By Cesar Miguelañez — 04 Jun 2025

Ultimate Guide to LLM Inference Optimization

Q: What’s the difference between GPUs and TPUs for LLM inference, and how do I choose the right one for my needs?

When working on LLM (Large Language Model) inference , the choice between GPUs and TPUs hinges on what your application specifically needs. GPUs are incredibly adaptable and widely favored for tasks requiring flexibility. They shine when combining deep learning with other computational processes or when compatibility with multiple frameworks is essential. This makes them perfect for diverse workloads. TPUs , in contrast, are specialized hardware designed specifically for machine learning and deep learning tasks. They handle large-scale tensor operations with impressive efficiency and are optimized for performance-per-watt. This makes them a solid option for projects that focus entirely on AI model inference. In short, for high throughput and energy-efficient deep learning , TPUs might be your go-to. But if your work demands versatility or involves a mix of computational tasks, GPUs are probably a better fit.

Learn essential techniques for optimizing LLM inference to improve speed, reduce costs, and enhance performance in AI applications.

Want faster AI responses and lower costs? Optimizing LLM inference is the key. Large language models (LLMs) power chatbots and AI tools, but their performance depends on how efficiently they generate responses. Here's what you need to know:

Why it matters: Optimization speeds up response times, reduces costs, and supports more users.
Challenges: Memory limits, balancing speed vs. capacity, and high computational demands.
Proven solutions: Techniques like quantization, pruning, dynamic batching, and hardware upgrades (e.g., GPUs, TPUs) can improve efficiency.
Results: Faster interactions, lower latency, and reduced operational expenses.

Quick Tip: Start with simple changes like reducing model precision (e.g., 8-bit quantization) for up to 4x faster results while keeping accuracy nearly intact. Ready to dive deeper? Let’s explore the techniques, tools, and hardware to optimize your AI systems.

Common Bottlenecks in LLM Inference

When it comes to building responsive AI applications, understanding the factors that slow down large language model (LLM) inference is key. Three main challenges stand out: memory limitations, the balance between latency and throughput, and the computational demands of transformer architectures. Let’s break these down and see how they impact performance in real-world scenarios.

Memory Constraints and Bandwidth Limits

For LLM inference, memory bandwidth often poses a bigger challenge than raw computational power. Because batch sizes are typically small during decoding, the process becomes memory-bound, meaning the system struggles more with moving data than with actual computations.

Traditional DDR memory can create bottlenecks by limiting how quickly data is accessed and processed. This becomes especially problematic for larger models, which need massive amounts of data to be readily available during inference. The speed at which data moves from GPU memory to local caches and registers directly affects how fast tokens are generated [4].

High-bandwidth memory (HBM) offers a way to address this issue. It provides significantly faster data transfer compared to DDR memory, which helps reduce latency and speeds up both training and inference. However, even cutting-edge hardware like the H100 GPU, which comes with 80 GB of VRAM, has its limits.

Performance improvements highlight the importance of tackling memory constraints. For instance, cache compression techniques can achieve up to a 2.9× speedup while nearly quadrupling memory capacity. Combining optimizations like HBM and cache compression directly tackles these bottlenecks.

But memory isn’t the only factor affecting speed - balancing latency and throughput is another critical challenge.

Latency vs Throughput Tradeoffs

Every LLM deployment must decide: prioritize quick responses for individual users or maximize the number of users served simultaneously. This tradeoff exists because increasing throughput - processing more queries at once - can slow down token generation for each query [4].

For example, using one NVIDIA A100 GPU, increasing the batch size to 64 can boost throughput by 14× but also raises latency by 4× [4]. For interactive applications like chatbots, responsiveness depends on two key metrics: the time-to-first-token, which affects how quickly a response starts, and time-per-output-token, which impacts the overall speed of the interaction [4]. Since the average human visual reaction time is around 200 milliseconds, keeping the time-to-first-token below this threshold makes a system feel snappy and responsive.

