Karnak-40B-v1.0
Karnak-40B is a 40-billion-parameter open-source LLM optimized for Arabic and English, built by extending and fine-tuning Qwen3-30B. It supports up to 20K token context and can run locally or on your own servers. Licensed under Apache 2.0, it's free for commercial use.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | Applied-Innovation-Center |
| Parameters | 40.7B |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 185.9k |
| Likes | 37 |
| Last updated | 2026-04-24 |
| Source | Applied-Innovation-Center/Karnak-40B-v1.0 |
What Karnak-40B-v1.0 is
Depth-extended causal language model (40B parameters) derived from Qwen/Qwen3-30B-A3B-Instruct-2507. Features include: Arabic-optimized tokenizer, multi-stage training pipeline (continued pre-training + SFT), safe context range up to 20K tokens, bfloat16 precision support. Compatible with Transformers, vLLM, and serves via OpenAI-compatible API.
Run Karnak-40B-v1.0 locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="Applied-Innovation-Center/Karnak-40B-v1.0")out = pipe("Explain retrieval-augmented generation in one sentence.", max_new_tokens=128)print(out[0]["generated_text"])Swap in vLLM or Ollama for production-grade serving. DEV.co can stand up the inference stack.
How you'd run it
A typical self-hosted path — open weights, an inference server, your application.
DEV.co builds each layer — from GPU infrastructure to the application.
Best use cases
Running & fine-tuning it
Estimated: ~80–100GB VRAM (bfloat16, single GPU); ~40–50GB with int8 quantization. Multi-GPU tensor parallelism supported. CPU memory for tokenizer/offloading additional ~32GB. No quantization or acceleration frameworks (GPTQ, AWQ) explicitly mentioned in card; Unknown whether they are validated.
Apache 2.0 license permits fine-tuning. Card does not mention LoRA/QLoRA support or adapter frameworks. Standard Hugging Face Transformers fine-tuning is feasible. Estimated training cost for domain adaptation (SFT) on 10K examples: ~1–3 days on single A100. No guidance on optimal hyperparameters or stability provided.
When to avoid it — and what to weigh
- Extreme Low-Latency or Edge Requirements — 40B parameters require significant VRAM and compute. For <100ms latency or mobile/embedded targets, consider smaller quantized models or distilled versions.
- No GPU Access or Severely Constrained Hardware — Minimum hardware not clearly stated; estimated ~80GB VRAM for bfloat16 inference. CPU-only inference is impractical; even quantized (int8) variants require substantial GPU memory.
- Languages Beyond Arabic/English — Model is optimized for Arabic and English. Multilingual support for other languages (Spanish, Chinese, etc.) is not documented and should not be assumed.
- Real-Time Factual Accuracy Requirements Without Validation — Card states 'high-quality filtered data' but provides no benchmarks on hallucination rates or factuality. Use in high-stakes applications (legal, medical) requires external fact-checking pipelines.
License & commercial use
Apache License 2.0 (SPDX: apache-2.0). Permissive, OSI-approved open-source license.
Apache 2.0 explicitly permits commercial use, modification, and distribution without royalties or permission. Model is ungated and publicly available. No proprietary restrictions, paid support agreements, or usage attribution requirements are stated. Suitable for commercial deployment provided you comply with Apache 2.0 terms (include license text, state material changes).
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Moderate |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | Moderate |
| DEV.co fit | Strong |
| Assessment confidence | High |
Model is open-source and not gated, allowing full inspection. No explicit security audit, red-teaming results, or alignment testing documented. Considerations: (1) Model could perpetuate biases in training data; (2) No adversarial robustness testing mentioned; (3) Running untrusted user prompts through any LLM carries injection/prompt-engineering risks; (4) Local deployment eliminates external data exposure but requires securing your inference endpoints. Recommend standard LLM safety practices: input validation, output filtering, rate limiting, and access controls.
Alternatives to consider
Qwen/Qwen3-30B-A3B-Instruct-2507 (base model)
Smaller base (30B vs. 40B depth-extended), lower inference cost. Directly comparable if Arabic optimization is not critical.
Mistral-7B-Instruct-v0.2
Significantly smaller (7B), faster inference and lower VRAM footprint. Supports English well; Arabic support weaker. Trade-off: less capability for faster deployment.
LLaMA 3.1-70B
Larger, stronger English/multilingual base model. Better for general-purpose tasks; Arabic optimization less tuned. Requires more compute; requires commercial agreement review (Llama Community License).
Ship Karnak-40B-v1.0 with senior software developers
Start with a test deployment on a single GPU. Validate inference latency, Arabic/English output quality, and context window performance against your use case. If multilingual or extreme low-latency is required, benchmark alternatives. Contact Devco for guidance on fine-tuning, production hardening, or integration with your RAG/application stack.
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Karnak-40B-v1.0 FAQ
Can we use Karnak-40B in production for commercial applications?
What GPU hardware do we need to run this model?
Does this model support languages other than Arabic and English?
What is the maximum safe context length?
Work with a software development agency
DEV.co is a software development agency delivering custom software development services to companies building on open source. Our software developers and web developers design, integrate, and ship production systems — spanning web development, APIs, AI, data, and cloud. If Karnak-40B-v1.0 is part of your open-source llms roadmap, our team can implement, customize, migrate, and maintain it.
Ready to Deploy Karnak-40B?
Start with a test deployment on a single GPU. Validate inference latency, Arabic/English output quality, and context window performance against your use case. If multilingual or extreme low-latency is required, benchmark alternatives. Contact Devco for guidance on fine-tuning, production hardening, or integration with your RAG/application stack.