LLM Inference Optimization: Speed, Scale, and Savings
Explore key techniques for optimizing large language model inference, enhancing speed, scalability, and cost efficiency while maintaining quality.
Large language models (LLMs) are powerful but expensive and resource-intensive to deploy. Optimizing their inference can make them faster, cheaper, and more efficient without sacrificing output quality. Here are five key techniques to achieve this:
- Model Distillation: Shrinks models while preserving performance, reducing size and computational needs.
- Quantization: Lowers precision of model weights (e.g., FP32 to INT8), improving speed and reducing memory usage.
- Pruning and Sparsity: Removes unnecessary parameters, cutting size and computational load.
- Dynamic and Continuous Batching: Groups requests for better GPU utilization, boosting throughput.
- KV Cache Optimization: Reuses computation results to speed up processing for long sequences.
These methods can cut costs by up to 80%, improve latency, and make LLMs scalable for various applications, from chatbots to real-time tools. Combining multiple techniques often yields the best results, balancing speed, cost, and scalability.
1. Model Distillation
Model distillation creates a smaller, more efficient "student" model that mirrors the behavior of a larger "teacher" model. This process significantly reduces the model's size and computational demands while maintaining strong performance. It’s a practical way for organizations to use large language models without compromising accuracy.
The method involves training the smaller model to replicate the outputs of the larger one. Instead of building the student model from scratch, it learns from the teacher's expertise, achieving similar results with far fewer parameters.
Speed
Distilled models process data faster than their full-sized counterparts. Take DistilBERT, for example - it retains 97% of BERT’s language understanding capabilities while being 40% smaller and up to 60% faster. Larger models also show this advantage: Llama 3.2 3B achieves 72% lower latency and 140% higher output speed compared to Llama 3.1 405B. Besides faster processing, distilled models load into memory more quickly and consume fewer computational resources per inference, making them ideal for real-time applications like chatbots and AI assistants.
Scalability
Distilled models are highly adaptable across various deployment platforms. Their reduced size and computational needs mean they can run on devices with limited resources, such as mobile phones and edge devices, making advanced AI tools more accessible. For instance, in January 2025, researchers in digital pathology successfully distilled H-Optimus-0, a Vision Transformer with over one billion parameters, into H0-mini, a compact model with just 86 million parameters (around 8% of the original size). Despite its smaller size, H0-mini delivered competitive performance, ranking 3rd on the HEST benchmark and 5th on the EVA benchmark, while proving more robust to variations in staining and scanning conditions.
Moreover, techniques like QLoRA enhance scalability by allowing a 65-billion-parameter model to be fine-tuned on a single 48GB GPU. Distilled models can retain up to 97% of their original performance, making them a practical choice for deploying AI in environments with limited resources.
Cost Savings
Model distillation offers significant cost benefits by reducing resource consumption. Smaller models need less memory, fewer computational resources, and lower energy usage. For example, SwiftKV optimizations in vLLM by Snowflake AI Research reduced inference costs by up to 75% and cut prefill compute needs by 50%. Snowflake’s SwiftKV-optimized Llama 3.3 70B and Llama 3.1 405B models deliver these savings through serverless inference in Cortex AI, as of January 16, 2025.
Stanford’s Alpaca project further highlights the cost-efficiency of model distillation. Built on Meta’s LLaMa 7B model, Alpaca was trained in under two months for less than $600 using OpenAI’s text-davinci-003. Despite the low budget, it achieved performance close to GPT-3.5 at the time. While fine-tuning can reduce training costs, it often keeps inference expensive. Model distillation, on the other hand, increases training costs slightly but makes inference significantly more efficient, leading to long-term savings.
Next, we'll look at how quantization techniques can further improve inference performance.
2. Model Quantization
Model quantization is all about reducing the precision of model weights. By converting higher-precision formats like FP32 into lower-precision ones such as INT8 or even 4-bit, it compresses the model's parameter range into fewer discrete values. The result? Lower memory usage and compute demands, all while maintaining accuracy that’s good enough for most applications.
