CorePick — AI Inference Performance Intelligence

Find the right model, hardware, precision and runtime for your performance and cost targets. Vendor-neutral AI model-to-hardware profiling, inference benchmarking, kernel flamegraph analysis, and automated deployment code generation for NVIDIA TensorRT, ONNX Runtime, Apple CoreML, and Qualcomm QNN.

Key Capabilities & Inference Engineering Tools

  • Interactive Roofline Model: Analyze operational intensity (FLOPs/byte), memory bandwidth limits, and compute saturation across NVIDIA H100, RTX 4090, A100, L40S, Apple M-Series, and Qualcomm Snapdragon X Elite.
  • Multi-GPU Tensor Parallelism (TP) Sizer: Calculate exact VRAM sharding, all-reduce communication overhead, and interconnect bandwidth scaling across NVLink and PCIe Gen5.
  • Hugging Face config.json Parser: Ingest any transformer checkpoint config to calculate exact tensor dimensions, attention heads, GQA ratios, and precision memory footprints in FP16, FP8, INT8, and INT4.
  • KV-Cache & Context Window Sizer: Model token memory growth, PagedAttention fragmentation, and FP8/INT4 KV compression for ultra-long context windows up to 128k tokens.
  • Quantization Accuracy & Drift Simulator: Audit perplexity drift, KL divergence, SNR, and activation outlier clamping across AWQ, GPTQ, SmoothQuant, and FP8.
  • Production Container & Helm Chart Generator: Export hardened Dockerfiles, vLLM / Triton / TGI configurations, and Kubernetes manifests with Prometheus metrics.

Supported Models & Hardware Silicon

Benchmark and optimize Llama-3.1 (8B, 70B, 405B), DeepSeek-V2/V3, Mistral, Qwen 2.5, Gemma 2, Whisper, and Stable Diffusion across NVIDIA Blackwell B200, H100 SXM5, A100 80GB, L40S, RTX 4090, AMD Instinct MI300X, Apple M3 Max, and Qualcomm NPU.