Hardware-Aware LLM Performance Engineering

Updated 29 August 2025

Hardware-aware LLM performance engineering is a suite of techniques that integrate device characteristics into model quantization, scheduling, and co-design to optimize inference efficiency.
It leverages methods such as activation-aware quantization, operator fusion, and dynamic DVFS to achieve significant speedups, energy savings, and reduced memory usage.
Key approaches include autotuning with JIT compilation and both analytical and ML-based performance forecasting to support scalable LLM deployment on GPUs, CPUs, and edge devices.

Hardware-aware LLM performance engineering encompasses algorithm, software, and system design techniques that explicitly integrate hardware characteristics—such as memory hierarchy, compute architecture, circuit-level timing, and interconnect topology—into the deployment and optimization of LLMs. The domain is driven by the need to accelerate and democratize LLM deployment on diverse platforms, including data center GPUs, CPUs, heterogeneous accelerators, edge NPUs, wafer-scale AI systems, and mobile devices, while meeting stringent latency, throughput, memory, and energy constraints. Its methodologies span model quantization, operator/kernel co-design, scheduling, autotuning, analytical and ML-based performance forecasting, and holistic hardware–software co-design.

1. Principles of Hardware-Aware Quantization

Efficient LLM deployment is often gated by memory bandwidth, cache capacity, and compute throughput. Hardware-aware quantization techniques—most notably Activation-aware Weight Quantization (AWQ) and frameworks like HALO and QuantX—go beyond traditional low-bit quantization by explicitly tying quantization choices to both accuracy preservation and hardware efficiency.

AWQ (Lin et al., 2023) identifies salient weight channels via activation statistics (rather than weight magnitude) and protects them from quantization error by channel-wise scaling. The optimal scaling factor $s^*$ for each channel is determined analytically by minimizing

$\alpha^* = \arg\min_\alpha \left\| Q(W \cdot \text{diag}(s_X^\alpha)) \cdot (\text{diag}(s_X^{-\alpha}) X) - W X \right\|$

with $s_X$ as the channel-wise activation average.

HALO (Juneja et al., 27 Feb 2025) incorporates MAC unit critical-path delay and energy profiles directly into its PTQ framework, partitioning weights based on Fisher information sensitivity and explicitly quantizing less-sensitive tiles to values that favor lower circuit delay. The quantization process is co-optimized with accelerator DVFS settings: $\min_{(V, f)} E(V, f)$ s.t. $1/f \geq \text{CriticalPath}$ .
QuantX (Mazher et al., 12 May 2025) adapts quantization centroids and group sizes per-layer and per-matrix, balancing hardware numeric support, minimal dequantization cost, and statistical weight distributions. It selects between uniform and non-uniform quantization via empirical assessment of metrics such as $\Vert \mathbf{A}_{\mathrm{unquantized}} - \mathbf{A}_{\mathrm{quantized}} \Vert_F$ , where $\mathbf{A}$ is the attention map.

Hardware-aware quantization methods have demonstrated up to $4\times$ memory reduction (e.g., INT4 weight-only quantization), performance improvement of up to 270% (HALO), and retention of accuracy within 6% of FP16 baselines (QuantX), all while making quantized models practical for edge and mobile deployment (Lin et al., 2023, Juneja et al., 27 Feb 2025, Mazher et al., 12 May 2025).

2. Operator, Kernel, and Parallelism Co-Design

Tailoring operator and kernel implementations to device-specific constraints is central to hardware-aware engineering:

