Hardware Acceleration of LLMs

Updated 25 December 2025

Hardware acceleration of LLMs is a multidimensional approach that leverages GPUs, FPGAs, ASICs, and PIM to optimize throughput, latency, and energy consumption.
Algorithmic innovations like quantization, operator fusion, and parallel decoding reduce computational load and memory footprint, enabling efficient token prediction.
Hardware-software co-design and memory-optimized architectures enhance scalability, supporting long-context inference and real-time deployment challenges.

Hardware acceleration of LLMs encompasses both algorithmic and architectural techniques to achieve high-throughput, low-latency, and energy-efficient inference across diverse hardware platforms. The recent surge in LLM deployments has amplified performance bottlenecks in GPU tensor core utilization, memory footprint, parallel decoding, and scalability. Key research trajectories focus on exploiting hardware-aware operator fusion, quantization-induced computation reuse, memory-optimized architectures, and algorithm–hardware co-design frameworks to address these challenges and optimize real-world deployments (Chen et al., 28 May 2024, Salmani et al., 24 Feb 2025, Huang et al., 31 Jan 2025, Ahadi et al., 26 Sep 2025, Ma et al., 26 Sep 2024, Kachris, 18 Jan 2024, Huang et al., 16 Jun 2024, Malekar et al., 31 Mar 2025, Huang et al., 31 Jul 2024, Bournias et al., 8 Nov 2024, Ma et al., 26 Aug 2025, Wang et al., 12 Jun 2025, Chen et al., 1 Sep 2024, Liang et al., 7 Apr 2025, Liu et al., 24 Apr 2025, Chitty-Venkata et al., 31 Oct 2024, Koilia et al., 5 Sep 2024, Li et al., 6 Oct 2024).

1. Architectural Paradigms and Platform Overview

Current hardware acceleration approaches span GPU tensor/matrix cores, FPGA custom systolic arrays, ASICs with compressed dataflows, and processing-in-memory (PIM) engines. GPUs (e.g., NVIDIA A100, H100) are optimized for mixed-precision matrix-multiply–accumulate paths and frequently exploit layer fusion and parallel decoding for throughput. FPGAs deploy reconfigurable processing architectures such as group vector systolic arrays and custom dataflow for tensorized compression, supporting flexible quantization and sparsity. ASICs offer fixed-function sparse attention and low-bit MAC arrays, often integrating algorithm–architecture co-design for token/head pruning. PIM solutions leverage analog/digital crossbar arrays, achieving near-memory computation with simultaneous capacity and bandwidth scaling (Kachris, 18 Jan 2024, Li et al., 6 Oct 2024, Koilia et al., 5 Sep 2024, Malekar et al., 31 Mar 2025, Liu et al., 24 Apr 2025).

Table: Peak Accelerator Metrics Across Platforms (summarized from (Chitty-Venkata et al., 31 Oct 2024, Koilia et al., 5 Sep 2024))

Platform	Throughput (tokens/s)	Energy Efficiency (tokens/J)
GPU (A100/H100)	~4,800–9,200	~0.5–0.83
FPGA	~100–300	~0.2–2.06
ASIC	~516–1,800	~0.86–18.6
PIM/NDP	~500–3,000	~10–46.7

2. Algorithmic Optimizations for Hardware Utilization

Efficient LLM inference necessitates algorithmic advances tightly coupled to hardware. Quantization, by reducing computation bitwidth from FP16/FP32 to INT4/INT8, enables high memory bandwidth utilization and activation/weight data packing, yielding multi-fold efficiency gains. Parallel and speculative decoding frameworks, such as Medusa and Parallel Prompt Decoding (PPD), generate multiple token guesses in one forward pass, integrating sparse attention masks to maximize tensor core occupancy while maintaining autoregressive dependencies. Operator fusion decomposes normalization (LayerNorm, Softmax) into element-wise and global scaling components, permitting concurrent GEMM and normalization on distinct compute engines, thus hiding latency and boosting throughput (Chen et al., 28 May 2024, Salmani et al., 24 Feb 2025, Huang et al., 16 Jun 2024, Ma et al., 26 Aug 2025, Ma et al., 26 Sep 2024).

Key PPD Equations (from (Chen et al., 28 May 2024)):

Multi-token prediction via prompt tokens:

$p_\theta\bigl(y_{i+j+1}\mid x,\;y_{1:i},\;t_{i+1:i+j}\bigr)$

Ensemble averaging:

$\hat p(y_{i+j+1}) = \frac1n \sum_{r=1}^n p_\theta(\cdot \mid \cdot)$

Operator Fusion (from (Salmani et al., 24 Feb 2025)):

Fused LayerNorm and linear layer:

$z = (1/\sqrt{\sigma^2+\epsilon}) \cdot [(x-μ)\Gamma F] + βF$

Fused Softmax and linear layer:

$z = (1/S) \cdot [uV],\ \text{where}\ u=exp(x)$

3. Compression and Quantization-Induced Hardware Techniques

Compression schemes such as Tensor-Train Decomposition (TTD) and structured pruning are integrated into hardware accelerators to shrink model parameters and computation cost. TTD decomposes large linear layers in LLMs into low-rank tensor products, yielding high compression ratios (up to 1.94× on 6B models) and direct mapping to group vector systolic arrays for efficient inference on FPGA. Quantization at 8-bit or lower enables advanced caching strategies; for example, AxLLM caches multiplication results for repeated weight values afforded by low-bit quantization, reaching up to 90% reduction in multiplies, 1.7× speedup, and 28% lower energy without retraining (Huang et al., 31 Jan 2025, Ahadi et al., 26 Sep 2025, Wang et al., 12 Jun 2025).