Speed
Quantized models are game changers when it comes to inference speed. For example, Deepseek (7B) running on an NVIDIA RTX 4090 saw its speed jump from 52 tokens per second to 130 tokens per second using AWQ quantization. Another example is Mistral (7B) on AWS EC2 g5.xlarge, which improved from 28 tokens per second to 88 tokens per second.
| Model | Deployment | Original Speed | Quantized Speed (AWQ) |
|---|---|---|---|
| Deepseek (7B) | NVIDIA RTX 4090 (24GB) | 52 tokens/s | 130 tokens/s |
| Deepseek (32B) | AWS EC2 g5.12xlarge (96GB) | 22 tokens/s | 50 tokens/s |
| Mistral (7B) | AWS EC2 g5.xlarge (24GB) | 28 tokens/s | 88 tokens/s |
| Llama 3.3 (70B) | AWS EC2 g5.48xlarge (192GB) | 23 tokens/s | 46 tokens/s |
Databricks also reported impressive results with their AutoGPTQ implementation on Llama 3.2 1B. A 4-bit quantized version of the model not only maintained consistent F1 scores (improving from 0.78 to 0.9) but also delivered a 30% boost in inference speed. Similarly, GPTQ quantization showed massive gains, offering up to 3.25× speedup on A100 GPUs and 4.5× on A6000 GPUs.
Scalability
Quantization isn’t just about speed - it’s also about making models more scalable. Whether you’re deploying in a high-end data center or on a resource-limited edge device, quantization makes it possible. For instance, a 500-million parameter language model in FP32 format takes up 2.0 GB of memory. When quantized to INT8, it requires only 0.5 GB - an impressive 75% reduction. This smaller memory footprint enables complex models to run efficiently on smartphones, IoT devices, and other edge platforms.
Qualcomm Technologies has even taken this a step further with their Generative Pretrained Transformer Vector Quantization (GPTVQ). This method groups parameters together for quantization rather than processing them individually. As Mart van Baalen and Abhijit Khobare from Qualcomm explain:
"Quantization is a technique used to reduce the precision of the numbers used in computations, which in turn decreases the model size and the resources needed to run it."
Cost Savings
Quantization doesn’t just save time - it also saves money. Compressing models through 4-bit quantization can reduce their size by 75%, while 8-bit quantization cuts memory usage in half with only about 1% accuracy loss.
Take PyTorch’s case study with a 7B-parameter Llama model as an example. Switching from FP16 to INT4 shrank the model size by 4×, from 16GB to just 4GB. Combined with low-level kernel optimizations, this change delivered a 25× speedup, slashing chatbot response times from 245 seconds to just 10 seconds.
Quantized models can also run on less expensive hardware, further driving down infrastructure costs. Depending on the use case, teams can choose between Post-Training Quantization (PTQ) - which is faster and easier to implement - or Quantization-Aware Training (QAT), which requires more resources during training but offers better accuracy. This flexibility helps organizations strike the right balance between cost and performance.
These benefits - speed, scalability, and cost efficiency - set the stage for techniques like model pruning and sparsity, which can push inference efficiency even further.
3. Model Pruning and Sparsity
Building on techniques like distillation and quantization, pruning takes optimization a step further by simplifying models to improve inference. This method involves removing less important or redundant weights - essentially turning them into zeros. By cutting out these unnecessary elements, pruning reduces both the size and complexity of a model while keeping its performance intact.
There are two main pruning approaches. Unstructured pruning targets individual weights, achieving higher sparsity but often requiring specialized hardware for processing sparse matrices. On the other hand, structured pruning eliminates entire groups of weights, such as neurons or channels, and works seamlessly with existing hardware.
Speed
One of the standout benefits of pruning is its ability to significantly speed up inference, which is critical for real-time applications. For example, a 2.4B model using ReLU activation achieved a 4.1× speedup compared to its dense counterpart. Similarly, Scaling Transformers demonstrated nearly a 20× faster prediction speed for a single token, cutting processing time from 3.690 seconds to just 0.183 seconds when compared to a dense Transformer with 17B parameters. Terraformer took this even further, achieving a 37× faster decoding speed than its dense baseline. Another example: LLaMA-30B was pruned in just 20 minutes using a single NVIDIA RTX 4090 GPU, showcasing the practical speed gains achievable with modern hardware.
Scalability
Pruning also makes models much more scalable by reducing their computational and memory demands. This makes it possible to deploy these optimized models on devices with limited resources, such as smartphones or IoT devices, where dense models would typically be unsuitable. For context, a standard dense Transformer requires about d² computations per prediction, while Scaling Transformers reduce this to roughly d^1.5, where "d" represents the number of parameters. This efficiency not only broadens deployment options but also helps cut operational costs.
