Dynamic INT8 Quantization Techniques

• Dynamic INT8 quantization is a technique that converts floating-point tensors into efficient 8-bit representations at runtime while maintaining near-original accuracy.
• It dynamically adjusts scale parameters per token, block, or channel using adaptive methods like clipping and fallback to handle outliers.
• The approach leverages hardware-optimized GEMM kernels to significantly enhance inference and training speed in deep neural networks.

Dynamic Int8 quantization is a class of techniques specializing in the conversion of floating-point tensors—weights, activations, or gradients—into a lower-bit integer representation, most commonly 8 bits (INT8), during inference or training of deep neural networks. These methods dynamically adapt quantization scale parameters at runtime, per token, per block, or per channel, achieving substantial reductions in computational cost and memory footprint while preserving near-original model accuracy. The versatility of dynamic INT8 quantization extends across convolutional, transformer, and attention-based architectures, often fusing quantization into hardware-efficient GEMM kernels and exploiting intrinsic distribution statistics for robust scale selection.

1. Fundamental Principles of Dynamic INT8 Quantization

Dynamic INT8 quantization leverages runtime computation of quantization scale factors, in contrast to static or post-calibration methods that fix scales a priori. The essential operation takes a real-valued tensor $T \in \mathbb{R}^{\cdots}$ and maps its elements to integer range $[-127,127]$ via

$$T_{\rm int8} = \text{round}\left( \frac{T}{s} \right)$$

where scale $s$ is computed using the observed maximum absolute value—either globally, per token, per group, or per channel. This process can be generalized for activations, weights, or gradients, and may employ additional techniques such as dynamic clipping or fallback quantization for outlier handling (El-Kurdi et al., 2022, Yao et al., 2023, Zhang et al., 11 Mar 2025, Zhao et al., 2021).

Clipping strategies (e.g., Tukey IQR), distribution-adaptive scale selection, and fallback mechanisms further enhance robustness against activation or gradient outliers. Dynamic schemes are often fused into GEMM kernels for tensor operations, maximizing locality and throughput.

2. Token-level and Per-group Scale Calibration

Distinguishing features of dynamic INT8 quantization are token-level and per-group (block, channel, column) scale assignment. For example, INT-FlashAttention (Chen et al., 25 Sep 2024) implements symmetric quantization with:

• Query (Q) and Key (K) matrices: per-token scaling, $s_Q[i] = \max_{k} |Q_{fp}[i,k]| / 127$ and $s_K[j] = \max_{k} |K_{fp}[j,k]| / 127$
• Value (V) matrix: global (per-tensor) scaling.

ZeroQuant-HERO (Yao et al., 2023) employs token-wise scales for LayerNorm and memory-bounded ops, static calibrations for per-feature quantization, and per-column scales for weights. Distribution Adaptive INT8 Quantization (Zhao et al., 2021) vectorizes the quantization for CNN gradients along the channel dimension, assigning each channel its scale according to the statistical shape of its distribution.

In block-level fallback quantization (Zhang et al., 11 Mar 2025), activations are partitioned into blocks, with dynamic scale parameters for each block, and a fallback to INT16 for blocks containing extreme outliers.

3. Outlier Handling and Dynamic Clipping Strategies

Dynamic quantization methods must address the presence of outliers—elements whose magnitude would excessively inflate the quantization scale, reducing precision for the bulk of data. TM-IQR (El-Kurdi et al., 2022) targets activations in Transformer inference with robust Tukey IQR clipping, setting the scale to: $t = q3 + 1.5 \cdot (q3 - q1)$ where $q1$ and $q3$ are first and third quartiles of per-token maxima. This ensures that clipping is robust to heavy-tailed distributions and reduces quantization error without requiring calibration data.

ZeroQuant-HERO (Yao et al., 2023) employs optional 99.9-percentile clipping for outlier suppression. Distribution Adaptive INT8 Quantization (Zhao et al., 2021) uses a magnitude-aware error weighting,

$$E(s) = \int_{g_{min}}^{g_{max}} |g - \hat{g}| \cdot f(g) \cdot p(g) \, dg, \quad f(g) = e^{\alpha |g|}$$

and channels with complex (inverted-T) distributions apply an iterative clipping recursion based on previous quantization parameters.

Block-level fallback methods (Zhang et al., 11 Mar 2025) identify blocks with outlier activations by comparing absolute maxima to adaptive thresholds, quantizing such blocks with an additional residual to recover precision.