Operator Fusion and Weight Packing: On-the-fly dequantization fused within matrix multiplication kernels (e.g., in TinyChat (Lin et al., 2023)), SIMD-/platform-aware weight packing (ARM NEON vectorization), and kernel fusion for layer normalization and QKV projections directly target overheads that would otherwise dominate in memory-bound workloads.
Autotuning with JIT Compilation: Performance portability across GPU vendors is addressed by combining just-in-time compilation (e.g., Triton) with autotuning over kernel parameter spaces. The configuration—choices over tiling ( $\text{BLOCK}_M,\text{BLOCK}_N$ ), warp count, stages—produces highly device-specific code, empirically identifying $x^* = \arg\max_{x \in \mathcal{X}} P(x)$ , where $P$ is throughput (Ringlein et al., 30 Apr 2025).
Wafer-Scale Parallelism: WaferLLM introduces the PLMR model to codify wafer-scale architecture constraints—massive parallelism, highly non-uniform mesh latency, limited local memory, and restricted routing—and proposes MeshGEMM and MeshGEMV, which bound communication and memory cost to mesh diameter and avoid pipeline bubbles (He et al., 6 Feb 2025). This yields end-to-end decoding throughput up to $39\times$ higher and up to $606\times$ faster GEMV than A100 clusters.

The productivity benefits of hardware-aware performance autotuning are exemplified by SwizzlePerf (Tschand et al., 27 Aug 2025), where LLM-guided spatial remapping (swizzling) of GEMM kernel blocks achieves up to $2.06\times$ speedup and $70\%$ higher L2 hit rate, with hardware configuration and profiling context guiding the search.

3. System-Level Scheduling, Optimization, and Controls

LLM serving exhibits distinct compute and memory characteristics in prefill (prompt processing) and decode (token-wise generation) stages. Hardware-aware systems target optimal scheduling and resource allocation for these stages:

Workload-Aware Scheduling: Distinguishing prefill and decode phases, Intelligent Router (Jain et al., 24 Aug 2024) uses a reinforcement learning-based policy, incorporating a response-length predictor and a mixing impact formulation, to optimize query assignment, reducing E2E latency by over 11%. Monolithic batchers that ignore phase distinctions experience degraded scheduling efficiency.
Dynamic Frequency and Power Control: GreenLLM (Liu et al., 22 Aug 2025) demonstrates substantial energy savings by separating DVFS policies for prefill and decode, guided by compact latency-power models:

$E_\text{total}(f) = P(f) \cdot \text{busy}(f) + P_\text{idle}\cdot [D-\text{busy}(f)]$

and operating in a queueing-aware, SLO-constrained optimization loop. This approach achieves up to 34% energy reduction with minimal SLO violations.

Integrated controllers for LLM clusters (Predictable LLM Serving (Darzi et al., 27 Aug 2025)) combine dynamic MIG reconfiguration and PCIe-aware placement to reduce SLO miss-rate by $\sim 32\%$ and p99 latency by 10–15%, using per-tenant tail sampling and dwell/cool-down logic.

4. Analytical and ML-Based Performance Forecasting

Accurate prediction of LLM inference performance under diverse hardware, workload, and optimization choices is central to deployment planning, cost management, and scaling.

Analytical Modeling: LIFE (Patwari et al., 29 Jul 2025) uses modular operator-level analytical models, parameterized by hardware TOPS and memory bandwidth, to forecast TTFT, TPOT, and TPS, enabling rapid simulation of the impact of quantization, KV compression, LoRA adapters, and operator fusion. The framework employs equations:

$\text{TTFT} = \max(t_c, t_m),\ \text{where}\ t_c=\sum_\text{op}\frac{\text{TOPS}_\text{op}}{\epsilon_{ec,\text{op}}\cdot\text{TOPS}}+t_\text{dispatch,op}$

avoiding the need for costly benchmarking.

LLM-Driven Performance Predictors: LLMPerf (Nguyen et al., 14 Mar 2025) estimates OpenCL kernel runtime from code and launch configuration, achieving mean absolute percentage error (MAPE) of 24.25% on large-scale synthetic validation. LLMulator (Chang et al., 25 Aug 2025) treats performance as categorical token sequences, using RL-based dynamic calibration for input-adaptive dataflow, reducing cycle prediction error and supporting range-agnostic, interpretable estimation.

LLM-Pilot (Łazuka et al., 3 Oct 2024) combines large-scale benchmarking with XGBoost regression, integrating both LLM and GPU profile features, to select hardware that meets SLA constraints at minimum cost, delivering performance-compliant recommendations 33% more frequently while cutting cost by 60% on average compared to alternative approaches.

