Tensor-Parallel Latent Attention (TPLA)

• TPLA is an advanced attention mechanism that compresses key-value caches into low-rank latent vectors and partitions them across devices to enable efficient tensor-parallel decoding.
• It employs orthogonal transformations during partitioning to maintain robust normalization and full representational capacity, reducing duplication inefficiencies found in previous methods.
• Empirical evaluations show TPLA delivers significant speedups and improved memory usage in LLMs, making it ideal for long-context, high-throughput inference applications.

Tensor-Parallel Latent Attention (TPLA) is an advanced attention mechanism for LLMs and transformer architectures that facilitates efficient inference under tensor parallelism. TPLA achieves this by compressing key-value (KV) caches into low-rank latent vectors and partitioning these representations—not just across heads, but also along the latent dimension—over multiple devices. This approach unlocks memory and bandwidth savings during decoding in multi-device deployments, while maintaining the strong representational capacity of every attention head. TPLA builds upon Multi-Head Latent Attention (MLA) and overcomes the duplication inefficiencies typical of MLA under tensor-parallel inference. Implementation strategies leverage orthogonal transforms for robust cross-shard partitioning and are compatible with high-performance attention kernels, enabling practical deployment in large-scale models.

1. Technical Foundations and Motivation

The principal challenge addressed by TPLA concerns KV cache scaling during tensor-parallel autoregressive inference in LLMs. In MLA—first introduced in DeepSeek-V2—the KV cache is compressed into a single latent vector, significantly reducing memory usage. However, during tensor parallelism (TP), this latent vector must be loaded fully on every device to allow each to compute its share of attention heads, nullifying MLA's memory advantage over grouped mechanisms like Grouped Query Attention (GQA).

TPLA partitions both the latent representation and the input (query) dimension over multiple devices. Each device receives a shard of both the latent vector and head input, computes attention locally, and then combines results via an AllReduce operation:

• Let latent vector $c^{KV} \in \mathbb{R}^{B \times L \times 4d_h}$ (batch, length, hidden dimension) be split as $c_0^{KV}$, $c_1^{KV}$, each with dimension $B \times L \times 2d_h$ for two devices.
• Query tensor $$Q \in \mathbb{R}^{B \times 1 \times h_q \times 4d_h}$$ (batch, 1, num heads, hidden dimension) is similarly partitioned as $Q_0$, $Q_1$.
• Local device computation: $$O_i = \mathrm{softmax}\left(\frac{Q_i \cdot (\mathrm{RMSNorm}(c_i^{KV}))^T}{\sqrt{d_h}}\right)$$.
• Final attention output is combined across devices: $O = \text{AllReduce}(O_0 + O_1)$.

Orthogonal transformations (Hadamard or PCA-based) are applied to the latent vector before partitioning, ensuring that local computations approximate the global softmax and normalization results, mitigating cross-shard interference.

2. Partitioning Strategy and Orthogonal Reparameterization

Maintaining the effectiveness of partitioned computations necessitates careful normalization and softmax calculations across shards:

• The RMSNorm operation $RMSNorm(1, c)$ is equivalently computed by $RMSNorm(1, cU)U^T$, with $U$ an orthogonal matrix. This ensures the L2 norm is preserved during cross-shard slicing.
• For softmax, orthogonal transformation maintains distributional properties so that the sum across local softmax outputs (up to scaling constants) closely matches the global computation.

Reparameterization of projection weights is achieved by adjusting them via orthogonal matrices. The Hadamard or PCA transforms are chosen to optimize for low cross-shard interference in both normalization and dot-product stages. Empirical analysis shows that using these transforms minimizes loss in representational fidelity and metric degradation when splitting across devices.

