Dedicated clusters now available for Q1 reservation

Inference infrastructure
for AI engineering teams.

Run production LLMs and diffusion models on dedicated bare-metal NVIDIA and AMD silicon. Zero multi-tenant queueing, deterministic latency, and 70% to 85% lower cost per million tokens.

Cost Reduction

70% – 85%

vs. Closed API Tokens

Time to First Token

< 14.2 ms

vLLM & TensorRT-LLM

Tenancy

100% Dedicated

Physical silicon locks

Base Reservation

$650

Locks production pod

Hardware Fleet

Dedicated Bare-Metal Clusters

Every node is bare-metal silicon allocated directly to your workloads. No shared hypervisors or noisy neighbor throttling.

AMD INSTINCT MI300X 8-WAY

AMD Instinct MI300X Pods

192GB HBM3 per accelerator (1.5TB total memory per node) with 5.3 TB/s memory bandwidth. Built for massive open-weight MoE models like DeepSeek V3 and Llama 3.3 70B.

Reservation: $650

NVIDIA HGX H100 SXM5

8x H100 80GB SXM5 Clusters

Full 3.35 TB/s memory bandwidth per GPU. 640GB total unified HBM3 pool connected via 900 GB/s NVLink 4.0 for ultra-low latency token generation.

Reservation: $1,200

HIGH DENSITY RTX 4090

8x RTX 4090 24GB Density Pods

The absolute sweet-spot for running 8B to 32B models, Whisper real-time audio transcription, and Stable Diffusion XL image pipelines at 80% lower cost.

Reservation: $650 (Base)

US-EAST (VIRGINIA) COLO

Tier 4 Certified Facilities

Direct peering with AWS, GCP, and Cloudflare via 400Gbps cross-connects with 2N electrical redundancy and 99.99% uptime guarantee.

Transit: 400Gbps RoCEv2

4U RACK TOPOLOGY

Dedicated Chassis Topology

Dual AMD EPYC 9654 processors, 1.5TB DDR5 ECC memory, and Gen 5 NVMe scratch storage capable of 14GB/s model weight loading.

PCIe: Gen 5.0 x16

ASYNC UTILITY TIER

Embedding & Offline Indexing

Cost-effective auxiliary compute nodes for RAG vector indexing, asynchronous embeddings, and background evaluation pipelines.

Reservation: $650

Engine Architecture

How Drizzle Bare-Metal Inference Works

High-performance LLM inference is not a generic compute problem—it is primarily a memory bandwidth and KV-cache management challenge. Here is how our custom bare-metal runtime eliminates bottlenecks:

PagedAttention & Zero Memory Waste

In standard inference engines, up to 60–80% of GPU VRAM is wasted due to internal and external fragmentation of dynamic Key-Value (KV) caches. Drizzle implements PagedAttention (inspired by virtual OS memory pages), allowing non-contiguous allocation and unlocking up to 4x higher concurrent request batching without out-of-memory errors.

Continuous Iteration-Level Batching

Traditional naive batching locks an entire batch until the longest output sequence finishes generating. Drizzle's runtime runs at the iteration level—as soon as one prompt finishes its token stream, a new incoming request is immediately injected into the next forward pass. This prevents GPU idle cycles and maintains flat TTFT latency under traffic spikes.

Quantized FP8 / AWQ Weight Acceleration

Memory bandwidth is the ultimate ceiling during the autoregressive token generation phase. By utilizing hardware-accelerated FP8 Tensor Cores (NVIDIA H100 / AMD MI300X) and Activation-aware Weight Quantization (AWQ), we double throughput while preserving 99.8% of FP16 model perplexity and reasoning accuracy.

White-Glove Infrastructure Assistance

You Get a Designated Migration Engineer

Migrating from closed frontier APIs to dedicated bare-metal inference should not require your core engineering team to become low-level CUDA experts. The moment you reserve your cluster, we assign a Primary Migration Engineer directly to your technical team:

Shared Slack / Discord War Room: Real-time asynchronous communication with the engineer who configured your silicon.

Custom Weights & LoRA Flashing: We help quantize, flash, and benchmark your fine-tuned model checkpoints onto the cluster.

Parallel Production Shadowing: Route 5% of your live traffic first to verify latency, token fidelity, and error rates before full cutover.

OpenAI SDK Compatibility: Switch your API base URL in minutes without rewriting prompt pipelines or routing logic.

Zero downtime cutover guarantee

Cost Arbitrage

Calculate Your Annual Savings

Compare the cost of proprietary multi-tenant cloud APIs against reserved bare-metal Drizzle clusters.

Model Architecture

Monthly Request Volume

50 Million Tokens

5M 100M 250M 500M+ Tokens

Flagship API Spend: $2,500 / mo

Drizzle Dedicated Pod: $475 / mo

Estimated Annual Savings

$24,300 / yr

Drop-in compatibility with existing OpenAI SDKs

Zero code refactoring required. Simply redirect your base URL.

# Point existing client to your dedicated Drizzle cluster
from openai import OpenAI

client = OpenAI(
    base_url="https://api.drizzleai.com/v1",  # Dedicated Bare-Metal Endpoint
    api_key="drz_live_enterprise_9x024k1a"
)

response = client.chat.completions.create(
    model="llama-3.3-70b-instruct",
    messages=[{"role": "user", "content": "Run high-concurrency production inference."}],
    stream=True
)

Engineering Support

Direct NOC & System Engineer Access

We don't route technical inquiries through generic ticket desks or automated bots. Every customer has immediate direct access to our systems infrastructure team.

PRIMARY INQUIRY & SUPPORT EMAIL support@drizzleai.online

< 15 min response

MIGRATION ASSISTANCE SLA

Your assigned primary engineer will conduct architecture review, memory layout audits, and stress testing alongside your team until your inference workload is 100% stable.

Speak With An Infrastructure Engineer

Have custom latency goals or a specific cluster footprint? Drop us a note:

Inference infrastructure for AI engineering teams.