Open-Weight LLM · Private & Custom AI
Karnak-40B-v1.0
Bilingual (Arabic–English) 40B depth-extended model for private deployment and custom fine-tuning in Arabic-heavy operational workflows.
Karnak is a 40B causal LLM built on Qwen3-30B, extended with depth and an Arabic-optimized tokenizer, supporting up to 20K context. It runs entirely self-hosted via Transformers or vLLM, making it suitable for companies needing bilingual automation without vendor lock-in or data residency constraints.
Model facts
Private deployment
Run Karnak-40B-v1.0 in your own environment
Download from HF, run on your own GPU cluster or on-premise infrastructure using vLLM (production-grade) or Transformers. Estimated VRAM: ~80GB (bfloat16, single GPU) or distribute across multiple GPUs. No external API calls; all data stays in your environment. Supports fine-tuning for domain-specific tasks (legal, finance, customer support in Arabic/English).
Operational AI use cases
Bilingual Customer Support Automation
Route and auto-respond to Arabic and English support tickets. Extract intent, classify urgency, draft replies, and escalate complex issues. Reduced latency and cost vs. external APIs; full ticket data stays on-premise.
Document Processing & Knowledge Extraction
Ingest regulatory filings, contracts, or internal memos in Arabic/English; extract entities, summarize sections, and populate internal knowledge bases. 20K context window handles multi-page docs without chunking overhead.
Workflow Automation for Arabic Markets
Automate data entry, form completion, and report generation for regional teams. Fine-tune on your own operational language (financial terms, local jargon) to improve accuracy and compliance over time.
Custom AI
As a base for custom AI
Strong foundation for building proprietary Arabic–English search, RAG, or vertical SaaS. Fine-tune on your domain data (legal, healthcare, fintech) to improve instruction-following and reduce hallucination. Depth-extended architecture provides reasoning capacity; Arabic tokenizer minimizes bloat.
In the operating system
Where it fits
Core inference engine in a private AI OS: powers knowledge retrieval (RAG), agentic workflows (tool-calling via chat template), and multi-turn conversation layers. Pair with vector DB (e.g., Weaviate, Milvus) for semantic search and memory, and with task orchestration (e.g., LangChain, LlamaIndex) for ops automation.
Data control & security
Self-hosting eliminates data transit to third-party APIs; all prompts, completions, and context remain in your infrastructure. No telemetry or training-data leakage. Data governance and compliance (GDPR, CCPA, local data residency) become architectural decisions, not vendor obligations—but you own operational security (encryption, access control, audit logging).
Hardware footprint
Estimate for bfloat16: ~80 GB VRAM single GPU, or distributed across 2–4 GPUs (tensor parallelism). FP8 quantization: ~40–50 GB. Int4 (GPTQ): ~15–20 GB. Inference latency: ~50–200ms per token depending on hardware and batch size. Recommend 24+ GB VRAM per GPU minimum for production throughput.
Integration
OpenAI-compatible API via vLLM (drop-in for existing chat integrations). Expose via REST/gRPC to ops dashboards, ticketing systems, CRMs, and document platforms. Use Transformers or vLLM SDKs for programmatic access. Chat template pre-built; supports system prompts and multi-turn context for agent loops.
When it's not the right fit
- —Your ops require sub-50ms latency at scale without GPU investment; edge inference on CPU is slow.
- —Your team lacks MLOps infrastructure (GPU provisioning, vLLM deployment, monitoring); bare-metal self-hosting adds operational burden.
- —You need English-only, production-hardened models with extensive safety/alignment research; Karnak is newer, fewer public evaluations.
- —Your workflow is English-dominant with no Arabic requirement; smaller, English-focused models (e.g., Mistral 7B, Llama2-13B) are cheaper and faster.
Alternatives to consider
Qwen/Qwen3-30B-A3B-Instruct-2507 (base)
Smaller, slightly faster inference, well-tested in production; trade depth/context for cost if Arabic optimization isn't critical.
Mistral 8x22B MoE
22B per expert, sparse activation, lower VRAM; English-focused but strong ops performance if you don't need Arabic.
LLaMA 2 70B
Mature, extensive evals, strong instruction-following; English/multilingual but not Arabic-optimized, larger footprint.
Related open models
FAQ
Can I run Karnak entirely on-premise without calling external APIs?
Yes. Download the model, deploy via vLLM or Transformers on your GPU infrastructure, and expose it internally. All data remains in your environment; no external telemetry.
Is Karnak licensed for commercial use?
Apache 2.0 license permits commercial use, modification, and distribution with attribution. No gating. Review Apache 2.0 terms for liability and warranty disclaimers.
How do I fine-tune Karnak for our internal workflows?
Use Transformers Trainer, TRL (Hugging Face), or axolotl with your labeled data. Start with LoRA for efficiency (~10–20% VRAM overhead). Model supports continued pre-training and SFT; no architectural restrictions.
What's the safe context window, and can I exceed it?
Documented safe range is 20K tokens. Exceeding may degrade coherence and accuracy. Test on your use case; some users report stability at 25K, but no guarantee.
Build a Private AI System for Your Arabic–English Operations
Karnak is a foundation for custom AI automation that stays in your control. Let LLM.co help you deploy, fine-tune, and orchestrate it into your operational workflows—no data leaving your environment.