Cost Savings
The cost savings from pruning are hard to ignore. By cutting model weights by over 50% - often with less than a 1% accuracy loss - pruning offers a clear financial advantage. For instance, an 83.8% sparse GPT-3 model achieved a 3× reduction in inference FLOPs and a 4.3× reduction in parameters, all without compromising performance. To put this into perspective, training a dense 175B parameter GPT-3 model on AWS can cost over $3 million for a single run.
Pruning also reduces storage needs, especially when paired with quantization, which can shrink model sizes by up to 50%. Beyond storage, it lowers power consumption - a crucial factor for mobile and edge computing. Additionally, using optimized compute instances on cloud platforms can lead to up to 90% savings compared to standard on-demand options.
That said, pruning isn’t without its challenges. Removing too many critical weights can hurt model performance. Careful analysis is needed to decide which parameters to prune, and post-pruning fine-tuning is often required to recover any lost accuracy. Despite these trade-offs, pruning and sparsity techniques remain powerful tools for improving speed, scalability, and cost-efficiency in large language model optimization.
4. Dynamic and Continuous Batching
Dynamic and continuous batching are methods designed to improve how requests are processed, ensuring GPUs operate at their full potential. Unlike static batching, which waits for an entire batch of requests before processing, dynamic batching groups requests based on either a full batch or a set time limit. Continuous batching takes it a step further by handling requests at the token level, adapting seamlessly to real-time traffic patterns.
Speed
Continuous batching significantly boosts processing speed, delivering up to 23× higher throughput compared to traditional batching methods. This improvement stems from better GPU utilization. Matt Howard from Baseten explains:
"Continuous batching improves GPU utilization over dynamic batching by eliminating the idle time waiting for the longest response of each batch to finish."
By injecting new requests into active batches, continuous batching reduces median latency and enhances responsiveness. While FasterTransformer keeps pace with continuous batching systems for shorter sequences, it begins to fall behind when generation lengths exceed 1,536 tokens.
Scalability
Continuous batching excels in high-demand scenarios by maximizing GPU efficiency. On AWS ml.g5.24xlarge instances, it nearly doubles the number of requests handled per second compared to dynamic batching:
| Batching Strategy | Requests per Second |
|---|---|
| Dynamic Batching | 3.24 |
| Continuous Batching | 6.92 |
| PagedAttention Batching | 7.41 |
As N. Patry from GitHub highlights:
"With continuous batching you can find a sweet spot. In general latency is the most critical parameter users care about. But a 2x latency slowdown for 10x more users on the same hardware is an acceptable trade-off".
The vLLM framework further enhances performance, delivering more than twice the efficiency of standard continuous batching methods. When paired with techniques like model distillation, quantization, and pruning, these batching strategies play a key role in reducing both latency and inference costs.
Cost Savings
Continuous batching offers considerable financial benefits, especially for organizations managing high volumes of LLM requests. For example, Anyscale reported that combining continuous batching with memory optimizations resulted in 23× greater throughput during LLM inference compared to processing requests individually.
One enterprise achieved a 2.9× cost reduction - and up to 6× savings when inputs shared common prefixes - by implementing continuous batching in its Anyscale batch inference pipeline.
Anthropic’s optimization of Claude 3 showcases another success story. Continuous batching increased throughput from 50 to 450 tokens per second, while slashing latency from around 2.5 seconds to under one second. Similarly, the latest vLLM v0.6.0 release improved throughput by 2.7× and reduced latency (measured per token) by 5× on Llama-8B compared to earlier versions.
Meta’s deployment of Llama 3.1 405B on AMD MI300X hardware underscores the value of continuous batching. By leveraging ROCm and vLLM optimizations, Meta managed production traffic at a fraction of the cost of NVIDIA-based setups, all while maintaining high performance.
These cost efficiencies are particularly valuable for organizations dealing with fluctuating traffic. Continuous batching ensures optimal hardware utilization during both peak and low-demand periods, making it a powerful complement to other optimization strategies for improving request processing.
5. KV Cache Optimization
KV cache optimization is a game-changer for improving the efficiency of large language model (LLM) inference. By reusing key (K) and value (V) tensors from earlier decoding steps, it eliminates the need for repetitive calculations during text generation. Without KV caching, LLMs would need to recompute attention for all previous tokens at every step of generation, leading to a computational cost that grows exponentially with sequence length.
Much like distillation and quantization, KV cache optimization is crucial for making LLM inference practical in production environments. As Sebastian Raschka, PhD, puts it:
"KV caches are one of the most critical techniques for efficient inference in LLMs in production."
