Day 0 LLM Inference Support: Hierarchical Quantization and Memory Access Optimization Practices

I. Background: New Architectures Make Day 0 Adaptation a Challenge

The iteration pace of large language models (LLMs) is accelerating, and attention mechanisms are continuously evolving. To support ultra-long contexts and reduce inference costs, new-generation models commonly introduce an “index-filter-sparse computation” architecture alongside standard attention: a lightweight indexer scores candidate tokens for each query position, and only a few highly relevant tokens participate in the full attention computation. While this structure drastically reduces computational overhead, it brings three major challenges to the inference side:

• Novel Operators: Operators like index scoring and Top-K filtering are not standard components in traditional inference graphs, requiring new implementations or restructuring of existing pipelines.
• New Data Flows: Beyond the KV cache, intermediate data such as index Q/K and index scores also consume GPU memory and bandwidth.
• Increased Memory Pressure: In long-context scenarios, both the KV cache and index data grow linearly with sequence length. Memory access rapidly replaces computation as the primary bottleneck.

“Day 0 support” means that on the day a new model is released, the inference side can complete adaptation and provide external services with near-optimal performance. Achieving this requires far more than just “getting the operators to run”; it demands a mature performance optimization plan. Quantization offers the most direct benefits, but success depends on how you quantize, not whether you quantize.

II. Why “One-Size-Fits-All” Quantization Fails

Applying indiscriminate low-precision compression across the entire pipeline is the most intuitive approach, but it is also the most error-prone. Different stages of the pipeline have vastly different sensitivities to numerical errors:

1. Projection Computations (Q/K/V and Output Projections): Located at the entrance of the attention mechanism, errors here propagate downstream to all subsequent stages. Since these are executed by high-throughput matrix multiplication units, BF16 is already sufficient to maximize hardware compute capacity. Compression yields limited benefits but introduces significant risks.
2. Index Scoring: This is the stage in sparse structures where “errors are amplified.” Index scores are solely used for ranking and selecting tokens. If an error causes a token that should have been selected to be filtered out, subsequent attention computations will completely miss that information—an irreversible loss. The absolute values of the scores do not matter; the relative order does. Therefore, its tolerance for quantization noise is exceptionally low.
3. KV Cache: This is the largest component, heavily consuming memory and bandwidth. However, because attention output is a weighted sum of a large number of Values, minor perturbations in individual elements are diluted by the averaging effect of the summation, making it relatively tolerant to errors.

This leads to a clear design principle: Hierarchical precision based on sensitivity—preserve precision where needed, compress aggressively where possible. Furthermore, the more aggressive the compression, the finer the quantization granularity required to ensure accuracy.

III. Detailed Hierarchical Quantization Strategy

3.1 Projection Computations: Retain BF16

In a sparse attention framework, whether in the indexing path or the main attention path, the inputs originate from shared projection computations. Retaining projections in BF16 is based on three reasons:

1. Controlling Error at the Source: Projection is the first gateway for quantization errors to enter the system. Maintaining high precision provides “clean” inputs for all downstream quantized stages, preventing error accumulation.
2. Limited Benefits: Projection is a typical compute-bound operator. The hardware throughput of BF16 matrix multiplication is already high, and switching to lower precision saves a negligible proportion of memory access.
3. Reducing Day 0 Risk: Eliminating the need to configure scaling factors and prepare calibration data for projections reduces adaptation complexity and the surface area for errors.

3.2 Indexing Path: MXFP8 Per-Group Dynamic Quantization, Group Size = 32

The indexer is responsible for calculating relevance scores for candidate tokens for each query position and completing the filtering. Its index Q/K and scoring pipeline adopt a fully quantized strategy, uniformly using the MXFP8 format with per-group dynamic quantization: every 32 elements share a single scaling factor. Key design points include:

• Why MXFP8: MXFP8 is a block-scaled floating-point format where scaling factors typically use exponent encoding (powers of 2). This has two advantages: scaling is achieved via exponent adjustment, avoiding extra multiplication overhead, and block-level scaling naturally aligns with hardware vectorized execution units.
• Why Dynamic Quantization: Index Q/K are activations, and their distributions fluctuate noticeably with input content, with outliers occurring frequently. Static calibration scaling factors can be “hijacked” by extreme values, degrading overall precision. Dynamic quantization calculates scaling factors per group at runtime, tightly tracking the actual dynamic range of the current data.
• Why Group Size = 32: Quantization error is generally positively correlated with the intra-group dynamic range. Smaller groups yield higher precision, but the storage and computational overhead of scaling factors increase. 32 is a widely industry-validated balance point: the scaling factor overhead is only about 1/32, and 32 FP8 elements equal 32 bytes, facilitating memory alignment and vectorized memory access.

In practice, the storage and transfer overhead of index Q/K and related data are directly halved, while the relative order of index scores remains largely unaffected, maintaining the stability of Top-K filtering.

3.3 KV Cache: FP8 Per-Token-Head-Tile Quantization, Tile Size = 128

The KV cache is the largest data structure in long-context inference and the primary battlefield for “maximizing compression benefits.” Here, FP8 storage is used with a quantization granularity of per-token-head-tile: within each Token and each Head, data is quantized in blocks of 128 elements, with each block sharing a scaling factor. The logic behind this granularity:

• Why Not Per-Tensor or Per-Channel: The numerical distributions of different Tokens and Heads vary significantly. Coarse-grained scaling will be elevated by occasional outliers, reducing the effective precision of most elements. Refining down to the “Token-Head-Tile” level gives each small block its dedicated scaling factor, isolating outlier impacts within the block.
• Why Tile Size = 128: 128 FP8 elements equate to exactly 128 bytes, compatible with cache lines and vectorized memory access granularities of mainstream hardware. A single memory transaction can retrieve the entire block. The scaling factor overhead is only 1/128, making metadata pressure negligible. When dequantization is fused with attention computation, the block data exhibits excellent locality; a scaling factor is loaded once to cover the entire block’s computation.
• When to Calculate Scaling Factors: A dynamic approach is used. Quantization and scaling factor calculation are completed at the moment the KV is written to the cache. From then on, the entire attention read path consumes FP8 data directly.