3. Comparison to MLA, GQA, and Related Compression Schemes

TPLA provides several key advantages over competing memory-efficient attention mechanisms:

• GQA splits the latent dimension such that each head only attends to a fraction of the full latent vector, reducing representational capacity. TPLA enables every head to leverage the full latent representation, partitioned at the tensor level without information loss.
• Direct conversion from MLA to GQA incurs substantial performance drops (e.g., worsened WikiText-2 perplexity), while TPLA conversion induces only minor degradation.
• TPLA is compatible with MLA-pretrained checkpoints, supporting drop-in conversion for efficient TP decoding without requiring model retraining.

TPLA's approach contrasts with Grouped Latent Attention (GLA), which sacrifices the richness of the per-head latent context in favor of reduced cache duplication.

4. Practical Implementation and Prefill–Decode Separation

TPLA is implemented with practical considerations:

• Prefilling (input encoding phase) is performed in full MLA mode to avoid potential recomputation errors.
• During decoding (autoregressive inference), TPLA partitions the latent and head dimensions per device, with local attention and aggregation by AllReduce.
• Orthogonal transform reparameterization ensures that partitioned RMSNorm and softmax computations correctly approximate global behavior.

TPLA integrates with next-generation attention kernels such as FlashAttention-3, providing compatibility with high-throughput hardware acceleration.

5. Performance, Empirical Results, and Applications

Model evaluations demonstrate notable improvements in speed and memory:

• In DeepSeek-V3 and Kimi-K2, TPLA reduces the per-device KV cache size, yielding 1.79x and 1.93x speedups, respectively, at 32k-token context length, compared to MLA baselines.
• Benchmarks on commonsense reasoning and LongBench datasets show TPLA maintains comparable accuracy to baseline MLA, while direct conversion to GQA causes notable accuracy loss.
• Ablation studies indicate that combining PCA-based reparameterization for both RMSNorm and softmax delivers optimal efficiency-accuracy tradeoffs.

TPLA is particularly advantageous for long context inference scenarios in LLMs, where model throughput and memory consumption are critical bottlenecks.

6. Extensions, Limitations, and Future Directions

TPLA's success at two-shard partitioning is empirically established. Scaling to higher degrees of tensor parallelism (beyond two groups) invites ongoing investigation:

• Exploration of higher-order Hadamard-like transforms and more sophisticated reparameterization strategies for multi-shard partitioning is underway.
• Training TPLA from scratch, rather than converting pretrained MLA models, may further optimize latent representation splitting.
• Further research into integrated hardware acceleration and prefill–decode separation dynamics is proposed.

A plausible implication is that the compositional flexibility of TPLA may extend to advanced multi-modal models and highly parallel deployment environments.

7. Context and Significance in Latent Factor and Tensorized Attention Research

TPLA fits within a broader trend of compressive and factorized attention strategies. Previous work in spectral tensor decomposition (Huang, 2016), tensorized attention (Feng et al., 28 Oct 2024), and tensor product attention (Zhang et al., 11 Jan 2025) similarly leverage tensor algebra to reduce complexity and facilitate parallel computation. TPLA's design reflects convergence between low-rank latent mechanisms and hardware-aware parallelization, combining the strengths of unsupervised latent factor discovery, memory-efficient key-value caching, and orthogonalized tensor partitioning. This suggests a trajectory toward increasingly efficient parallel inference in foundation models—balancing model accuracy, throughput, and resource constraints.

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Table 1: TPLA vs. MLA and GQA in Cache Partitioning

Method	Latent Dimension per Head	Device Cache Duplication	Accuracy Retention
MLA	Full	Full (per device)	High
GQA	Partial	Sharded	Low
TPLA	Full (per Head, per Shard)	Sharded (orthogonally transformed)	High

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TPLA provides a principled solution for tensor-parallel autoregressive decoding in LLMs, combining memory and bandwidth efficiency with full representational capacity, and integrates smoothly with practical deployment environments via orthogonal partitioning and AllReduce aggregation. The approach is underpinned by both theoretical guarantees and empirically validated performance on contemporary foundation model benchmarks (Tang et al., 21 Aug 2025).