Real-world examples show how optimizations can improve these metrics. In one case, NVIDIA's Llama 3.1 8B Instruct model achieved a 2.5× boost in throughput, a 4× faster time-to-first-token, and a 2.2× reduction in inter-token latency compared to leading open-source alternatives.

Rajvir Singh and Nirmal Kumar Juluru from NVIDIA explain, "The trade-off between throughput and latency is driven by the number of concurrent requests and the latency budget, both determined by the application's use case."

Different batching strategies - such as continuous, static, or dynamic batching - can help balance throughput and latency depending on the specific use case [4].

While memory and batching are key considerations, the computational nature of transformer architectures adds another layer of complexity.

Compute Challenges in Transformer Architectures

Transformer architectures, the backbone of LLMs, come with their own set of computational hurdles. Since these models generate text one token at a time, the sequential nature of token generation makes it hard to parallelize the process, creating bottlenecks.

Model size also plays a role in computational demands, though the relationship isn’t always straightforward. For example, MPT-30B has roughly 2.5× the latency of MPT-7B, while Llama2-70B’s latency is about 2× that of Llama2-13B [4]. Larger models are slower, but the increase in latency doesn’t scale linearly with the number of parameters [4].

The power demands of these computations are also substantial. A single H100 GPU uses 700 watts to perform 3×10^15 8-bit floating-point operations per second (FLOPs). Other factors, like input prompt length, output length, network conditions, and system load, further contribute to delays. Even lower-level issues like system calls and page faults add to the overall latency.

Targeted optimizations can make a big difference here. For instance, an optimized version of Anthropic's Claude 3.5 Haiku model achieved up to a 42.20% reduction in median time-to-first-token and a 51.70% reduction in the 90th percentile metric. Similarly, Meta's Llama 3.1 70B model saw up to a 51.65% reduction in median time-to-first-token and a staggering 97.10% improvement in the 90th percentile time.

These challenges, while complex, are not insurmountable. With the right engineering approaches, such as quantization and system-level optimizations, these bottlenecks can be effectively addressed. The next section will dive into specific techniques that help tackle these constraints and maximize performance.

Proven Techniques for LLM Inference Optimization

Addressing memory, latency, and computational challenges requires targeted techniques. Below are some proven methods to optimize large language model (LLM) inference.

Quantization for Reduced Precision

Quantization is a technique that shrinks model size and speeds up inference by converting parameters from high-precision formats (like FP32) to lower-precision formats (such as INT8 or INT4).

For example:

A PyTorch BERT model saw its size drop from 417.72 MB to 173.08 MB, with inference speed improving by about 27%.
A TensorFlow MobileNetV2 model was reduced from 8.45 MB to 2.39 MB.

There are two main approaches to quantization:

Post-Training Quantization (PTQ): This method applies quantization after a model is trained. While quick, it can lead to accuracy losses of 5–20% for binary quantization or 2–10% for ternary quantization.
Quantization-Aware Training (QAT): This integrates quantization into the training process, generally preserving accuracy more effectively. For instance, TensorFlow Lite’s PTQ has demonstrated 2× to 4× faster inference speeds with only a 1–2% accuracy drop.

Quantization often pairs well with pruning, further trimming computational needs by removing less essential parameters.

Pruning and Model Compression

Pruning reduces model size and computation by cutting parameters that have minimal impact on the output. This technique can streamline models without significantly affecting their performance.

Unstructured Pruning: Offers flexibility by creating sparsity in parameters but often requires specialized sparse matrix multiplication kernels.
Structured Pruning: Removes entire groups of weights, simplifying dimensionality reduction without the need for special hardware.

For instance, SparseGPT has achieved 50% sparsity in GPT-175B models with minimal performance loss. Tools like LLM-Pruner go a step further by using data-dependent estimators to identify less critical connections. After pruning, a short fine-tuning phase (around three hours) restores accuracy.

Other techniques, like knowledge distillation - where a smaller model learns to replicate a larger one - and iterative pruning with retraining, can further enhance performance while maintaining accuracy.

