Skill UI

Guidance for training large multimodal MoE models with miles, covering FP8/INT4 quantization-aware regimes, train-inference alignment, speculative RL throughput tricks, and enterprise stability practices for production deployments.

Guides teams on reducing latency and improving throughput when integrating Mistral AI by selecting low-latency models, enabling streaming, caching deterministic requests, trimming prompts, and managing request concurrency.

Speeds up transformer attention with Flash Attention techniques, offering 2-4× throughput gains and 10-20× memory reduction. Ideal for long-context models on PyTorch (native or flash-attn lib), H100 FP8, and sliding window scenarios to resolve GPU memory bottlenecks and improve inference.

Deploy LLMs with vLLM to maximize throughput and minimize latency through PagedAttention, continuous batching, quantization, and tensor parallelism for production APIs or batch inference on constrained GPUs.

Speeds up LLM inference by combining speculative decoding with Medusa multi-heads and lookahead Jacobi techniques, letting production services hit 1.5-3.6× throughput while keeping quality intact on limited hardware.

Optimizes LLM inference on NVIDIA GPUs, delivering 10-100× faster throughput, sub-10ms latency, and multi-GPU scaling with FP8/INT4 quantization, in-flight batching, and production-ready serving for real-time deployments.