---
title: 'AxLLM: Accelerator for Quantized LLMs'
url: https://www.emergentmind.com/topics/axllm
type: topic
---

# AxLLM: Accelerator for Quantized LLMs

AxLLM is a hardware accelerator architecture for quantized large language models that treats quantization not only as a compression mechanism but also as a source of parameter locality. Its central premise is that quantized weights repeat frequently enough to permit online reuse of multiplication results, thereby reducing redundant operations in the matrix multiplications that dominate LLM inference. The architecture couples this reuse mechanism with dual datapaths that support both base models and LoRA fine-tuned models, while requiring no parameter alteration, retraining, or offline preprocessing [2509.22512].

## 1. Design premise and placement in the accelerator landscape

AxLLM is defined around a specific observation: quantization increases parameter locality, creating opportunities for computation reuse. On that basis, the design targets quantized models and introduces a redundancy elimination technique that caches and reuses multiplication results for repeated weight values. This shifts part of the acceleration problem away from further reducing arithmetic precision alone and toward exploiting value repetition induced by quantization [2509.22512].

Within the broader LLM acceleration literature, this places AxLLM alongside, but conceptually distinct from, other quantization-centered systems. AccLLM combines pruning, $\Lambda$-shaped attention, and a W2A8KV4 quantization scheme with FPGA-based co-design for long-context inference on edge devices [2505.03745]. EXAQ instead targets the softmax bottleneck through exponent-aware sub-4-bit quantization of the softmax input, accelerating both $e^x$ and $\sum e^x$ [2410.03185]. AxLLM addresses a different locus of inefficiency: repeated multiplications arising from repeated quantized weight values [2509.22512].

## 2. Architectural organization

The high-level structure is lane-based. AxLLM consists of $L$ parallel lanes, with each lane assigned one element of the input vector $\mathbf{x}$ and responsible for multiplying it by a corresponding row or partition of the weight matrix $\mathbf{W}$. The target operation is the usual matrix multiplication

$$
\mathbf{y} = \mathbf{x} \cdot \mathbf{W}.
$$

Each lane contains a Reuse Cache, a multiply path, a reuse path, and an output buffer. Lane outputs are accumulated through an adder tree to form the final result. The design also slices input, output, and cache buffers into partitions for fine-grained parallel execution, with flow-control mechanisms for collision handling [2509.22512].

The Reuse Cache is quantization-aligned. For the current input element, it stores multiplication results corresponding to unique quantized weight values encountered on that lane. The multiply path is used on the first occurrence of a value; the reuse path bypasses the multiplier on subsequent occurrences and reads the stored result instead. For 8-bit weights, the cache has $2^q$ entries, i.e., $256$ entries, and valid bits indicate whether a result has already been computed for the current input [2509.22512].

## 3. Quantization-driven reuse and redundancy elimination

The core redundancy elimination method is value-centric. For each input element $x[i]$, AxLLM examines the corresponding quantized weight row $W_{i,:}$ and identifies the set of unique values

$$
\mathcal{U}_i = \{W_{i,j}\mid j \in 1..n\}.
$$

Instead of computing $x[i] \times W_{i,j}$ for every column position $j$, AxLLM computes one product per unique value $u \in \mathcal{U}_i$,

$$
r_u = x[i] \times u,
$$

stores $r_u$ in the Reuse Cache, and then reuses that result for every position where $W_{i,j} = u$:

$$
y_j \mathrel{+}= r_u.
$$

Operationally, for each position in a row, the controller checks whether the corresponding cache entry is valid. If it is invalid, the multiply path computes and stores the result; if it is valid, the reuse path fetches the stored value and writes it to the output buffer. This replaces a large fraction of multiplications with cache lookups and assignments [2509.22512].

A notable property of the mechanism is that it is online and parameter-free. It does not require pre-processing or lookup-table setup, and it is stateless across inputs because the cache is reset for each new input. The paper therefore characterizes it as post-training and as requiring no retraining, reparameterization, or model format conversion [2509.22512].