This optimization shifts compute scaling from quadratic to linear, enabling LLMs to handle thousands of tokens more efficiently. Most decoder models now come with KV caching enabled by default, highlighting its importance in enterprise AI applications. Beyond boosting efficiency, KV caching significantly enhances processing speed, making it indispensable for modern deployments.
Speed
The speed improvements from KV cache optimization are impressive across different model sizes. For instance, a 124M parameter model achieved a 5× speed-up when generating a 200-token sequence. Larger models and longer sequences see even greater gains.
A standout example is vLLM's PagedAttention implementation, which takes KV caching to the next level. Developed by Kwon et al., PagedAttention treats GPU memory like virtual memory pages for KV storage. Instead of allocating one large, continuous cache per sequence, it breaks the cache into smaller, fixed-size blocks that can be dynamically allocated and reused. This approach reduced memory fragmentation from about 70% to under 4% and increased throughput by up to 24× compared to standard HuggingFace inference.
Flash-Decoding, another advanced KV caching technique, eliminates redundant operations in the attention mechanism. This results in up to 8× faster generation for very long sequences.
Scalability
KV cache optimization is essential for scaling LLMs in production. It allows models to process thousands of tokens efficiently and supports multi-user scenarios by sharing prompt prefixes, which reduces duplicate computations and improves throughput.
One of its key advantages is predictable memory usage. For example, LLaMA-2 13B requires about 1 MB of cache per output token, making it easier to plan infrastructure needs. Advanced methods like PagedAttention further enhance scalability by reducing cache fragmentation and optimizing memory use.
Real-world deployments showcase the benefits of KV caching. The vLLM system, using PagedAttention, achieved 14× to 24× higher throughput when serving models like LLaMA-7B and 13B. Meanwhile, Mistral 7B delivered 70% faster inference throughput compared to LLaMA-2 on the same hardware. KV caching has become a standard feature in production-grade LLM serving frameworks, especially for applications involving sequential or repeated queries. These improvements naturally lead to lower operational costs.
Cost Savings
By enhancing efficiency, KV cache optimization also reduces computational costs. Benchmark tests using NVIDIA H100s showed that optimized KV caching doubled throughput for models like Llama-3.3-70B. In real-world workloads, organizations reported up to a 75% reduction in serving costs, thanks to faster throughput and lower latency.
Snowflake's research team highlighted this efficiency, stating:
"SwiftKV achieves up to a 50% reduction in prefill compute while maintaining the accuracy levels demanded by enterprise applications."
When combined with other techniques, such as speculative decoding and FlashAttention, KV caching further maximizes both speed and cost efficiency. This makes it particularly valuable for applications like interactive chatbots, streaming services, long-document processing, and multi-user LLM setups.
Advantages and Disadvantages Comparison
Here's a breakdown of the trade-offs involved in the optimization techniques we’ve explored. Each method offers distinct benefits and drawbacks, making them suitable for specific deployment scenarios. This summary highlights how these techniques stack up in terms of speed, scalability, cost efficiency, and potential downsides.
Model distillation creates smaller models that retain much of the original's performance. It strikes a balance between speed, memory efficiency, and accuracy. However, it comes with a hefty computational cost during training and usually results in slight accuracy loss. For example, distillation can significantly shrink a model’s size but may sacrifice a bit of precision in the process.
Quantization is highly effective for reducing memory usage and speeding up inference. For instance, after AWQ quantization, benchmarks showed Deepseek (7B) running on an NVIDIA RTX 4090 jumped from 52 tokens per second to 130 tokens per second. While the accuracy loss is often minimal, more aggressive quantization levels might lead to noticeable degradation.
Pruning and sparsity focus on cutting out unnecessary parameters to reduce model size and computational load. Structured pruning, which removes entire components, delivers practical runtime benefits. On the other hand, unstructured pruning can be less effective due to hardware limitations in managing sparse computations. Interestingly, pruning can sometimes improve generalization, but it requires careful retraining to maintain overall performance.
Dynamic and continuous batching improves GPU efficiency by processing multiple requests at once. This technique is particularly useful in high-demand environments, as it significantly boosts throughput. However, the trade-off is that individual requests may experience increased latency while waiting to be batched.
KV cache optimization speeds up processing for long sequences by storing key and value tensors, avoiding redundant calculations. While this greatly enhances throughput, especially for lengthy inputs, it comes at the cost of increased memory usage.
