest for specific models, hardware, and precisions, especially reasoning models under limited precision, so portability to every inference stack and model family should be treated as an informed but not universal conclusion.

Open Questions

• Which queue-depth, preemption, or batch-shape thresholds best predict real agent task failure rather than only slower response times?
• How much output drift remains in production-serving stacks that adopt batch-invariant kernels or full 32-bit floating-point inference?
• Can prompt-length-aware routing and capacity classes outperform global concurrency caps for mixed fleets of short-chat and long-horizon agent workloads?

sources

• [x] vLLM OpenAI-Compatible Server
• [x] vLLM Optimization and Tuning
• [x] vLLM Frequently Asked Questions
• [x] Hugging Face Text Generation Inference
• [x] Hugging Face Text Generation Inference Architecture
• [x] Kwon et al. (2023) Efficient Memory Management for Large Language Model Serving with PagedAttention
• [x] Agrawal et al. (2024) Taming Throughput-Latency Tradeoff in Large Language Model Inference with Sarathi-Serve
• [x] Yuan et al. (2025) Understanding and Mitigating Numerical Sources of Nondeterminism in Large Language Model Inference
• [x] Vellaisamy et al. (2025) Characterizing and Optimizing Large Language Model Inference Workloads on CPU-GPU Coupled Architectures
• [x] Thinking Machines Lab (2025) Defeating nondeterminism in Large Language Model inference
• [x] Ollama Frequently Asked Questions
• [x] Ollama envconfig/config.go
• [x] ggml-org llama.cpp HTTP Server README
• [x] ggml-org llama-bench README
• [x] Anyscale Understand LLM latency and throughput metrics