Accelerating LLM Inference Without Attention Approximation like group query attention

Apr 15, 2025

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Accelerating LLM Inference Without Attention Approximation
Model Quantization and Compression
Parallelism and Optimized Inference Kernels
Speculative Decoding (Parallel Token Generation)
Caching and Batching Strategies
Real-World Applications and Implementations

Large Language Model (LLM) inference is notoriously slow due to the sequential, token-by-token generation in autoregressive transformers. Recent research in 2024–2025 has focused on accelerating LLM response times without resorting to approximate attention mechanisms (e.g. group query attention). Below we review key techniques – quantization, parallelism, speculative decoding, caching, and others – citing state-of-the-art studies and discussing practical implementations.

Model Quantization and Compression

Reducing numerical precision of model weights and activations can drastically speed up inference by lowering memory bandwidth and compute requirements. Quantization represents parameters in fewer bits (e.g. 8-bit or 4-bit instead of 16/32-bit). Post-training quantization (PTQ) has been shown to accelerate LLMs with minimal accuracy loss ( I-LLM: Efficient Integer-Only Inference for Fully-Quantized Low-Bit Large Language Models). Hu et al. (2024) introduce I-LLM, an integer-only 4-bit inference framework that avoids any float operations (even for layer norms and softmax) and achieves nearly the same accuracy as full precision . In an extreme case, Ma et al. (2024) demonstrate a ternary-weight 1.58-bit LLM that matches the performance of a full 16-bit model while significantly improving latency, memory, and energy efficiency ( The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits). These results suggest aggressive quantization (to 4-bit and beyond) can yield multi-fold speedups.

Other compression techniques include pruning and distillation. Pruning removes less-important weights or neurons, reducing the model’s size and computation. Recent work on structured pruning for LLMs (e.g. Probe Pruning) dynamically removes up to 40% of model weights per input with minimal loss in accuracy (Probe Pruning: Accelerating LLMs through Dynamic Pruning via Model-Probing). Meanwhile, knowledge distillation (KD) trains a smaller “student” model to imitate a large model. KD has become pivotal for compressing LLMs – transferring the knowledge of GPT-4-scale teachers into lightweight students that run faster ( A Survey on Knowledge Distillation of Large Language Models). For example, distilled models like MiniLLM show that one can retain strong capabilities in a fraction of the parameters (MiniLLM: Knowledge Distillation of Large Language Models - arXiv). These compression approaches trade some training effort (or a slight drop in accuracy) for substantial inference speed gains, and they are complementary to other optimizations.

Parallelism and Optimized Inference Kernels

Another line of attack is to leverage more hardware parallelism. Tensor parallelism splits the model’s weight matrices across multiple GPUs (or TPUs) so that each forward-pass matrix multiplication is done in parallel, effectively reducing latency. This was critical for serving the earliest 100B+ models and remains standard in 2024 deployments. Similarly, pipeline parallelism divides the stack of transformer layers among devices and streams batches through them, keeping all GPUs busy. Modern inference frameworks (e.g. DeepSpeed-Inference and TensorRT-LLM) combine tensor and pipeline parallelism behind the scenes to meet strict latency targets ( DeepSpeed-FastGen: High-throughput Text Generation for LLMs via MII and DeepSpeed-Inference). For instance, DeepSpeed-FastGen (2024) uses such a backend to serve models with 2× lower latency on average than prior systems .

Beyond distribution, low-level kernel optimizations greatly improve throughput. One example is FlashAttention, an exact algorithm that reorders attention computations to minimize memory access. FlashAttention and its successors (adopted in many 2023–2024 LLMs) substantially speed up attention without approximations. Likewise, fused GPU kernels for layer operations, optimized quantized GEMM instructions, and hardware-specific libraries (CUDA Graphs, AMP, etc.) all contribute to faster inference. Research on hardware-software co-design, such as custom support for mixed precision multiplication (MixPE: Quantization and Hardware Co-design for Efficient LLM Inference), further boosts efficiency on new AI accelerators. In practice, these optimizations are often bundled into high-performance inference engines (NVIDIA’s FasterTransformer, ONNX Runtime, etc.), enabling developers to achieve near hardware-peak speeds with minimal code changes.

Speculative Decoding (Parallel Token Generation)

Speculative decoding is a novel paradigm that tackles the fundamental sequential bottleneck of autoregressive generation. The idea is to predict multiple future tokens in parallel and verify them, rather than generating strictly one-by-one. Early works (Leviathan et al., 2023; Chen et al., 2023) introduced the two-model approach: a small draft model quickly generates a few token candidates ahead, and the large model then validates them in one pass, ensuring the final output distribution is unchanged ( Speculative Streaming: Fast LLM Inference without Auxiliary Models). This method can roughly double generation speed with negligible quality impact .

Recent research in 2024 has refined speculative decoding to avoid maintaining two separate models. Medusa (Cai et al., 2023) is a single-model variant that adds multiple parallel prediction heads to an LLM, allowing it to output up to 4 tokens per step. While effective, Medusa incurs a large parameter overhead for those extra heads . Apple’s Speculative Streaming method addresses this by training the model to n-gram predict its own future tokens, fusing draft and verify steps together . This achieves 1.8×–3.1× faster decoding on tasks like summarization without any helper model . Moreover, it matches the speedups of Medusa-style architectures while using 10,000× fewer extra parameters , making it attractive for resource-constrained settings. Another innovation is Early-Exit Speculative Decoding (EESD) ( Speculative Decoding via Early-exiting for Faster LLM Inference with Thompson Sampling Control Mechanism), which uses the early layers of the large model itself to draft tokens (essentially a “fast but rough” preview) and then confirms them with the full model’s later layers . Liu et al. (2024) report that EESD significantly accelerates generation on 13B–70B models while guaranteeing the exact same output distribution as standard decoding . Overall, speculative decoding techniques offer theoretical speedups of 2–4×, and 2024 deployments are beginning to adopt them for high-throughput generative services.

