High-Performance Compute Fabric

Composable kernels, distributed runtimes, and observability for large GPU fleets.

Kernel Optimization

FlashAttention variants, fused ops, and mixed-precision GEMMs tuned to your workloads.

Triton, CUDA, and HIP kernels

FP8/INT4 quantization pipelines

Distributed Systems

Elastic training, Ray/DeepSpeed orchestration, and continuous batching inference.

InfiniBand & RoCE optimization

Resilient checkpointing and recovery

Observability & Reliability

DCGM, Prometheus, and custom telemetry for throughput, utilization, and thermal headroom.

GPU Health

Throughput

Efficiency

Compiler to Cluster

Engineering the entire compute fabric

From MLIR graph rewrites to fleet-level observability, we harden every layer that carries your model.

Compiler & Kernel Engineering

Specialized MLIR passes and Triton kernels collapse latency-critical stages into single sweeps.

Custom MLIR pipelines for attention, mixture-of-experts, and sparse operators
Triton autotuning across H100, MI300, and Grace Hopper fleets
Numerics validation from FP8 to INT4 with golden signal harnesses

Runtime Orchestration

Ray, DeepSpeed, and Kubernetes fabrics are co-designed with storage, checkpointing, and service SLAs.

Elastic multi-node training with topology-aware sharding
Throughput-aware inference batching with preemption-safe fallbacks
Zero-downtime upgrades via staged rollout playbooks

Retrieval & Data Fabric

Vector search, streaming transforms, and policy-guarded retrieval keep AI-RAG pipelines grounded.

Sharded embedding services with ANN indexes tuned per modality
Latency budgets enforced through CUDA-aware caching tiers
Trust layers: redaction, audit, and retention by jurisdiction

Delivery Playbooks

A proven pipeline for peak performance

Each engagement is mapped to a repeatable pipeline so you can forecast improvements week by week.

Profiling & Baselines

Nsight, ROCm SMI, and in-house profilers capture kernel stall reasons and interconnect contention.

Heatmaps for SM occupancyNetwork saturation tracesTelemetry gap analysis

Compiler Transformations

We author MLIR pass pipelines and schedule-aware graph rewrites that collapse launch overhead.

Launch count reductionsAchieved occupancy deltasCache hit improvements

Kernel & Triton Optimization

Autotuned Triton kernels, fused memory staging, and async copy orchestration close the throughput gap.

Effective TFLOPs per GPUTensor core utilizationDRAM vs. HBM balance

Distributed Execution

Ray, DeepSpeed, and NCCL/RCCL topologies are hardened for long-context inference and hybrid workloads.

Tokens/sec at ultra-long context lengthsRecovery time objectivesCross-region bandwidth usage

Solution Areas

Purpose-built accelerators for modern AI

We pair kernel work with architectural patterns that ship to production across industries.

Long-Context LLM Acceleration

Mixture of sliding-window attention, ring attention, and tensor parallelism unlock ultra-long sequences.

Ultra-long context decoding with adaptive KV cache eviction
Sequence parallelism for massive GPU pods
Guardrails for latency-sensitive copilots

Retrieval-Augmented Generation Fabric

Multimodal RAG pipelines blend curated corpora, online search, and policy-compliant responses.

Feature stores synced to streaming warehouses
Memory-optimized embedding refresh jobs
Human-in-the-loop validation surfaces

Fleet Efficiency & Reliability

Holistic observability and repair automations maximize GPU availability while honoring compliance envelopes.

Closed-loop alerting with DCGM and Prometheus
GPU-aware auto scaling playbooks
SLO dashboards for training & inference

Ready to accelerate?

Connect with ClaireChains engineers to co-design your next training or inference milestone.