Bare-metal GPU servers tuned for LLM serving — vLLM, TGI, or TensorRT-LLM pre-installed, continuous batching enabled, KV cache sized for your context length, and tail-latency SLOs we actually defend.
L40S FP8 doubles serving density vs FP16. B200 FP4 doubles it again. We help you quantize without losing quality.
vLLM PagedAttention and TensorRT-LLM in-flight batching — 4–8x throughput vs static-batch baselines.
Match the GPU's HBM to your context length. H200's 141GB is purpose-built for 32K–128K-token serving.
Best $/token for 7B–70B models in FP8. Common stack: vLLM + AWQ/GPTQ quantization + Triton routing.
When p99 TTFT and tokens-per-second matter, NVLink + HBM3(e) + FP8 wins. H200 doubles concurrency at the same latency.
NVIDIA GPU Operator + KEDA + your traffic shape. Scale to zero on cold paths, warm pre-pull on traffic spikes.
Prometheus + Grafana dashboards for token throughput, TTFT, TPS, KV cache hit rate, queue depth. Hooked into PagerDuty on request.
Short answer: L40S for cost-optimized serving of <70B models in FP8/INT8; H100 PCIe for low-latency serving of 70B–200B dense models; H200 SXM5 when long context (32K+) or large KV cache dominates; B200 (when available) for FP4 inference at maximum density.
Yes — all three. We pre-install your chosen serving stack with NVIDIA Triton, vLLM, HuggingFace TGI, or TensorRT-LLM. Auto-prefix-caching, continuous batching, and speculative decoding all supported.
Yes. Kubernetes with the NVIDIA GPU Operator, KEDA-based autoscaling, and a shared model registry. Quoted per deployment based on tail-latency SLO and peak QPS.
Local NVMe scratch + warm replicas keep cold-start under 30s for 70B models in FP8. Persistent volumes hold pre-quantized weights so you're not re-quantizing on every restart.
Tell us model, context length, peak QPS, and latency SLO — we'll spec the right hardware and stack.