Zhang Alpha Research architects end-to-end AI infra — bare-metal GPU clusters, Kubernetes orchestration, blazing inference stacks, and the AI systems that run on them. Train faster. Serve cheaper. Scale without drama.
One partner for the full accelerated stack — silicon, networking, orchestration, models, and apps. No quota tickets. No 12-month procurement purgatory.
Your own H100/H200 island: bare metal, NVLink, InfiniBand, NVMe, Slurm or Kubernetes. Hourly bursts or reserved capacity with white-glove bring-up in <48h.
Build your cloud →vLLM, TensorRT-LLM, TGI tuned for throughput. Autoscaling, prompt caching, speculative decoding. 70B at 4,000 tok/s for $0.28 / 1M tokens.
Optimize serving →Distributed training that actually scales: NCCL-tuned, checkpoint-safe, spot-tolerant. SFT / LoRA / DPO pipelines for Llama, Mistral, Qwen.
Train faster →GPU-aware K8s: time-slicing, MIG, Karpenter autoscaling, KubeFlow, Ray. GitOps deploys, observability, and FinOps guardrails included.
See platform →Production copilots grounded on your data — hybrid search, reranking, citations, tool-using agents with approvals. Eval-gated, hallucination-resistant.
Launch AI apps →Real-time transcription, call copilots, visual QA — GPU-streamed with 800ms latency. 31k hours/month on a single A100 rack.
Scale multimodal →Model monitoring, drift detection, red-teaming, paging, cost alerts. We run it — or hand you the runbooks and train your team.
Stay reliable →1PB NVMe, S3-compatible lakehouse, vector indexes that feed both BI and LLMs. Ingest once, serve everywhere.
Unify data →| Accelerator | VRAM / Fabric | From |
|---|---|---|
| H100 SXM 8× | 640GB HBM3 • IB 3.2Tbps | $2.49/hr |
| H200 NVL 8× | 1.1TB HBM3e • IB | $3.49/hr |
| A100 80GB 8× | 640GB • NVLink | $1.99/hr |
| L40S | 48GB • PCIe | $1.19/hr |
| RTX 4090 | 24GB • PCIe | $0.69/hr |
PyTorch 2.4, CUDA 12.4, vLLM, Ollama images. SSH / Jupyter / VS Code in <15 min.
Configure cluster →At 10M tokens/day, OpenAI bills ~$15k/mo. Same workload on two L40S with distilled 8B: ~$1.6k/mo. We help you cross that chasm — distill, quantize, and serve on your own accelerated fabric.
Inference Factory
2× L40S + vLLM + Redis cache + Langfuse. 70B-class quality via distilled 8B. 3.2s p95, $0.08/1M tokens.
Training Pod
32× H100, IB, WeKa-style NVMe, checkpoint to S3 every 15 min. 2.3× faster than previous cloud for genomics transformer.
Sovereign RAG Cloud
On-prem A100s + hybrid search + permission filters. 12M legal docs, cited answers, 94% attorney approval.
Workload profiling, GPU sizing, network + storage plan. Fixed quote in 48h.
Bare metal racked, K8s/Slurm installed, benchmarks (MLPerf-style) shared.
vLLM / TRT-LLM tuning, distillation, eval gates. Latency & $/token proven.
24/7 SRE, upgrades, scaling. Or enable your team and step back.
Single nodes in hours, 8–32 GPU IB clusters in 24–48h from live inventory. Larger / reserved builds in 1–3 weeks. Tell us your dates — we hold capacity with a deposit.
Both. VMs for elasticity, bare metal for max NCCL bandwidth and compliance. Same API, same support.
Yes. We ship reference Kubernetes + Slurm stacks to your cage, AWS, GCP, or ours, with private networking, SSO, and audit logs.
If you exceed ~5M tokens/day or need data control, self-hosted inference usually wins 5–10× on cost. We benchmark your prompts and show the math before you migrate.
You own code, fine-tuned weights, and data. Hardware is rented or customer-owned — your choice. No lock-in scripts.
Send your model, tokens/day, and latency target. We return sizing, architecture diagram, and $/1M token estimate.