Technical Specifications
Comprehensive architecture details, performance benchmarks, API limits, and deployment requirements for the NexusAI platform.
🧠 Core Model Specs
| Model Family | Nexus V3 Ultra |
|---|---|
| Parameters | 175B (Dense) / 1.8T (MoE Active: 130B) |
| Training Tokens | 14.2T tokens (Multi-lingual, Code, Scientific) |
| Context Window | 256K tokens (Full attention) |
| Architecture | Hybrid MoE + Sparse Attention + KV Cache Optimization |
| Tokenization | Nexus BPE-Extended (150K vocab) |
| Output Modality | Text, Code, JSON, Structured Data, Multi-modal |
⚙️ Fine-Tuning & Customization
| Methods | LoRA, QLoRA, Full FT, DPO, RLHF |
|---|---|
| Min Dataset | 50 labeled examples (Low-shot mode) |
| Max Dataset | Unlimited (Streamed batches) |
| Training Time | ~45min (LoRA 8-bit) to ~6hrs (Full FP16) |
| Validation | Automatic hyperparameter sweep + early stopping |
🌐 API Endpoints
POST https://api.nexusai.dev/v3/chat/completions
POST https://api.nexusai.dev/v3/embeddings
POST https://api.nexusai.dev/v3/finetune/jobs
GET https://api.nexusai.dev/v3/status
| Authentication | Bearer API Key / OAuth 2.0 |
|---|---|
| Rate Limits | 1,000 RPM / 1M TPM (Starter) → Unlimited (Enterprise) |
| Timeout | 120s (Stream) / 60s (Sync) |
| Retry Policy | Exponential backoff (max 5, 429/500 errors) |
📦 Supported SDKs
| Python | v2.4.0 pip install nexusai |
|---|---|
| TypeScript/Node | v2.4.0 npm i @nexusai/sdk |
| Go | v1.8.2 go get github.com/nexusai/go-sdk |
| Java/Kotlin | v0.9.1 Maven/Gradle |
| cURL/REST | Native OpenAPI 3.1 Spec |
📊 Latency & Throughput
| First Token (p95) | 85ms |
|---|---|
| Throughput (tokens/sec) | 120 - 850 (Hardware dependent) |
| Batch Inference | Up to 256 concurrent requests |
| Streaming Support | SSE & WebSocket |
🎯 Benchmark Scores
| MMLU | 89.4% |
|---|---|
| Hellaswag | 92.1% |
| HumanEval (Code) | 84.7% pass@1 |
| Winograd | 91.3% |
| BLEU (Translation) | 48.2 avg |
☁️ Cloud & On-Prem
| Supported Clouds | AWS, GCP, Azure, Oracle, IBM |
|---|---|
| Container | Docker / Kubernetes / OCI |
| On-Prem Hardware | NVIDIA A100/H100 or AMD MI300X (8x GPU min) |
| Storage | ~2.8TB (FP16) / ~1.4TB (Int8 quantized) |
| Auto-Scaling | Horizontal pod autoscaling (K8s) + GPU dynamic partitioning |
🛠️ System Requirements
| OS | Linux (Ubuntu 20.04+, RHEL 8+), macOS, Windows Subsystem |
|---|---|
| CUDA | 12.1+ recommended |
| vRAM | 80GB+ (8x A100) or 96GB+ (8x H100) |
| CPU | 64+ cores, AVX-512 support |
| Network | 100GbE+ interconnect, InfiniBand optional |
🔒 Data & Encryption
| In-Transit | TLS 1.3 (AEAD ciphers only) |
|---|---|
| At-Rest | AES-256-GCM, Customer-managed keys (CMK) |
| Data Retention | Zero-log by default. Optional 7/30/90 day audit trails |
| Isolation | Dedicated VPC, Tenant-level GPU partitioning |
📜 Compliance & Governance
| Certifications | SOC 2 Type II, ISO 27001, HIPAA, GDPR, CCPA |
|---|---|
| AI Safety | Red-teaming, Constitutional AI alignment, Adversarial filtering |
| Access Control | RBAC, SSO (SAML/OIDC), MFA enforcement |
| Auditing | Immutable API logs, Prompt/Response tracing, Export to SIEM |