System-Level Optimizations

Optimizing the broader system is just as important as refining the model itself. These strategies focus on making better use of hardware and improving memory management.

KV Caching: Stores key-value pairs to avoid redundant token computations. However, its memory demands scale with sequence length and batch size.
PagedAttention: Manages GPU memory like virtual memory pages, reducing KV memory fragmentation from about 70% to under 4%. The vLLM library’s PagedAttention implementation, for example, achieved up to 24× higher throughput compared to a basic Hugging Face setup by packing sequences more efficiently.
Dynamic Batching: Unlike static batching, in-flight batching removes completed sequences from a batch in real time, allowing new requests to join. This approach significantly improves GPU utilization in practical scenarios.

Other system-level tweaks include:

Pinning inference threads to specific CPU cores to reduce latency jitter.
Using asynchronous I/O and memory-mapped files to minimize system calls.
Enhancing attention mechanisms with techniques like multi-query attention (MQA) and grouped-query attention (GQA), which reduce memory usage by cutting the number of stored key and value heads.

When combined, these system-level optimizations can turn resource-intensive models into efficient, production-ready systems with far better performance.

Hardware and Tools for Optimization

Once you’ve tackled model-level and system optimizations, the next step to achieving top-notch inference performance lies in choosing the right hardware and tools. Modern accelerators, paired with specialized frameworks, can dramatically boost speed and cut costs.

GPU Acceleration and Mixed Precision Computing

GPUs vs TPUs: Picking the Right Option

GPUs are known for their thousands of efficient cores, making them versatile for a wide range of AI tasks. TPUs, on the other hand, are built specifically for tensor operations in deep learning.

The performance gap between the two can be striking. For example, processing a batch of 128 sequences with a BERT model takes just 3.8 milliseconds on an NVIDIA Tesla V100 GPU, while the same task on a Google Cloud TPU v3 takes only 1.7 milliseconds. The difference becomes even more pronounced in training tasks - training a ResNet-50 model on the CIFAR-10 dataset for 10 epochs takes around 40 minutes on a Tesla V100, compared to just 15 minutes on a TPU v3.

Hardware	Processing Speed (BERT 128 sequences)	Training Time (ResNet-50)	Cost per Hour
NVIDIA Tesla V100	3.8 ms	40 minutes	$2.48
NVIDIA A100	N/A	N/A	$2.93
Google Cloud TPU v3	1.7 ms	15 minutes	$4.50
Google Cloud TPU v4	N/A	N/A	$8.00

GPUs are a solid choice when you need broad framework support and flexibility. Meanwhile, TPUs shine in TensorFlow-based projects that demand fast training, quick inference, and energy efficiency.

The Perks of Mixed Precision Computing

Switching to mixed precision formats like FP16 or bfloat16 can deliver 2×–3× speed improvements and lower memory usage. For instance, Volta generation GPUs with Tensor Cores can process eight times more data compared to single-precision pipelines.

Here’s a real-world example: switching from standard precision to mixed precision reduced training time from 21.75 minutes to 7.25 minutes, cut memory usage from 5.37 GB to 4.31 GB, and even improved test accuracy from 89.92% to 92.15%.

Mixed precision also impacts inference efficiency. For instance, the LLaMA model in float32 consumed twice as much memory (27.02 GB vs. 13.52 GB) and was 30% slower (11.47 tokens/sec vs. 16.70 tokens/sec) compared to bfloat16.

To fully leverage Tensor Cores, align dimensions like mini-batch sizes, linear layers, and convolution channels to multiples of 8. This ensures you’re making the most of your hardware’s capabilities.

With these hardware optimizations in place, specialized frameworks can further enhance inference performance.

Specialized Inference Frameworks

Advanced Matrix Multiplication Kernels

Specialized kernels, like Marlin, have significantly improved matrix multiplication for large language models. Marlin achieves 4× speedups with FP16×INT4 computations for batch sizes up to 32, consistently delivering near-optimal performance across varying batch sizes.

