Optimizing LLM Inference: Hardware Choices, Cost Reduction, and Advanced Technique

Building on our understanding of LLM inference fundamentals covered in our previous article, we now turn to practical optimization strategies. Mastering these techniques transforms theoretical knowledge into tangible business value through reduced costs and enhanced user experiences.

This article explores optimization approaches across four key domains:

1. 1

   Hardware Selection

   Matching infrastructure to model requirements and workload patterns
2. 2

   Quantization Techniques

   Reducing numerical precision while maintaining response quality
3. 3

   Memory Management

   Implementing KV caching and attention optimizations
4. 4

   Processing Strategies

   Leveraging batching and parallelization for maximum throughput

Each domain offers multiple optimization opportunities. Combining techniques creates multiplicative benefits rather than merely additive gains. The right combination can yield 5-10x performance improvements while maintaining output quality.

Product leaders armed with this knowledge can implement inference solutions that balance performance, cost, and quality considerations for their specific use cases.

Key performance metrics for LLM inference evaluation

Product leaders need clear metrics for evaluating inference performance to make informed decisions about LLM deployment. These measurements provide the foundation for comparing solutions, optimizing systems, and setting realistic expectations for both technical teams and end users.

Time to first token

Time to First Token (TTFT) measures how quickly users start seeing a model's output after entering their query. This metric reveals the efficiency of request scheduling and input prefilling. Low TTFT is essential for real-time interactions but less important for offline workloads. Several factors influence TTFT, including network speed, input sequence length, and model size.

For interactive applications, users prefer having outputs streamed back from the inference server. TTFT provides insight into how well the model's server handles varying user requests in real-world settings.

Tokens per second throughput

Throughput measures how many requests an LLM can process or output it can produce within a given timeframe. This is typically measured in tokens per second, which tracks how many tokens an inference server generates across all users and requests.

Unlike requests per second (which depends on total generation time), tokens per second isn't dependent on input or output length. This makes it a more standardized metric for comparing model performance across different implementations.

Most production applications operate with a latency budget. Experts recommend maximizing throughput within that budget to optimize inference efficiency.

Memory utilization patterns

Memory usage represents the primary bottleneck in LLM inference, particularly during the decode phase. Unlike traditional deep learning tasks that are compute-bound, LLM token generation is memory-bound—limited by how quickly data can be transferred between GPU memory and compute units rather than by raw processing power.

Model Bandwidth Utilization (MBU) offers a critical metric for optimization, measuring what percentage of a GPU's theoretical memory bandwidth is actually being used. For most LLM inference workloads with small batch sizes, MBU rarely exceeds 30-40% of peak capacity, creating significant optimization opportunities.

The Key-Value (KV) cache dominates memory usage during inference, growing linearly with context length. For a 70B parameter model, each 1,000 tokens of context can require several gigabytes of memory. Advanced systems implement three key optimizations:

• Dynamic allocation that provisions memory only as needed
• Compression techniques that reduce precision of cached values
• Intelligent pruning of less relevant cached information

Benchmark comparison across model sizes

Performance scales sub-linearly with model size. Benchmarks show that on identical hardware, larger models are slower but not directly proportional to their parameter count. For example, MPT-30B latency is approximately 2.5x that of MPT-7B, while Llama2-70B latency is about 2x that of Llama2-13B.

Input length has minimal impact on performance but affects hardware requirements. Adding 512 input tokens increases latency less than producing 8 additional output tokens. However, supporting longer inputs can make models harder to serve.

As models scale from 7B to 70B parameters, throughput decreases while memory requirements increase dramatically. Tests across multiple GPUs show performance improvements with scaling up to 4 GPUs, but diminishing returns or even performance degradation beyond that point.

AI Edge Compute - a smart approach is hardware-software co-design with specialized inference accelerators that optimize both memory bandwidth and token generation speed.

With these performance metrics in mind, we can now turn our attention to the hardware infrastructure needed to support efficient LLM inference.