Here’s a quick reference table summarizing these trade-offs:
| Technique | Speed Improvement | Scalability | Cost Savings | Main Disadvantage |
|---|---|---|---|---|
| Model Distillation | Moderate (e.g., 60% faster for DistilBERT) | High | High | Accuracy loss and high training complexity |
| Quantization | High (2–4× faster inference) | Very High | Very High | Potential accuracy degradation |
| Pruning/Sparsity | Moderate to High | High | High | Implementation complexity |
| Dynamic Batching | High throughput gains | Very High | High | Increased latency for individual requests |
| KV Cache Optimization | Very High (significant throughput boost) | High | High | Increased memory consumption |
The choice of technique depends on your specific needs. For example, quantization is ideal for setups focused on cost efficiency, while dynamic batching and KV caching excel in high-throughput environments. Many organizations combine methods - such as using quantization for memory savings, KV caching for speed, and batching for throughput - to create a well-rounded, scalable solution.
In terms of implementation, quantization and KV caching are relatively easy to deploy, often requiring minimal changes. On the other hand, model distillation involves significant engineering effort and computational resources. Pruning falls somewhere in the middle, with structured pruning generally being more practical for real-world use than unstructured methods.
Conclusion
Achieving optimal LLM inference requires blending techniques that align with your deployment goals. From exploring distillation and quantization to pruning, batching, and KV caching, it's clear that no single method dominates. Instead, the key lies in strategically combining these approaches.
If speed is your priority, quantization offers a major boost to inference times. Pair it with KV cache optimization, and you'll notice significant improvements in throughput, especially for applications requiring long-context processing.
For teams focused on cutting costs, quantization should be the first step. The real savings, however, come from layering multiple techniques. By integrating strategies like efficient fine-tuning, distillation, batching, and prompt optimization, it's possible to achieve over 80% cost savings without compromising performance.
In high-throughput scenarios, continuous batching is indispensable. Unlike traditional static batching, which forces all requests to wait for the slowest one, continuous batching dynamically processes incoming requests. This approach maximizes GPU utilization and significantly reduces the cost per request.
The most effective production setups typically combine at least three techniques. For instance, a high-performance configuration might use quantization for memory efficiency, KV caching for speed, and continuous batching for throughput. This layered strategy tackles multiple bottlenecks at once, resulting in a scalable and well-rounded solution.
While quantization and KV caching require minimal engineering effort, model distillation demands more resources. For most teams, starting with Post-Training Quantization (PTQ) strikes the best balance between speed, ease of implementation, and access to pre-made quantized checkpoints.
Ultimately, the right mix of these methods depends on your specific use case and constraints. Organizations that excel in optimizing these techniques will gain a competitive edge, both in cost efficiency and user experience. Whether you're leveraging platforms like Latitude for collaborative AI engineering or developing custom solutions, start by addressing your biggest bottleneck and gradually layer optimizations to build a system tailored to your needs.
FAQs
How does model distillation help lower the costs of deploying large language models?
Model distillation is a technique that creates smaller, more efficient versions of large models while maintaining their performance. These compact models use fewer computational resources, which means faster processing times and lower operational costs.
This approach optimizes resource usage, making it possible to deploy large language models on a broader scale without breaking the bank. It’s a smart way to achieve high performance while significantly cutting expenses.
What are the key differences and trade-offs between quantization and pruning for optimizing LLM inference?
Quantization is all about trimming down the precision of model weights and activations. This approach can boost inference speed and cut down on memory usage, often with only a minor trade-off in accuracy. It's a relatively simple technique to implement and works well in situations where computational efficiency takes center stage.
Pruning, meanwhile, focuses on paring down the model by removing less essential elements, like neurons or connections. This reduces the model's size and complexity, leading to significant savings in both memory and computation. However, it typically demands careful fine-tuning and can have a more noticeable impact on the model's performance.
To sum it up, quantization is quicker to roll out and delivers immediate perks, while pruning can dig deeper into optimization but often requires more effort and comes with a higher risk of affecting accuracy.
What is continuous batching, and how does it improve GPU performance, processing speed, and cost efficiency?
Continuous batching boosts GPU performance by dynamically grouping incoming requests into batches as they arrive. This method keeps the GPU running efficiently by minimizing idle time, as it doesn't have to wait for a complete batch to form.
The benefits? Higher throughput, lower latency, and faster processing speeds. Plus, it can slash costs - sometimes by as much as 40%. By making the most out of available resources, continuous batching is a smart, budget-friendly way to optimize large-scale LLM inference.