What is FP8 Quantization?
FP8 Quantization uses 8-bit floating point format providing middle ground between INT8 and FP16, with hardware acceleration on modern GPUs. FP8 offers efficient inference with better dynamic range than integer quantization.
This model optimization and inference term is currently being developed. Detailed content covering implementation approaches, performance tradeoffs, best practices, and deployment considerations will be added soon. For immediate guidance on model optimization strategies, contact Pertama Partners for advisory services.
FP8 quantization doubles inference throughput on supported hardware while halving memory consumption, enabling companies to serve twice the traffic on identical GPU infrastructure. The memory reduction allows deploying larger models on single GPUs that previously required expensive multi-GPU configurations, saving USD 5K-20K per serving node. For companies scaling AI inference to production volumes, FP8 quantization frequently determines whether unit economics support profitable deployment or require unsustainable infrastructure spending.
- 8-bit floating point (vs. 8-bit integer).
- Better dynamic range than INT8.
- Hardware support on H100, Ada Lovelace GPUs.
- Used in training (FP8 mixed precision) and inference.
- Minimal quality loss vs. FP16.
- Growing adoption with hardware support expansion.
- Verify hardware support before adopting FP8 since only NVIDIA H100 and newer GPUs provide native FP8 tensor core acceleration that delivers actual throughput improvements.
- Benchmark FP8 accuracy degradation on your specific model and task combination because sensitivity to reduced precision varies significantly across architectures and application domains.
- Use FP8 for inference workloads first where accuracy requirements are well-understood before applying quantization to training pipelines where numerical stability impacts convergence reliability.
- Implement mixed-precision strategies combining FP8 computation with higher-precision accumulation to maintain numerical accuracy while capturing most of the throughput and memory benefits.
Common Questions
When should we quantize models?
Quantize for deployment when inference cost or latency is concern and minor quality degradation is acceptable. Test quantized models thoroughly on your use cases. 8-bit quantization typically has minimal impact, 4-bit requires more careful evaluation.
How do we choose inference framework?
Consider model format compatibility, hardware support, performance requirements, and operational preferences. vLLM excels for high-throughput serving, TensorRT-LLM for low latency, Ollama for local deployment simplicity.
More Questions
Batching increases throughput but raises per-request latency. Optimize for throughput in offline batch processing, latency for interactive applications. Continuous batching balances both for variable workloads.
References
- NIST Artificial Intelligence Risk Management Framework (AI RMF 1.0). National Institute of Standards and Technology (NIST) (2023). View source
- Stanford HAI AI Index Report 2025. Stanford Institute for Human-Centered AI (2025). View source
Inference in AI is the process of running a trained model to generate outputs -- such as predictions, text responses, image classifications, or recommendations -- from new input data. It is the production phase of AI where the model delivers value to end users, as opposed to the training phase where the model learns.
Inference is the process of using a trained AI model to make predictions or decisions on new, unseen data in real time, representing the production phase where AI delivers actual business value by processing customer requests, analysing images, generating text, or making recommendations.
Repetition Penalty reduces probability of previously generated tokens to discourage repetitive text, improving output diversity. Repetition penalties are essential for coherent long-form generation.
Stop Sequences are tokens or strings that trigger generation termination when encountered, enabling control over output length and format. Stop sequences are critical for structured generation and chat applications.
Structured Generation constrains model outputs to match specified formats (JSON, XML, grammars) through constrained decoding. Structured generation ensures parseable, valid outputs for integration with systems.
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