GPU-Accelerated INT8 Quantization for KV Cache Compression in Large Language Models

Abstract: The key-value (KV) cache in LLMs presents a significant memory bottleneck during inference, growing linearly with sequence length and often exceeding the memory footprint of model weights themselves. We implement and evaluate GPU-accelerated INT8 quantization for KV cache compression, achieving 4$\times$ memory reduction with minimal accuracy degradation. We develop four CUDA kernel variants -- naive, tiled, coarsened, and vectorized -- and benchmark them across realistic workload sizes up to 1 billion elements. Our vectorized kernel achieves up to 1,694$\times$ speedup over CPU baselines while maintaining reconstruction error below 0.004 and attention score error below 0.1 even for 8K-dimensional heads. These results demonstrate that INT8 quantization provides a practical approach for reducing memory pressure in LLM inference with negligible computational overhead (6--58ms) and minimal impact on downstream model behavior

• The paper presents per-channel INT8 quantization that reduces KV cache memory by 4× with minimal accuracy loss.
• It evaluates four CUDA kernel variants, with the vectorized kernel achieving up to 1,694× speedup over CPU processing.
• The study demonstrates that memory-bound transformer cache operations benefit from bandwidth-centric optimizations for enhanced LLM inference.

GPU-Accelerated INT8 Quantization for KV Cache Compression in LLMs

Introduction and Motivation

Efficient deployment of transformer-based LLMs remains constrained by memory bottlenecks primarily attributable to the key-value (KV) cache that grows linearly with sequence length during autoregressive inference. Real-world applications demanding long-context windows (e.g., 128k tokens) often see KV cache memory surpassing model weights, impeding throughput, context length, and incurring substantial GPU serving costs. This paper presents a systematic implementation and evaluation of per-channel INT8 quantization for KV cache compression, exploring four CUDA kernel variants—naive, tiled, coarsened, and vectorized—across matrix sizes approaching billion-element scales. Quantization, specifically INT8, offers 4-fold memory reduction; the challenge lies in maintaining runtime efficiency and negligible accuracy degradation under the stringent requirements of production LLM serving.

Quantization Approach and Implementation

The method utilizes per-channel linear INT8 quantization: each column of the KV cache matrix is assigned an independent scale factor, optimizing precision preservation versus global scaling. The scale factor for dimension $d$ is computed as $s_d = \frac{\max_t |K[t, d]|}{127}$, allowing maximal dynamic range utilization for each head dimension. Quantization maps FP32 $K$ values to INT8 via $$K_{\text{int8}}[t, d] = \mathrm{round}(K[t, d]/s_d)$$, with clamping in $[-127, 127]$; dequantization recovers $\hat{K}[t, d] = K_{\text{int8}}[t, d] \times s_d$, introducing quantization error strictly bounded by $s/2$. Data structures are minimal and pragmatic for both CPU and GPU implementations, and kernels are engineered to exploit CUDA memory hierarchy and parallelism.

Four kernel variants are meticulously constructed:

• Naive: Thread-per-element computation with coalesced global memory access.
• Tiled: Shared memory is leveraged to stage scales, reducing redundant memory loads.
• Coarsened: Thread coarsening enables multiple element processing per thread, targeting instruction-level parallelism.
• Vectorized: Vector loads and stores (e.g., float4, char4) maximize memory bandwidth utilization.

All variants are validated against a CPU reference implementation for correctness across extensive unit tests.

Performance Evaluation

Significant empirical benchmarks are performed on realistic hardware (NVIDIA T4, 16GB), spanning configurations up to 131k tokens × 8k dimensions (over 1B elements). The vectorized kernel emerges as the optimal strategy, achieving up to 1,694× speedup over CPU, particularly as workload size grows and launch overheads amortize. Figure 1 illustrates the comparative speedup across test configurations, with vectorization clearly outperforming all other variants.

Figure 1: GPU kernel speedup comparison across all test configurations.

Execution time comparison (CPU vs GPU) manifests a three-order-of-magnitude gap; even for the largest matrices, the GPU kernels consistently execute in under 50ms, in stark contrast to a 79s CPU baseline.

Figure 2: Execution Time: CPU vs GPU.

Critically, for realistic LLM workloads, total quantization overhead per operation is constrained to 6–58ms, rendering the cost negligible relative to end-to-end inference latencies.

Figure 3: GPU Performance on realistic LLM loads.

Memory footprint reduction is precisely 4× over FP32 baselines, directly enabling longer contexts and larger batch sizes on commonly available GPU hardware.

Numerical Accuracy and Error Propagation

Reconstruction error from quantization follows theoretical bounds, with maximum absolute error constant at 0.00394 regardless of configuration—a result stemming from bounded quantization steps over the $[-1, 1]$ interval with 255 levels. L2 error scales appropriately with matrix size. Figure 4 presents L2 reconstruction and attention score error as functions of matrix size and head dimension.

Figure 4: L2 Reconstruction and Attention Score Error vs Matrix Size.

Attention score error, directly relevant for downstream transformer computations, remains below 0.1 even at D=8192. This demonstrates that per-channel INT8 quantization does not meaningfully alter attention distributions, particularly given the dynamic range expansion induced by softmax in typical transformer attention pipelines.

Kernel Behavior and Optimization Insights

A nuanced analysis of kernel-level performance reveals that naive coalesced memory access saturates memory bandwidth for this workload, rendering shared-memory tiling ineffective due to lack of data reuse and overhead from explicit synchronization. Thread coarsening yields modest gains by decreasing launch and instruction overhead, but the optimization plateau for memory-bound workloads is reached quickly. In contrast, vectorized kernels—by virtue of multi-element memory transactions—substantially reduce aggregate memory operations and maximize throughput. Figure 5 delineates how speedup scales with problem size, confirming the dominance of vectorization beyond a critical workload threshold.

Figure 5: Speed Scale Ups vs Problem Size.

These results clarify that, for KV cache quantization, further optimization is bandwidth-centric rather than compute-centric; vectorization achieves bandwidth saturation, while other CUDA optimizations provide diminishing return unless the underlying data format changes.

Practical and Theoretical Implications

The practical consequence of this work is the enabling of long-context autoregressive inference on mainstream GPUs via KV cache quantization, with negligible computational overhead and minuscule impact on model behavior. Batch sizes and sequence lengths previously infeasible on available hardware are now attainable, substantially improving LLM throughput and cost efficiency in deployed environments. Theoretically, the paper reinforces that transformer cache operations are fundamentally memory-bound, advocating for further GPU kernel innovation primarily in data layout and precision representation rather than memory hierarchy exploitation.

Potential future directions include even lower bit-width quantization (INT4, INT2) for extreme compression, mixed-precision strategies tailored to layer criticality, and dynamic/adaptive quantization in response to runtime input distributions. Integration with modern inference engines (e.g., vLLM, TensorRT-LLM) and rigorous end-to-end evaluation on standard benchmarks (perplexity, QA, generation) will further determine the operational utility of these kernels. GPU-native formats such as FP8 warrant exploration for alternative memory-accuracy tradeoffs.

Conclusion

This work delivers a robust and systematic study of GPU-accelerated per-channel INT8 quantization for KV cache compression in transformer-based LLM inference. The vectorized CUDA kernel achieves up to 1,694× speedup versus CPU reference, sustaining sub-0.1 attention score error at extreme matrix scale, and rendering quantization overhead negligible for current LLM workloads. The research provides compelling evidence for the practicality of memory-saving quantization strategies, establishes best practices for kernel optimization, and lays critical groundwork for forthcoming advances in bandwidth-bound transformer inference architectures.