Hardware selection and deployment architecture

The hardware foundation you choose for LLM deployment dramatically affects both performance capabilities and operational costs. Making informed infrastructure decisions requires understanding the unique demands that inference places on computing resources and how different configurations support various use cases.

Technical requirements for model sizes

Selecting appropriate hardware begins with understanding memory requirements based on model size. For 16-bit precision (FP16 or BF16), which balances accuracy and efficiency, approximate VRAM needs are:

• 7B parameter models: 14-16GB VRAM
• 13B parameter models: 26-28GB VRAM
• 33B parameter models: 65-70GB VRAM
• 70B parameter models: 140-145GB VRAM

Quantization can substantially reduce these requirements:

• 8-bit quantization (INT8) cuts memory needs by ~50%
• 4-bit quantization (INT4) reduces requirements by ~75%

This explains why a 70B parameter model that would require multiple A100 GPUs at 16-bit precision can often run on a single GPU when quantized to 4-bit precision, though with potential trade-offs in output quality that must be evaluated for your specific use case.

GPU options for inference workloads

Different GPU models offer varying capabilities for LLM inference:

• NVIDIA L40S: Provides an optimal balance between performance and cost
• NVIDIA A100 (80GB): Ideal for larger models with 80GB HBM memory
• NVIDIA H100: Highest performance with specialized transformer engines
• NVIDIA T4: Cost-effective option for smaller models
• AMD Instinct MI300X: Strong alternative with high-throughput capabilities

LLM inference is typically memory-bound rather than compute-bound, making memory bandwidth a critical factor in GPU selection.

Single-server versus distributed configurations

For models exceeding single-GPU capacity, consider:

• Tensor parallelism: Distributing model layers across GPUs
• Pipeline parallelism: Splitting inference into sequential stages
• Multi-node deployments: Using NVLink or InfiniBand for high-speed connections

Cloud versus on-premises evaluation

When deciding between cloud and on-premises deployment, consider:

• Data privacy: Sensitive applications may require on-premises solutions
• Operational overhead: Cloud platforms reduce management complexity
• Scaling needs: Cloud offers faster scaling but at premium pricing
• Total cost of ownership: On-premises can be 40-60% cheaper for consistent workloads

Often a hybrid approach provides the best balance for many organizations.

Quantitative cost assessment

Right-sizing hardware can substantially reduce inference costs:

• Moving from H100 to A100 GPUs can reduce costs by approximately 40% for many workloads
• Batch processing optimization can improve throughput by 3-5x
• Implementing 8-bit quantization typically reduces costs by 50%
• 4-bit quantization can further decrease expenses by 30-40%

One enterprise reduced monthly inference expenses from $75,000 to $32,000 by optimizing GPU selection and implementing quantization.

CPU and edge device options

Not all inference requires GPUs:

• Modern server CPUs can efficiently run quantized 7B parameter models
• Intel's IPEX-LLM library optimizes inference on Core Ultra processors
• AMD's EPYC processors excel in multi-threaded inference workloads
• Edge devices can leverage 4-bit quantized models under 7B parameters

For IoT and mobile deployments, specialized edge accelerators like the Intel Neural Compute Stick provide dedicated inference capabilities with minimal power consumption.

With the hardware foundation established, we now turn to the software optimization techniques that can dramatically improve the efficiency of LLM inference on any infrastructure.

Inference optimization techniques and implementation

Beyond hardware selection, software optimization techniques can dramatically improve inference performance, often by orders of magnitude. These approaches allow organizations to extract maximum value from their infrastructure investments while delivering responsive user experiences.

Quantization for efficient deployment

Quantization reduces model precision to decrease memory and computational costs. Converting from 32-bit floating-point to lower precision formats like INT8 or INT4 can dramatically improve inference efficiency:

• 16-bit precision cuts memory requirements by roughly 50%
• 8-bit quantization reduces memory usage by approximately 75%
• 4-bit quantization can decrease GPU requirements by up to 75%

While quantization offers significant benefits, it must be implemented carefully. Testing with benchmarks like the Mosaic Eval Gauntlet is essential to ensure quality doesn't suffer from reduced precision.

