From training clusters to edge terminals, every system ships pre-validated, fully observable, and ready to integrate. Designed, configured, and supported end-to-end by Nexus Forge engineers.
Starting prices are indicative for a base configuration. Final pricing depends on specification, region, and current supply — contact us for a tailored quote.
Dense GPU systems for foundation-model pre-training and fine-tuning.
Forge GX-8 Cluster
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8× NVIDIA H200 · 1.4TB HBM3e · NVLink 4
Flagship dense training node engineered for foundation-model fine-tuning, RLHF, and long-context multi-modal pipelines. Ships pre-validated against MLPerf reference runs.
70B+ parameter fine-tunes, RLHF, multi-modal training
Forge GX-4 Cluster
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4× NVIDIA H100 · 640GB HBM3 · NVLink
Mid-scale training node for domain adaptation, continued pre-training, and embedding model development — a balanced footprint for teams growing past single-GPU experimentation.
900 GB/s NVLink switch · NVSwitch backplane
Air-cooled 4U chassis · standard rack-ready
Dual 48-core AMD EPYC Genoa · 1TB DDR5 ECC
30TB NVMe Gen5 · dual 200GbE
BMC + redundant Titanium PSUs
Ubuntu 24.04 LTS · CUDA 12.6 · NGC stack
Ideal for
7B–34B fine-tunes, embedding model training, LoRA at scale
Forge GX-Pod 32
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32× H200 · 400GbE InfiniBand · 5.6TB HBM
Pre-validated 4-node training pod with non-blocking NDR interconnect, shared parallel storage, and rack-level liquid cooling — delivered as a turnkey unit with scheduler and observability online.
Foundation-model pre-training, large-scale RLHF, multi-tenant research
Inference Servers
Low-latency serving nodes for agents, RAG, and real-time generation.
Forge IS-2 Inference Node
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2× NVIDIA L40S · 192GB RAM · 100GbE
Cost-efficient 2U serving node for agentic workflows, RAG pipelines, and mid-sized model APIs — tuned out of the box for sub-100ms time-to-first-token and high concurrency.
13B–34B model serving, embeddings, RAG, agent backends
Forge IS-8 Inference Node
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8× NVIDIA H100 NVL · 188GB HBM3
High-throughput 4U serving node engineered for long-context generation, multi-tenant model hosting, and production-grade chat platforms with strict latency SLOs.
Compact 1U serving node for small models, classifiers, vision pipelines, and edge gateways — silent enough for office deployment, dense enough to rack.
Local fine-tuning, prototyping, on-prem inference dev
Inference Server — Forge R4000 (4U)
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8× NVIDIA L40S · 384GB GDDR6 ECC · FP8 native
Dense 4U inference server for high-throughput serving, batched embeddings, and multi-tenant model hosting.
2× AMD EPYC 9354 (32C / 64T) · 512GB DDR5 ECC
15TB NVMe array (RAID-10) · dual 10 GbE + BMC
Redundant 1600W Titanium PSU
Ubuntu Server 24.04 LTS · vLLM + Triton ready
Ideal for
Production inference, batched embeddings, multi-tenant serving
Edge & Embedded
Ruggedized systems for on-device perception, robotics, and field deployment.
Forge Edge Terminal
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Jetson AGX Orin 64GB · 2TB NVMe · IP65
Rugged edge unit for on-device perception, sensor fusion, and autonomous decisioning — sealed for outdoor, vehicle, and industrial deployment without an enclosure.
275 TOPS AI compute · 12-core Arm Cortex-A78AE
-40°C to 70°C operating · IP65 sealed aluminum chassis
Plug-in 2U gateway for multi-camera vision pipelines with on-prem retention, hardware-accelerated decode, and role-based access — no cloud round-trip required.
NVIDIA L4 GPU · 64GB DDR5 · 16TB enterprise NVMe
8× PoE++ camera ports · ONVIF + RTSP ingest
Frigate, DeepStream, and custom Triton pipelines
Hardware H.264/H.265/AV1 decode · 200+ streams
RBAC + SSO + tamper-evident audit log
Encryption at rest · 30-day rolling retention default
Training pod spine, GPUDirect RDMA, parallel storage fabric
Forge EN-400 Switch
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32× 400GbE · RoCEv2 · cut-through
Open-network Ethernet leaf for inference clusters, storage backplanes, and AI-Ethernet fabrics — runs SONiC or vendor NOS with zero-touch provisioning.
Parallel and object storage tuned for training datasets and model artifacts.
Forge DS-Parallel 200
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200TB NVMe · 80 GB/s read · RDMA
All-flash parallel filesystem for training dataloaders, checkpoint streams, and shared scratch — engineered to keep GPUs saturated under heavy shuffle.
WekaFS or Lustre · POSIX + NFS + S3 multi-protocol
GPUDirect Storage · NVMe-oF over RDMA
Snapshots, clones, and policy-based tiering to object
Dual active-active controllers · HA failover
Inline compression + end-to-end checksums
Ideal for
Training dataset hot tier, fast checkpointing, shared scratch
Forge DS-Object 1P
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1PB raw · S3 API · erasure coded
On-prem object store for datasets, model registry, and long-term retention — S3 + Iceberg compatible so your data lake and AI stack share one source of truth.
1PB raw · 12+4 erasure coding · multi-site replication
S3 + Iceberg + Parquet optimized · presigned URLs
Versioning, object lock, and WORM compliance modes
AES-256 encryption at rest · KMS / Vault integration
Cold data, model registry, dataset lake, audit archive
Orchestration & Racks
Pre-integrated racks with scheduling, observability, and security baked in.
Forge Orchestrator Rack
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42U · Kubernetes + Slurm · 25kW
Pre-validated 42U control rack delivered with scheduler, observability, secrets management, and identity wired in — your platform team starts on day one, not month three.
Kubernetes + Slurm + Run:AI / Kueue scheduling
Prometheus + Grafana + Loki + DCGM exporters
OIDC SSO · HashiCorp Vault · sealed-secrets
Harbor private registry + Argo CD GitOps
Out-of-band BMC fabric · serial console aggregation
Power, thermal, and per-port network telemetry
Ideal for
Turnkey on-prem AI platform, multi-team GPU sharing
Forge Liquid Rack 60kW
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Rear-door heat exchanger · 60kW · 48U
High-density liquid-cooled 48U rack for H200, B200, and GB200-class systems — drops into existing data halls without raised-floor chilled water.