Table: Quantization vs. Compression Effect (summarized from (Huang et al., 31 Jan 2025, Ahadi et al., 26 Sep 2025))

Method	Compression Ratio	Computation Reduction	Accuracy Drop (PPL)
TTD + INT4	~1.60–1.94×	~50–60%	+2.62 (LLaMA2-7B)
AxLLM (Q=8b)	—	up to 90%	<0.2 PPL

4. Memory-Efficient Architectures and Scalability

Memory hierarchy and capacity are pivotal for LLM acceleration, particularly for long-context inference. Architectures such as DIMM-PIM (L3) offload the memory-bound multi-head attention decoding phase to scalable DIMM-side PIM compute, addressing the linear growth of KV-cache with context length. L3 resolves DRAM data layout mismatches, pipelines communication to hide PCIe transfer latency, and adaptively schedules GPU and PIM work, yielding up to 6.1× speedup over HBM-PIM and supporting larger batch sizes without increasing time-between-tokens (Liu et al., 24 Apr 2025). Mobile frameworks (MNN-LLM) deploy hybrid DRAM–Flash storage with cold embeddings in Flash and hot weights/KV-cache in DRAM, using quantization and multicore scheduling to maximize edge device efficiency (Wang et al., 12 Jun 2025).

5. Pipeline, Parallelism, and Speculative Decoding Designs

Modern hardware acceleration exploits pipeline and parallelism in both compute and scheduling. AcceLLM introduces a redundancy-based pipeline that pairs instances, duplicates KV caches, and dynamically schedules prefill and decode tasks to balance load and minimize remote access penalties. This achieves up to 30% better latency and cost efficiency than monolithic batching or static disaggregation, supporting linear scaling to large clusters and robust load balancing (Bournias et al., 8 Nov 2024). Parallel prompt decoding synergizes with speculative frameworks, partially restoring autoregressive dependencies via learned prompt tokens and sparse tree attention, providing orthogonal throughput gains over draft-verify schemes (Chen et al., 28 May 2024).

6. Hardware–Software Co-Design and Compilation

Algorithm–hardware co-design underpins many advances in hardware-accelerated LLM inference. AccLLM couples semi-structured 2:4 pruning, Λ-shaped long-context attention, and 2-bit/8-bit/4-bit quantization into a reconfigurable FPGA computing engine; pipelines select MM/VM modes for prefill/decode, leveraging mixed-precision DSP packing and sparse selectors. Compilation frameworks (ScaleHLS, HIDA) apply graph and loop IR transformations, memory partitioning, and HLS directives to optimize hardware pipelines, achieving up to 3,825× improvement over vanilla HLS on kernel throughput (Liang et al., 7 Apr 2025, Huang et al., 16 Jun 2024).

7. Energy Efficiency and Comparative Evaluation

Benchmark surveys reveal platform-dependent trade-offs. While ASIC and PIM arrays deliver unparalleled energy efficiency (up to 10,000× improvement), FPGAs balance flexibility and moderate throughput, and GPU software optimizations remain the most deployable for short-term scaling. Best practices include maximizing batch size until throughput plateaus, pairing quantization with operator fusion and parallel decoding, and leveraging hardware-aware scheduling for resource-constrained scenarios (Li et al., 6 Oct 2024, Koilia et al., 5 Sep 2024, Chitty-Venkata et al., 31 Oct 2024).

8. Emerging Directions and Open Challenges

Trends indicate growing multimodality (e.g., vision–LLMs), runtime programmable inference (“inference-time compute”), advanced near-data processing, and increased deployment of hybrid architectures combining GPU tensor cores and PIM/ASIC memory-centric accelerators. Open challenges persist in dynamic sparsity load balancing, compiler co-optimization, and full-stack co-design for real-time edge deployment (Li et al., 6 Oct 2024, Huang et al., 16 Jun 2024).

In summary, hardware acceleration of LLMs is a multidimensional discipline uniting algorithmic innovations (e.g., quantization, speculative/parallel decoding, tensor decompositions), architectural advances (FPGA systolic arrays, GPU tensor core scheduling, DIMM-PIM offload), memory-optimized designs, and comprehensive software–hardware co-design. It delivers dramatic improvements in throughput, latency, and energy efficiency, scales to contexts spanning thousands of tokens, and underpins the next generation of scalable, realtime AI inference (Chen et al., 28 May 2024, Salmani et al., 24 Feb 2025, Huang et al., 31 Jan 2025, Ahadi et al., 26 Sep 2025, Ma et al., 26 Sep 2024, Kachris, 18 Jan 2024, Huang et al., 16 Jun 2024, Malekar et al., 31 Mar 2025, Huang et al., 31 Jul 2024, Bournias et al., 8 Nov 2024, Ma et al., 26 Aug 2025, Wang et al., 12 Jun 2025, Chen et al., 1 Sep 2024, Liang et al., 7 Apr 2025, Liu et al., 24 Apr 2025, Chitty-Venkata et al., 31 Oct 2024, Koilia et al., 5 Sep 2024, Li et al., 6 Oct 2024).