AI model too slow in production.Optimised this session.

Yes. Latency reduction from 8 seconds to under 2 seconds is achievable in a Starter session for most setups. The engineer diagnoses the latency bottleneck: if you are using the OpenAI or Anthropic API, the bottleneck is usually token count (the prompt is too long, or you are requesting too many output tokens for the use case fix: prompt compression and output length constraints). If you are self-hosting, the bottleneck is usually inference configuration not enabling continuous batching in vLLM, running on undersized GPU instances, or not using quantisation (4-bit or 8-bit GPTQ reduces memory pressure and increases throughput). The engineer implements the appropriate optimisation and measures before-and-after P50 and P99 latency on representative queries.

Yes. Migration from OpenAI API to self-hosted inference is a Full Day to Sprint Pack engagement depending on model selection and traffic volume. The engineer handles: model selection and benchmarking (which open-source model matches your quality bar at your required latency and cost target), GPU infrastructure provisioning (Lambda Labs, Vast.ai, CoreWeave, or your cloud provider's GPU instances), vLLM or TGI deployment and configuration (tensor parallelism for multi-GPU, continuous batching configuration, KV cache sizing), load balancer setup for high availability and traffic distribution across multiple GPU instances, API compatibility layer so your existing application code can switch from the OpenAI SDK to your self-hosted endpoint with minimal changes, and monitoring integration (Prometheus metrics for throughput, latency percentiles, and GPU utilisation).

Yes. GPU autoscaling for inference is a Full Day session. The engineer implements: a Kubernetes-based autoscaling setup using KEDA (Kubernetes Event-Driven Autoscaling) with a custom metric requests per GPU or queue depth in your load balancer as the scaling trigger, minimum and maximum GPU instance counts configured for your cost and availability requirements, a scale-down cooldown period to prevent rapid instance churn (GPU startup takes 2-3 minutes so premature scale-down is expensive), a warm pool of pre-initialised instances to prevent cold-start latency during sudden traffic spikes, and health check configuration so Kubernetes replaces unresponsive GPU instances automatically. The engineer tests the autoscaling behaviour under simulated load before the session ends.

The choice depends on your performance requirements, model, and operational preferences. vLLM is the best choice for maximising throughput and supporting the widest range of models its PagedAttention algorithm dramatically reduces GPU memory fragmentation, enabling 2-4x more concurrent requests than naive implementations. It supports continuous batching and prefix caching natively. TGI (Text Generation Inference by HuggingFace) is simpler to deploy and has better HuggingFace Hub integration good if you want to serve models directly from the Hub without a custom setup. BentoML is the best choice if you need to serve multiple models or modalities from a single serving layer, build complex multi-step inference pipelines, or need a managed cloud serving platform (BentoCloud). PM recommends based on your specific model, expected concurrency, and infrastructure team's operational familiarity.

The techniques that consistently achieve 70-80% latency reduction, in order of impact: first, speculative decoding using a small draft model to predict tokens and the large model to verify them, cutting the number of full forward passes by 3-4x for typical outputs; second, KV cache quantisation storing the key-value attention cache in INT8 or FP8 instead of FP16, doubling the effective context length at the same GPU memory budget and reducing memory bandwidth bottlenecks; third, continuous batching processing multiple requests simultaneously rather than sequentially, which improves GPU utilisation from 20-30% to 70-80%; fourth, model quantisation AWQ or GPTQ 4-bit quantisation reduces model weights by 4x, enabling larger batch sizes on the same GPU; fifth, prompt caching caching the KV state of a shared system prompt across requests, eliminating the prefill cost for every conversation turn.