When deploying large language models (LLMs), understanding how inference workloads interact with your GPU is crucial for controlling costs and maximizing performance.
This guide breaks down what happens during inference and offers practical strategies for optimization.
🧠 The Basics of LLM Inference
LLM inference is the process of generating tokens based on a user’s input. It follows a sequential pipeline:
- Tokenization – Text is converted into tokens the model understands
- Prefill Stage – The attention mechanism processes the entire input prompt
- Token Generation – The model generates one token at a time
- Caching – Each token is stored and influences future generations
Key insight 🤔: LLMs must retain all previous tokens during generation to ensure contextual coherence. This consumes significant GPU memory.
🖥️ What Happens on Your GPU
As an LLM processes a prompt, your GPU performs the following:
- Converts text into token IDs using the model’s vocabulary
- Transforms token IDs into embedding vectors (matrices)
- Applies the model’s weights to process these embeddings
- Uses the attention mechanism to determine token relevance
🔑 The Role of the KV Cache
The Key-Value (KV) cache stores past computations, so the model doesn’t reprocess the entire sequence each time it generates a token. While it significantly boosts speed, it also uses a lot of GPU memory.
🧮 GPU Memory Breakdown
GPU memory is generally split between:
- Model Weights – ~2GB per 1B parameters (in FP16 precision)
- KV Cache – Grows with the number of tokens generated
Example:
- An 8B model (FP16) → 16GB for weights
- On a 20GB GPU → Leaves ~4GB for KV cache and everything else
📏 Measuring Performance
Track these key metrics to evaluate and optimize inference:
- Time to First Token – Measures the speed of prompt processing
- Token-to-Token Latency – Speed of generating each subsequent token
- Total Generation Time – Time from start to final output
✨ Common Query Patterns:
- Long input, short output – Heavy prefill, light generation
- Long input, long output – Most resource-intensive
- Short input, long output – Fast initial response, longer generation
- Short input, short output – Fastest overall scenario
🛠️ Optimization Strategies
To improve efficiency and reduce costs, consider the following:
- 🔻 Quantization – Convert FP16 → FP8 → FP4 to reduce memory usage
- 🧵 In-Flight Batching – Serve new requests as others complete
- 🔗 Tensor Parallelism – Distribute models across multiple GPUs
- 💾 Quantized KV Cache – Store more tokens within memory limits
- ⚙️ Specialized Engines – Tailor models to frequent query patterns
- 📊 Seasonal Engines – Adjust infrastructure to traffic trends
🧰 Tools for Implementation
Several tools can streamline and optimize your LLM deployments:
- TRT-LLM – NVIDIA’s compiler for high-performance LLM inference
- Triton – Open-source inference server for flexible deployment
- NVIDIA Inference Microservice (NIM) – Enterprise-level deployment solution
🔭 Looking Forward
Precision formats are evolving toward even more memory-efficient inference:
Understanding your inference workload patterns and deploying the right optimization strategies can significantly reduce compute costs and improve responsiveness.
💡 Scale Efficiently with BlackSkye
BlackSkye’s decentralized GPU marketplace offers cost-effective, high-performance compute ideal for LLM inference workloads.
Benefits:
- 🔄 Real-time pricing & transparent performance
- 💰 Lower infrastructure costs
- 📈 Scalable access to GPUs on demand
By tapping into BlackSkye, organizations can scale inference dynamically, without upfront infrastructure investments.