4. Hardware-Optimized Execution: Kernels and Memory Layout

Modern INT8 quantization frameworks exploit hardware primitives, notably CUDA DP4A and INT8 TensorCore instructions, to maximize computational throughput. INT-FlashAttention (Chen et al., 25 Sep 2024) replaces FP16 GEMMs with INT8×INT8→INT32 DP4A GEMMs, using tightly packed memory layouts:

• Tile partitioning for memory locality and SRAM bank maximization.
• Coalesced loads and high-occupancy scheduling that stream INT8 blocks into DP4A pipelines.
• On-chip quantization and dequantization fusing softmax normalization with quantized intermediate representations.

ZeroQuant-HERO (Yao et al., 2023) fuses scale computation into LayerNorm kernels and post-quant GEMM into Triton/FlashAttention kernels. Scales are pre-merged where possible to reduce overhead, and outputs remain INT8 until subsequent computation.

Dynamic Block-Level Fallback (Zhang et al., 11 Mar 2025) divides GEMM into block-wise quantized regions, with mixed-precision execution for outlier blocks, retaining full compatibility with INT8 TensorCore units without fallback to FP16/FP32 inside core loops.

5. Integration into Training and Inference Workflows

Dynamic INT8 quantization is employed both for post-training quantization and in forward/backward passes during training. INT-FlashAttention (Chen et al., 25 Sep 2024) operates as a fully INT8 quantized attention kernel, compatible with token-level post-training quantization and adaptable to INT4 or INT2 formats. No retraining or per-tile calibration is required after offline scale estimation.

ZeroQuant-HERO (Yao et al., 2023) integrates token-wise dynamic quantization into Transformer layers (embedding, LayerNorm, attention, MLP), supporting mixed-precision “switch modes” for sensitive submodules. The dynamic quantization flow tracks ranges per token or per feature and fuses these into kernel execution.

Fallback quantization (Zhang et al., 11 Mar 2025) integrates into training loops by quantizing activations and gradients per block. Blocks with outlier patterns trigger two-step fallback quantization (effective INT16) during forward passes; backward passes use stochastic INT8 rounding for gradient quantization.

Distribution Adaptive INT8 Quantization (Zhao et al., 2021) applies channel-wise vectorized quantization and magnitude-aware scale selection to CNN gradient computation throughout backward passes, retaining near-lossless accuracy.

6. Empirical Performance and Comparative Accuracy

INT-FlashAttention (Chen et al., 25 Sep 2024) demonstrates up to 72% faster end-to-end inference speed on A100/Ampere vs. FP16 baselines, with 82% lower quantization error vs. FP8 attention and 50% reduced activation memory footprint. For instance, on RTX4090 (sequence length 8k), inference time drops from 101.2 ms (FP16) to 28.5 ms (INT8). Mean relative error for full INT8 is 4.2% (Gaussian), 1.7% (Uniform).

ZeroQuant-HERO (Yao et al., 2023) achieves 1.8–2.3× speedup vs. FP16 for a full W8A8 pipeline, with GLUE accuracy loss below 0.5% for moderate quantization; full INT8 (mode M3) incurs a –3.1% drop, especially on CoLA.

Dynamic Block-Level Fallback (Zhang et al., 11 Mar 2025) achieves a 1.57× end-to-end training speedup on RTX4090, recovering BF16-level accuracy where static block quantization fails. For HellaSwag, Llama-3.1-8B is 91.0% FP32 vs. 91.3% proposed INT8 fallback.

Distribution Adaptive INT8 Quantization (Zhao et al., 2021) keeps ImageNet accuracy within ±0.1% of FP32 on ResNet/AlexNet/VGG/Inception, outperformed prior UI8 methods (up to –1.8% loss). Training speedup exceeds 200% over FP32 and ~18% vs. optimized FP16.

Zero-Shot Dynamic Quantization with TM-IQR (El-Kurdi et al., 2022) delivers <0.5% GLUE average drop, often recovering 80+% of accuracy gap versus naive static quantization, with only ~2% throughput overhead.

7. Extensions and Limitations

Dynamic INT8 quantization frameworks generalize to INT4, INT2, and mixed-precision (e.g., INT8/FP16, fallback INT16) via scale parameter adjustment and hardware-primitive mapping. Limitations include sensitivity to fallback block frequency and threshold tuning (Zhang et al., 11 Mar 2025), impact on highly sensitive modules (e.g., attention score matrices (Yao et al., 2023)), and the overhead of runtime scale computations, albeit marginal on current architectures.

A plausible implication is that future advances will hinge on adaptive error-based threshold control, hardware/software co-design for fused fallback kernels, and extension to additional architectures (non-Transformer, RNN, CNN, etc.) or quantized optimizers (e.g., 8-bit Adam).

Dynamic INT8 quantization epitomizes hardware-efficient, distribution-adaptive quantization strategies, instrumentally advancing the deployment and scalability of deep neural models for resource-constrained and high-throughput settings.