In some cases, custom configurations can push these gains even further. For example, using W1A2 and W2A2 setups for larger matrices (1k×1k and above) yielded 44× and 50× speedups, respectively.

Collaborative Development Platforms

Beyond raw speed, collaborative platforms play a vital role in deploying these innovations in practical scenarios. Tools like Latitude allow engineers and domain experts to work together on production-grade LLM features. This teamwork blends technical know-how with domain-specific insights, resulting in better-performing systems.

Optimizing GPU Resources

Getting the most out of your GPU requires careful management of resources like global memory, L2 cache, shared memory, vector cores, and tensor cores. Techniques such as asynchronous global weight loads, circular shared memory queues, and smarter task scheduling can significantly boost efficiency.

For instance, using circular buffers to overlap data transfers with computation minimizes idle time and keeps the GPU running at maximum capacity.

Performance Profiling and Monitoring

Key Metrics to Watch

To identify bottlenecks, focus on metrics like FLOPs utilization, latency breakdown, and memory bandwidth. FLOPs utilization highlights computational efficiency, while latency and bandwidth metrics can uncover data transfer issues.

Profiling Best Practices

Start by establishing baseline measurements for various batch sizes and sequence lengths. Monitor GPU memory usage to spot fragmentation or allocation inefficiencies, and analyze the ratio of compute time to memory transfer time. Profiling tools can help you identify kernel-level bottlenecks, allowing you to combine smaller operations into more efficient ones.

Continuous Monitoring in Production

In production environments, keep an eye on latency percentiles (P50, P95, P99) rather than just averages. Monitoring memory usage trends can help detect memory leaks or gradual performance degradation. Alerts for throughput drops or latency spikes can signal hardware problems or inefficient batching. Regularly tuning inference parameters ensures your system stays efficient as models and workloads evolve. This ongoing process is key to maintaining peak performance.

Applications and Case Studies

Optimization Success Stories

Perplexity AI achieved a major performance boost by upgrading to H100 GPUs, cutting latency by 54% and increasing throughput by 184%. Adding fp8 optimization further reduced latency by 49% and improved throughput by an impressive 202%.

Microsoft's ONNX Runtime optimizations for Llama2 models delivered up to 3.8× faster inference speeds. For the 13B model specifically, end-to-end throughput improved by 2.4×.

AWS took optimization to the next level by deploying Llama 2 70B on Inferentia2 instances. This setup achieved approximately 42.23 tokens per second with a per-token latency of just 88.80 ms.

Headset revamped its batch categorization pipeline by switching providers, slashing execution time from 20 minutes to just 20 seconds. That’s a staggering 60× improvement in speed.

These examples highlight how advancements in hardware and quantization can dramatically enhance performance. Such improvements are not just technical milestones - they’re reshaping how industries tackle unique business challenges.

Industry Use Cases

By leveraging these optimization strategies, industries are seeing dramatic reductions in latency and operational costs. For example, banking apps and e-commerce platforms are using techniques like quantization and batch processing to improve customer support and deliver smoother, faster experiences.

The manufacturing sector is also reaping the benefits. One FMCG manufacturer used an LLM to streamline supply chain operations. By employing Tensor Parallelism to distribute the model's workload across multiple GPUs, they reduced the time needed to generate insights and scaled their operations more effectively.

In the automotive world, companies like Nissan are using optimization to accelerate their AI initiatives. Partnering with Snowflake, Nissan shortened their project timeline by two months for a customer intelligence tool. This tool analyzes customer sentiment from reviews and forums, helping dealerships refine their offerings.

Speed has become a critical advantage in AI deployment. Skai, an e-commerce analytics company, launched a categorization tool in just two days. The tool helps customers identify purchasing patterns by creating categories that work seamlessly across different e-commerce platforms.

One of the most striking examples of efficiency gains comes from document processing. Tasks like categorizing 10,000 support tickets, which would take an employee about 55 hours, can now be done in minutes with an optimized LLM pipeline. This represents a productivity boost of over 100× in some automated workflows.

