Reduce LLM Inference Latency on GPUs: A Technical Guide

Before optimizing, you must distinguish between the two primary latency metrics. Time to First Token (TTFT) measures how quickly the model starts responding, which is critical for interactive applications. Time Per Output Token (TPOT) measures the speed of subsequent tokens, determining the overall reading speed. According to a 2025 report from Artificial Analysis, the industry benchmark for high-performance Llama 3.1 70B inference is a TTFT under 200ms and a TPOT under 30ms.

Latency bottlenecks typically fall into two categories: compute-bound and memory-bound. During the prefill phase (generating the first token), the GPU is often compute-bound as it processes the entire input prompt in parallel. During the decoding phase (generating subsequent tokens), the GPU becomes memory-bound because it must fetch model weights and the KV cache from HBM (High Bandwidth Memory) for every single token generated.

• Prefill Phase

  Highly parallel, benefits from raw TFLOPS.

• Decoding Phase

  Sequential, benefits from high memory bandwidth (GB/s).

• KV Cache

  Grows with context length, leading to Out-of-Memory (OOM) errors if not managed.

The choice of inference engine is the most significant software decision you will make. Standard PyTorch implementations are insufficient for production. Modern engines like vLLM and NVIDIA TensorRT-LLM use a technique called PagedAttention to manage the KV cache. This prevents memory fragmentation and allows for much higher batch sizes, which indirectly reduces latency by increasing throughput.

In March 2026, the release of NVIDIA Dynamo 1.0 provided a standardized orchestration layer that bridges the gap between raw compute and high-level APIs. An open-stack approach combining vLLM with NVIDIA Dynamo ensures customer portability. Unlike black-box proprietary engines, this stack allows you to maintain control over your model weights while achieving performance parity with specialized API providers. By using continuous batching, these engines process new requests immediately rather than waiting for an entire batch to finish, reducing average wait times by up to 70% in high-traffic scenarios.

Quantization reduces the bit-precision of model weights, which decreases the amount of data the GPU must move from memory to the processing cores. Moving from FP16 (16-bit) to FP8 (8-bit) effectively doubles the memory bandwidth of an H100 GPU. According to NVIDIA's 2025 technical documentation, FP8 inference on Hopper architecture provides a 2x to 4x throughput increase with negligible loss in model accuracy.

Common quantization methods include:

1. AWQ (Activation-aware Weight Quantization)

   Protects the most important weights to maintain accuracy at 4-bit precision.

2. FP8

   The current standard for H100 and B200 GPUs, offering a perfect balance of speed and precision.

3. INT8

   Older but reliable for previous-generation hardware like the A100.

For teams running large models like Llama 3 405B, quantization is not optional; it is a requirement to fit the model within the VRAM of a single 8-GPU node. Reducing the memory footprint also allows for larger KV caches, enabling longer context windows without a linear increase in latency.

Speculative decoding is a powerful technique where a smaller, faster "draft" model predicts the next few tokens, which are then verified by the larger "target" model in a single forward pass. If the draft model is correct, you generate multiple tokens in the time it would usually take to generate one. This can improve TPOT by 2x to 3x without changing the underlying model's weights.

However, speculative decoding requires careful implementation. If the draft model's acceptance rate is low (below 50%), the overhead of verification can actually increase latency. We recommend using a draft model from the same family as your target model, for example, using a Llama 3 8B model to speculate for a Llama 3 70B model. This ensures a higher alignment in token distribution and better performance gains.

Network latency often negates GPU-level optimizations. For European startups, hosting models in US-based data centers adds 100ms to 150ms of round-trip time (RTT) due to physical distance. This is a deal-breaker for real-time applications like voice AI or interactive coding assistants. Furthermore, EU-regulated teams in healthcare or manufacturing face strict GDPR and AI Act requirements that mandate data residency within Europe.

Lyceum provides a sovereign alternative by hosting all infrastructure in European data centers. By provisioning VMs across a network of supply-side partners, you can deploy inference endpoints close to your end-users. Our Pythia AI Scheduler further optimizes costs by predicting VRAM requirements and selecting the most efficient GPU for your specific workload, often resulting in 30-34% savings compared to unmanaged hyperscaler instances. Using an OpenAI-compatible API, you can transition from US-based providers to EU-sovereign infrastructure with zero code changes, ensuring both compliance and low-latency performance.