## 4. Dual pipelines and support for LoRA-adapted models

AxLLM implements two concurrent datapaths per lane. The compute path is invoked on the first occurrence of a unique weight value and performs the multiplication, writing the result into the Reuse Cache. The reuse path is invoked on later occurrences of that same value and fetches the cached result directly. These paths overlap in time: while initial results are being generated, subsequent repeated values can already be serviced through the reuse path. The architecture stalls only in the rare case where a reused weight arrives before its first multiplication result is available, which the paper reports occurs in fewer than $2\%$ of cases [2509.22512].

The design also supports LoRA fine-tuned models. For a base weight matrix $W$, LoRA introduces low-rank adapter matrices $A$ and $B$, yielding forward computation of the form

$$
xW + xAB.
$$

AxLLM exploits the fact that $xW$ and $xA$ share the same input $x$. When quantization is applied to the adapters as well, the architecture treats $W$ and $A$ as a combined matrix during per-input processing and shares the Reuse Cache between them for matching values. This allows reuse not only within the base matrix but also between the base matrix and the adapter computation, again without parameter modification, retraining, or precomputation [2509.22512].

## 5. Empirical characteristics

The reported empirical results emphasize reuse rate, throughput, energy, and area trade-offs. For large matrices, AxLLM avoids at least $87\%$ of weight multiplications through reuse; under constrained buffer sizes, that figure is about $70\%$. For LoRA adapters, $90\%$ of adapter matrix elements reuse a result found in the base weight matrix, which eliminates most of the additional computation introduced by adaptation [2509.22512].

At the accelerator level, the paper reports up to $90\%$ reduction in multiplication operations, up to $1.7\times$ execution speedup over a baseline accelerator that uses multipliers only, and $28\%$ lower energy consumption due to multiplier bypasses by reuse-cache lookups. For LoRA-adapted models, the speedup is reported as up to $1.82\times$ on BERT IMDb. The paper also notes that reuse rates and speedups increase with model size, since larger matrices provide more opportunities for repeated quantized values [2509.22512].

These gains are not presented as cost-free. The implementation incurs about $23\%$ greater area than the baseline, attributed to the Reuse Cache and associated control structures. The paper frames this as a tradeoff between hardware area and the throughput and efficiency obtained from reuse [2509.22512].

## 6. Scope, interpretation, and common misunderstandings

AxLLM is not a general long-context serving framework and not a generic software inference system. Its contribution is a specialized hardware architecture for quantized models, centered on online result reuse at the level of repeated weight values. This differentiates it from systems such as AccLLM, which combine pruning, $\Lambda$-shaped attention, and quantization with FPGA co-design for efficient long-context inference on edge devices [2505.03745], and from xLLM, which addresses enterprise-grade serving through a decoupled service-engine architecture, elastic scheduling, PD and EPD disaggregation, global KV-cache management, and execution-pipeline optimizations [2510.14686].

A frequent misunderstanding would be to read AxLLM as a method for caching results across requests or across prompts. The paper does not describe such a mechanism. Its cache is reset for each new input, and the reuse opportunity arises within the current computation over quantized rows. This suggests that the principal benefit depends on the repetition structure induced by quantization rather than on cross-request memoization. A further plausible implication is that AxLLM is most advantageous when quantized rows contain many repeated values and when the hardware budget can absorb the cache-area overhead [2509.22512].

In that sense, AxLLM is best understood as an architectural reinterpretation of quantization. Rather than using low bit-width solely to shrink storage and arithmetic cost, it leverages the reduced value alphabet itself as a source of reuse. That reframing is the paper’s defining contribution, and it underlies its reported reductions in redundant computation, energy use, and execution time for both base and LoRA-adapted LLM inference [2509.22512].

Source: https://www.emergentmind.com/topics/axllm