The benefits go beyond just speed. Successful LLM optimization requires a deep understanding of business goals, combined with the right mix of techniques, hardware, and deployment strategies. The payoff is clear: faster responses, lower costs, better user experiences, and quicker rollouts of AI-driven features.

Conclusion and Key Takeaways

Optimizing LLM inference has become a critical focus for businesses striving to balance performance with resource efficiency. By strategically applying techniques like quantization, knowledge distillation, architectural tweaks, and memory management, organizations can achieve substantial improvements in speed and resource usage without compromising too much on accuracy.

For example, DistilBERT demonstrates the power of compression by shrinking a BERT model by 40%, while still retaining 97% of its language understanding capabilities and operating 60% faster. Similarly, Apple's "Apple Intelligence" model leverages a mixed 2-bit and 4-bit configuration, averaging 3.7 bits-per-weight, to maintain uncompressed model accuracy while adhering to strict memory and power constraints.

Looking ahead, hardware-software co-design will play an increasingly pivotal role. Tailoring optimization strategies to specific hardware, such as NVIDIA GPUs or Google TPUs, can unlock performance levels that generic methods simply can't achieve. On the system side, techniques like in-flight batching and speculative inference offer significant boosts, with speculative decoding proving to enhance speed while preserving response quality.

Each optimization method comes with tradeoffs. Quantization offers immediate gains in memory and speed but may slightly reduce accuracy. On the other hand, knowledge distillation prioritizes accuracy retention, though it requires additional training effort. Balancing these tradeoffs is key to addressing the memory, latency, and computational challenges discussed earlier.

To ensure sustained success, continuous monitoring is essential. Metrics such as Time-to-First-Token (TTFT) and Inter-token Latency (ITL) provide valuable insights into how well optimization efforts are performing over time.

Platforms like Latitude simplify the process of LLM optimization by offering open-source tools for collaborative development. This infrastructure ensures that optimization strategies are not only implemented efficiently but also deliver measurable business results.

FAQs

How does quantization make large language models more efficient without losing much accuracy?

Quantization is a method used to boost the efficiency of large language models (LLMs) by lowering the precision of their weights and activations. By doing so, the memory needed to store the model is reduced, and computational demands are minimized. The result? Faster inference times and less energy consumption.

Even when precision is reduced to formats like 4-bit or 8-bit, these quantized models typically retain accuracy levels that are nearly identical to their full-precision counterparts. This makes quantization an effective way to improve performance while keeping the quality of results virtually intact.

What’s the difference between GPUs and TPUs for LLM inference, and how do I choose the right one for my needs?

When working on LLM (Large Language Model) inference, the choice between GPUs and TPUs hinges on what your application specifically needs.

GPUs are incredibly adaptable and widely favored for tasks requiring flexibility. They shine when combining deep learning with other computational processes or when compatibility with multiple frameworks is essential. This makes them perfect for diverse workloads.

TPUs, in contrast, are specialized hardware designed specifically for machine learning and deep learning tasks. They handle large-scale tensor operations with impressive efficiency and are optimized for performance-per-watt. This makes them a solid option for projects that focus entirely on AI model inference.

In short, for high throughput and energy-efficient deep learning, TPUs might be your go-to. But if your work demands versatility or involves a mix of computational tasks, GPUs are probably a better fit.

How does dynamic batching optimize GPU performance, and what are its effects on latency and throughput in practical applications?

Dynamic batching boosts GPU performance by combining multiple requests and processing them at the same time. This technique takes advantage of the GPU's parallel processing power, increasing throughput - or the number of inferences it can handle per second. By minimizing idle time, it ensures the GPU operates more efficiently, making better use of its computational capacity.

That said, there’s a trade-off: latency for individual requests might go up slightly since the system waits momentarily to gather enough inputs to form a batch. Even so, the overall performance usually gets a lift, particularly during heavy workloads, as more requests are completed in less time. This approach is a practical way to speed up and streamline LLM inference when handling real-world demands.