GPU-Accelerated INT8 Quantization

Updated 11 January 2026

GPU-accelerated INT8 quantization is a technique that converts floating-point neural network data into 8-bit integers using per-channel scaling and optimized CUDA kernels.
It achieves significant memory reduction and throughput improvements by leveraging vectorized GPU kernels, with speedups up to 1,694× and a 4× reduction in memory.
This approach maintains model fidelity with negligible accuracy loss, making it highly effective for large models like LLMs and CNNs in production deployments.

GPU-accelerated INT8 quantization is the process of mapping floating-point data in neural networks to 8-bit integer representations and executing quantize, dequantize, and arithmetic operations efficiently using modern GPU hardware. This technique is critical for dramatically reducing memory usage and compute intensity in large models, particularly in deployment scenarios such as LLM inference, convolutional neural networks, and other memory-bound workloads. State-of-the-art methods employ highly optimized CUDA kernels and use per-channel scale factors, ensuring minimal accuracy loss with massive performance and memory benefits (Taneja et al., 8 Jan 2026).

1. Mathematical Foundations and Quantization Error

The foundation of GPU-accelerated INT8 quantization involves linearly mapping each floating-point value $f$ to a signed 8-bit integer $q$ using a scale factor tailored for each channel (dimension). For a key matrix $K \in \mathbb{R}^{T \times D}$ in LLMs, per-channel scaling is defined as:

$s_d = \frac{\max_{0 \leq t < T} |K[t, d]|}{127}, \quad d = 0, \dots, D-1$

Each element is quantized by:

$q[t, d] = \mathrm{clamp}\bigl( \mathrm{round}[K[t, d] / s_d], -127, 127 \bigr)$

Dequantization reconstructs the approximate float value:

$\hat{K}[t, d] = q[t, d] \cdot s_d$

The per-element quantization error is bounded by $s_d/2$ , and in practice, the maximum quantization error remains around $0.00394$, as predicted by $1/(2 \cdot 127)$ (Taneja et al., 8 Jan 2026).

Error propagation in downstream computations (e.g., attention logit computation in LLMs) is measured via metrics like the mean absolute difference in attention scores, which remains below $0.1$ in practical KV-cache quantization with $D=8192$ , implying negligible impact on downstream model performance.

2. GPU Kernel Design and Optimization Variants

Efficient INT8 quantization on GPUs exploits memory bandwidth and vectorized compute by using custom CUDA kernels:

Naive Kernel: Each thread processes a single matrix element, fetching the scale and applying quantize logic directly. Achieves full coalescing but with redundant loads (Taneja et al., 8 Jan 2026).
Tiled Kernel: Warps collaboratively prefetch per-block (e.g., channel) scale factors into shared memory, reducing global memory bandwidth needs at the cost of synchronization.
Coarsened Kernel: Each thread processes an entire channel, fetching scale once and amortizing overhead, which is effective when $T \gg D$ .
Vectorized Kernel: Threads process groups of 4 elements (using float4/char4 loads and stores), optimizing coalesced access and halving the number of memory transactions and boosting observed throughput to ~150 GB/s on NVIDIA T4 GPUs.

All kernels write to row-major stored matrices for maximum memory bandwidth, and the vectorized approach requires the head dimension $D$ to be divisible by 4 or includes special handling for tails (Taneja et al., 8 Jan 2026).

3. Performance Benchmarking and Workload Characterization

Multiple workloads were profiled:

Workload Size	Tokens $T$	Dim $D$	Elements ( $T \times D$ )
Realistic V.L.	131,072	8192	~1.07B
Realistic L	131,072	4096	~536M
Realistic S	131,072	1024	~134M
Large	65,536	256	~16.8M

The vectorized GPU kernel quantizes or dequantizes 1B elements in under 50 ms, achieving up to 1,694× speedup against CPU implementations. All GPU kernels complete in 6–58 ms for workloads up to 1B elements, a negligible overhead compared to other inference costs in LLMs (Taneja et al., 8 Jan 2026).

Memory reduction is a direct 4×, as INT8 storage is 1 byte/element compared to FP32's 4 bytes/element. The end-to-end quantization process can be overlapped or fused with existing attention kernels, eliminating extra data movement.

4. Accuracy, Error Analysis, and Model Fidelity

Reconstruction error is tightly bounded:

Maximum Absolute Error: $\sim 0.00394$ per element across all measured cases.
Attention Score Error: At $D=8192$ , the mean attention logit deviation remains $\sim 0.095$ ; this is orders of magnitude smaller than the scale of attention logits and does not affect the output probabilities after softmax for practical purposes.
Downstream Impact: No observable drop in end-to-end perplexity or generation quality (citing prior literature for similar schemes) (Taneja et al., 8 Jan 2026).

This demonstrates that quantized KV-caches or activations—in both inference and training scenarios—do not meaningfully degrade model accuracy when quantizer parameters are carefully chosen.

5. Integration into Inference Pipelines

INT8 quantization is integrated into LLM inference by converting new key-value pairs to INT8 upon generation and appending quantized blocks to the KV cache. During attention computation, only the required KV rows are dequantized, minimizing memory and compute cost.

A hybrid path allows recent tokens to remain in FP32 (for maximum precision on the current context) and older tokens to be quantized. Design parameters such as the window size of the FP32 segment can be adjusted per-application (Taneja et al., 8 Jan 2026).

Recommendations for deployment:

Use per-channel scaling to minimize error.
Favor vectorized kernels for throughput, padding dimensions as needed.
Quantize the entire cache only when memory is most constrained.
Validate with a development set by measuring full-model perplexity or metric drift.

6. Comparative Context, Trade-Offs, and Practical Guidelines

The key trade-offs are pragmatically summarized:

Trade-off	Benefit	Cost/Consideration
Memory savings	4× smaller KV cache	~$0.004$ max abs error
Throughput	$<$ 60 ms for 1B elements	Negligible vs. attention computation
Accuracy	$<$ 0.1 attention error	No observed quality loss
Compute overhead	Vectorized: lowest	D must be padded to multiple of 4

The approach generalizes to INT8 quantization in other neural architectures, as seen in fully integer CNNs (Zhao et al., 2020) and post-training quantization frameworks that tune per-channel scales and fusions for GPU deployment (Jiang et al., 2021).

Practically:

Always apply per-channel scales.
Favor kernel layouts and thread-block shapes that maximize vector load/store efficiency.
Apply kernel fusion to minimize global memory transactions.
In post-training quantization, alternate-optimizing weight and activation scales is effective (Wu et al., 2020).
Use data-driven calibration for high accuracy when integrating into production pipelines.

7. Broader Impact and Applicability

GPU-accelerated INT8 quantization is now widely used not only for memory savings in LLM inference but also for accelerating CNNs, speech recognition models, and ensemble methods handling large pairwise proximity matrices (via low-rank INT8 factorization). Scalability to billions of elements, <0.1% error rates, 1–2 orders of magnitude throughput increase, and compatibility with industry-standard GPU tensor core instructions render INT8 quantization a central tool in modern model deployment (Kuchar, 23 Nov 2025, Kurtic et al., 2024).

Emerging trends indicate further generalization to lower-bit formats (INT4/NF4), adaptive calibration schemes, dynamic kernel fusion for complex model architectures, and hardware specialization for increasing the efficiency of INT8 operations.

References: (Taneja et al., 8 Jan 2026, Zhao et al., 2020, Jiang et al., 2021, Kuchar, 23 Nov 2025, Chen et al., 2024, Wu et al., 2020, Kurtic et al., 2024)