Caching and Batching Strategies

Many latency optimizations come from smarter reuse of computations and better workload scheduling. Caching is a fundamental technique: as an LLM generates text, it caches the key/value vectors from each transformer layer so that on the next token, the model doesn’t recompute attention over all past tokens. This key-value cache yields an order-of-magnitude speedup for long sequences by avoiding repeated work. Modern LLM libraries implement caching in the decoding loop, as illustrated below:

## Pseudocode: autoregressive decoding with KV cache
cache = None
for t in range(max_new_tokens):
    logits, cache = model(input_ids=prev_token, past_key_values=cache)
    next_token = select_top_token(logits)
    prev_token = next_token

Here, past_key_values (the cache) carries forward the necessary state so that each iteration’s work is proportional to one token, not the entire generated sequence. In multi-turn interactions or systems with a fixed prompt prefix, caching the computed hidden states of the prompt can also save time on subsequent queries.

In multi-user or batch settings, dynamic batching and concurrent decoding are crucial for throughput. By grouping multiple requests together, GPUs can be better utilized through vectorized operations. However, naively batching LLM requests is challenging when they have different lengths or arrive asynchronously. Cutting-edge inference servers use techniques like prompt splitting and continuous batching to maximize efficiency. For example, DeepSpeed-FastGen introduces Dynamic SplitFuse, which splits long prompts into smaller chunks so that other short requests can interleave, achieving up to 2.3× higher throughput and 2× lower latency than prior systems like vLLM ( DeepSpeed-FastGen: High-throughput Text Generation for LLMs via MII and DeepSpeed-Inference). Similarly, Agrawal et al. (2024) propose stall-free batching to avoid idle time when some sequences finish earlier, improving GPU utilization.

At the systems level, recent work has focused on memory management for caching. The vLLM engine (Li et al., 2023) introduced PagedAttention, which treats the GPU memory for the KV cache like virtual memory – allocating it in flexible pages rather than a fixed contiguous block per session (vAttention: Dynamic Memory Management for Serving LLMs without PagedAttention). This eliminates wasted memory (internal fragmentation) and allows serving larger batches with high throughput. Microsoft’s vAttention (2024) builds on this by leveraging hardware-supported memory paging to keep KV caches contiguous in virtual address space while still allocating physical memory on-demand . This design avoids custom kernels and further improves token generation speed over vLLM . These caching strategies, combined with optimized memory allocators, enable production systems to handle hundreds of concurrent generations with minimal latency overhead.

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Real-World Applications and Implementations

In practice, accelerating LLM inference requires combining the above techniques within deployment constraints. Cloud providers serving models like GPT-4 or PaLM 2 at scale use model parallelism (sharding the model across many GPUs) alongside quantization to fit the model in memory and reduce latency. They also heavily batch user queries and use caching for conversational contexts, to meet strict response-time SLAs. OpenAI’s and Meta’s infrastructure details are proprietary, but the inference optimizations are in line with the published research. For instance, OpenAI has hinted at using 8-bit weight quantization and custom GPU kernels to speed up GPT-3-class models ( I-LLM: Efficient Integer-Only Inference for Fully-Quantized Low-Bit Large Language Models), and Meta’s deployment of Llama2-chat likely relies on tensor parallelism across GPUs and continuous batching similar to FastGen’s methods.

On the edge and in smaller-scale deployments, quantization and distillation are even more critical. Community projects have shown that a 7B-13B parameter LLM can run on a consumer GPU – or even on a smartphone – by using 4-bit weights and other optimizations, albeit with some sacrifice in accuracy. Apple’s research on Speculative Streaming explicitly targets on-device use, achieving multi-X speedups without extra model overhead ( Speculative Streaming: Fast LLM Inference without Auxiliary Models). Likewise, open-source serving frameworks like Hugging Face’s Text Generation Inference (TGI) and TensorRT-LLM integrate many of these techniques (quantized kernels, KV caching, concurrent batching) to deliver low-latency responses in real-world applications.

In summary, a plethora of 2024–2025 studies have advanced the state of the art in LLM inference. Techniques such as ultra-low-bit quantization, clever model parallelism, speculative decoding for parallel token generation, and intelligent caching/batching strategies all contribute to faster response times. Importantly, these methods are often complementary – e.g. one can deploy a 4-bit quantized LLM with speculative decoding on a multi-GPU server with dynamic batching. By adopting these innovations, practitioners can achieve significant latency reductions (often 2–4× or more) without resorting to approximate attention techniques, thus preserving model accuracy while greatly improving usability .

References: The review cites recent works from arXiv 2024–2025, including techniques for quantization ( I-LLM: Efficient Integer-Only Inference for Fully-Quantized Low-Bit Large Language Models), model-serving optimizations ( DeepSpeed-FastGen: High-throughput Text Generation for LLMs via MII and DeepSpeed-Inference), speculative decoding methods ( Speculative Streaming: Fast LLM Inference without Auxiliary Models), and others as detailed above. Each citation corresponds to the latest available findings relevant to accelerating LLM inference without attention approximations.