KV caching mechanisms

KV (Key-Value) caching is a transformer-specific optimization that significantly improves computational efficiency during token generation. This technique:

• Stores intermediate key-value tensors from previous iterations
• Eliminates redundant computations when generating new tokens
• Reduces latency while improving throughput

Advanced KV cache management techniques include pruning outdated entries, compressing cached values to lower precision, and enabling multiple requests to utilize the same cached values when appropriate.

Batching strategies for throughput optimization

Batching processes multiple requests simultaneously to maximize GPU utilization. Three primary approaches include:

1. 1
   Static batching: Processes fixed-size input batches, ideal for offline inference
2. 2
   Dynamic batching: Groups inputs of varying sizes adaptively
3. 3
   Continuous batching: Also known as in-flight batching, replaces completed requests with new ones without pausing ongoing decodes

An effective approach depends usually on your task-specific requirements. Static batching works well for predictable offline tasks, while continuous batching excels in online serving environments with varying request patterns.

Parallelization techniques

Parallelization distributes computations across hardware resources:

• Speculative inference: Pre-generates tokens using a smaller model to reduce waiting time
• Pipeline parallelism: Splits inference into stages across devices
• Tensor parallelism: Distributes weight tensors across multiple GPUs

For models with billions of parameters, implementing tensor or pipeline parallelism becomes essential as they enable processing that would be impossible on a single device.

Memory optimization approaches

Memory optimization is critical since LLM inference is typically memory-bound rather than compute-bound. Effective techniques include:

• FlashAttention: Reduces memory complexity from quadratic to linear in sequence length
• PagedAttention: Dynamically allocates memory for KV cache to reduce fragmentation
• Parameter offloading: Stores model parameters in server memory and prefetches before execution

These optimizations can yield up to 20x reduction in memory usage for long sequences while maintaining speed.

Inference optimization requires balancing throughput, latency, memory usage, and accuracy based on specific use case requirements. The most effective implementation combines multiple techniques tailored to your deployment environment and performance goals. With these optimization approaches in mind, we can now synthesize this knowledge into strategic guidance for product leaders.

Real-world performance impacts

Optimization techniques yield significant real-world performance improvements when properly implemented. Benchmark data reveals the magnitude of potential gains:

Quantization:

• 8-bit quantization typically delivers 1.8-2.2x speedup with minimal quality loss
• 4-bit quantization can achieve 3-4x speedup but requires careful quality monitoring

KV Caching:

• Reduces computation by 65-80% during token generation
• Most effective for longer responses where redundant computation would otherwise accumulate

Continuous Batching:

• Improves throughput by 2.5-3.5x compared to static batching
• Particularly valuable for services with variable request patterns

Combined Approaches:

• Applying quantization, KV caching, and continuous batching together can yield 5-10x overall performance improvements
• One enterprise reduced inference costs from $75,000 to $32,000 monthly through these combined techniques

These performance gains directly translate to lower operational costs and improved user experiences through faster response times.

Conclusion 

Optimizing LLM inference requires balancing multiple techniques tailored to your specific deployment context. The approaches outlined demonstrate how hardware selection and software optimization work together to create high-performance, cost-effective inference systems.

The most successful implementations combine complementary strategies:

• Quantization reduces memory requirements, enabling deployment on more affordable hardware
• KV caching minimizes redundant computation during token generation
• Batching maximizes hardware utilization across multiple simultaneous requests
• Parallelization distributes workloads efficiently across available resources

Together, these techniques can yield 5-10x performance improvements while maintaining response quality. The multiplicative effect creates transformative efficiency gains beyond what any single approach could achieve.

When evaluating optimization options, consider these implementation principles:

• Start with right-sized hardware for your specific model and throughput requirements
• Implement quantization with careful quality benchmarking
• Add KV caching and memory optimizations for improved token generation
• Apply appropriate batching strategies based on your request patterns

The field continues evolving rapidly. Emerging techniques like speculative inference and hardware-specific optimizations promise further efficiency gains. By applying the frameworks presented here, product leaders can implement inference systems that deliver exceptional performance while controlling costs.