5. Hardware-Aware Methods for Synthesis, Test, and Co-Design

Beyond inference kernels, hardware-aware performance engineering extends to design automation and verification.

High-Level Synthesis: HLSPilot (Xiong et al., 13 Aug 2024) leverages LLMs for automatic decomposition of C/C++ kernels and in-context learning of HLS directives, integrating performance profiling (e.g., gprof) and external design space exploration (DSE) to systematically tune code for hybrid CPU-FPGA architectures. This framework matches or exceeds the performance of hand-crafted accelerators across standard benchmarks.
Verification and Test: VerilogReader (Ma et al., 3 Jun 2024) demonstrates that LLMs, guided by prompt engineering and integrated simulator coverage data, can generate coverage-directed tests that significantly outperform random generation—achieving 100% code coverage with far fewer input cycles, especially in sequential circuits with hard-to-reach states.

Such approaches suggest LLMs can serve as "hardware-aware agents" not only in inference but across the hardware/software engineering stack, including code gen, test, and performance validation.

6. Trends, Limitations, and Future Directions

Hardware-aware LLM performance engineering is moving towards full-stack, cross-platform, and self-optimizing systems:

Open Ecosystem Vision: The three-layer decoupled architecture (Hou et al., 6 Mar 2025)—application, protocol, and hardware layers—models cross-platform, modular, and hardware–software co-design strategies for efficient and secure LLM deployment, emphasizing adaptive scheduling, federated execution, and security.
Wafer-Scale and Edge Directions: As wafer-scale chips (PLMR model, MeshGEMM/MeshGEMV (He et al., 6 Feb 2025)) and edge compute emerge, operator co-design, minimal memory traffic, and shift-based cache management will become essential.
End-to-End Automation: Tools like GPU Kernel Scientist (Andrews et al., 25 Jun 2025) and SwizzlePerf (Tschand et al., 27 Aug 2025) portend fully autonomous, LLM-driven performance engineers that iteratively generate, evaluate, and deploy hardware-specialized code modifications using only observed timings and contextual architectural knowledge.

Ongoing limitations are the lack of generalizable profiling for new hardware (especially for high-level LLM kernels), imperfect modeling of dynamic workload characteristics, and the sensitivity of LLM-guided optimization to prompt context, history, and architecture-specific knowledge. Extensions to fully automated, multi-objective optimization and explainable performance forecasting are active areas of research.

7. Summary Table: Key Hardware-Aware Techniques and Outcomes

Technique / Framework	Hardware Focus	Key Outcomes
AWQ (Activation-aware quant.)	Memory-bound, low-bit quantization	$3\times$ speedup, democratized on-device LLMs (Lin et al., 2023)
HALO/PTQ	MAC timing, DVFS, energy	270% perf, 51% energy saved (Juneja et al., 27 Feb 2025)
Autotuning/JIT	GPU code generation, portability	2.3 $\times$ faster, 70 $\times$ smaller kernels (Ringlein et al., 30 Apr 2025)
WaferLLM/PLMR	Wafer-scale mesh, on-chip memory	$39\times$ speedup over A100 (He et al., 6 Feb 2025)
GreenLLM	Fine-grained DVFS (GPU)	34% energy reduction, $<$ 3.5% SLO loss (Liu et al., 22 Aug 2025)
LIFE/LLMPerf/LLMulator	Analytical & ML-based prediction	MAPE ≈24%, hardware/dataset agnostic (Nguyen et al., 14 Mar 2025, Patwari et al., 29 Jul 2025, Chang et al., 25 Aug 2025)
SwizzlePerf	Cache/bottleneck tuning (GPU)	Up to 2.06 $\times$ speedup, 70% L2 hit rate gain (Tschand et al., 27 Aug 2025)

Hardware-aware LLM performance engineering systematically incorporates device, circuit, and system-level knowledge into model, operator, scheduling, and deployment design, producing scalable, cost-effective, and energy-efficient solutions for LLM inference across an increasingly heterogeneous and performance-constrained hardware landscape.