Effectively, the KV cache footprint is halved: under the same memory budget, the supported context length or concurrent batch size approximately doubles. Since attention is a classic memory-bound operator, halving the bytes read for KV yields an almost equivalent improvement in step latency.

IV. Benefit Estimation: A Clear Accounting

• Storage Compression Ratio (2:1): FP8 takes 1 byte per element, BF16 takes 2. Any data switched to FP8 storage has its footprint directly halved.
• Capacity Benefits: KV cache usage grows linearly with “context length × concurrency.” Halving the footprint means the scale of long-context requests servable under the same memory budget roughly doubles.
• Latency Benefits: The latency of memory-bound operators is roughly proportional to the volume of data transferred. Combined with operator fusion to reduce extra read/write roundtrips, this typically yields a significant latency reduction (actual magnitude depends on the overlap between computation and memory access).
• Indexing Pipeline Benefits: Index Q/K and score data are read and written frequently during the filtering phase. The benefits of halving transfer volume are realized repeatedly.

V. Engineering Implementation Details

Beyond strategy, implementation quality determines success or failure. Key details include:

1. Fusing Quantization with Preceding Operators: If scaling factor calculation and precision conversion are independent operators, they introduce extra memory read/write roundtrips. In practice, quantization is fused into the tail of preceding operators like positional encoding and scaling, achieving “quantize upon computation.”
2. Scaling Factor Layout and Loading: Although per-group/per-tile scaling factors are small in volume, they are accessed frequently. They must be laid out logically with the main data to ensure they can be loaded into registers vectorizedly during the main matrix multiplication loop.
3. Fusing Dequantization into Matrix Multiplication: After loading FP8 data in the main computation, dequantization is completed in registers before participating in multiply-accumulate (MAC). Alternatively, hardware instructions supporting block scaling can directly consume the scaling factors, avoiding explicit intermediate dequantization tensors.
4. Numerical Safety: Scaling factor calculation must handle all-zero groups (preventing division by zero). Clipping must cover FP8 representation boundaries to prevent overflow from generating Inf/NaN values that pollute subsequent computations.

Below is the pseudo-code illustrating per-group dynamic quantization:

# Example: Per-group dynamic quantization for Index Q/K (group_size = 32)
FP8_MAX = 448.0  # FP8(e4m3) representation upper limit, illustrative value
def quant_per_group(x, group_size=32):
    # x: [num_tokens, num_heads, head_dim]
    g = x.reshape(-1, group_size)                 # Split into groups of 32 elements
    amax = g.abs().amax(dim=-1, keepdim=True)     # Max absolute value within the group
    scale = torch.where(amax > 0, amax / FP8_MAX,
                        torch.ones_like(amax))    # Prevent division by zero
    q = (g / scale).clamp(-FP8_MAX, FP8_MAX)      # Normalize and clip
    return q.to(torch.float8_e4m3fn).reshape(x.shape), scale

The block quantization logic for the KV cache is structurally similar, except the grouping dimension becomes “Token × Head × 128-element block.” For precision validation, it is recommended to establish a three-level evaluation:

1. General perplexity and downstream task evaluation to ensure overall capabilities do not regress.
2. Metrics specific to sparse structures, such as index Top-K hit rate, directly measuring “whether the tokens that should be selected are actually selected.”
3. Extreme long-context scenario testing (e.g., needle-in-a-haystack tasks) to cover quantization performance under the most adverse distributions.

VI. From Point Tricks to a Day 0 Methodology

Distilling the practices above, we can outline a reusable Day 0 adaptation process:

1. Structure Analysis and Operator Inventory: Identify non-standard operators and new data flows (like index data) introduced by the new model. Clarify the sources of memory and bandwidth increments.
2. Precision Sensitivity Grading: Grade each stage based on “whether errors are reversible” and “whether they are amplified downstream.”
3. Quantization Strategy Configuration: Retain high precision for compute-bound and error-sensitive stages. Use fine-grained dynamic quantization for large activations with fluctuating distributions. Use relatively coarser but hardware-aligned granularity for the largest cache data where errors are diluted by averaging effects.
4. End-to-End Evaluation and Tuning: Use precision metrics as a baseline and jointly tune for memory access throughput and end-to-end latency.

This methodology does not depend on any specific framework or chip; its essence is a systematic treatment of the “precision vs. overhead” tradeoff. Whether building an in-house inference cluster or using cloud-based inference platforms like Baidu AI Cloud, fine-grained quantization and memory access optimization remain the universal path to reducing costs and increasing efficiency for long-context inference.

VII. Conclusion

The essence of hierarchical quantization is acknowledging that “no two stages in the pipeline have the same precision requirements.” Anchoring projections in BF16, entrusting the indexing path to fine-grained MXFP8, and assigning the massive KV cache to block-level FP8 creates a solid balance between precision and overhead. This combination turns the slogan “high-performance availability on day one of a new model release” into a repeatable engineering workflow. For inference system builders, the truly valuable takeaway is not a specific parameter, but this complete methodology of “sensitivity grading, granularity selection, fused implementation, and end-to-end validation.”