Jais 30B Chat v3
By MBZUAI / Core42 · UAE
Updated 2026-07-13
Overview
MBZUAI and Core42's reference open Arabic LLM — a 30B trained natively (not a fine-tune) for Arabic with strong English bilingual support under Apache 2.0.
When to pick this model
- Arabic-first chat and content generation
- Bilingual Arabic-English customer support workloads
- MENA-region sovereign deployments
- Research on non-Latin-script LLM training
- Apache-licensed Arabic alternatives to closed APIs
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 18 GB |
| Q5_K_M | 22 GB |
| Q8_0 | 33 GB |
| FP16 (no quantization) | 60 GB |
VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.
In practice, Jais 30B Chat v3 wants a 24 GB card at Q4_K_M (18 GB). Stepping up to Q8_0 nearly doubles the footprint to 33 GB, and unquantized FP16 weights take 60 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, Jais 30B Chat v3 needs roughly 28 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 3 tokens/sec on entry-level GPUs, on the order of 13 tokens/sec on a mid-range card, and up to 32 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.
What hardware do you need
The table below matches Jais 30B Chat v3 to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.
| GPU memory | Example cards | Best fit for Jais 30B Chat v3 |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | Does not fit — needs 18 GB at Q4_K_M |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | Does not fit — needs 18 GB at Q4_K_M |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | Does not fit — needs 18 GB at Q4_K_M |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | Q5_K_M (22 GB used) |
| 32 GB | RTX 5090 | Q5_K_M (22 GB used) |
Which GPU should you buy to run Jais 30B Chat v3?
To run Jais 30B Chat v3 locally at Q4, you need ~18 GB of VRAM. The best value for this is a RTX 4090 (24 GB VRAM).
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Strengths
- Native Arabic-first architecture, not a Llama fine-tune
- Strong bilingual Arabic-English performance
- Apache 2.0 license enables commercial use
- Backed by MBZUAI, Core42, and Cerebras
Limitations
- 8k context limits long-document workflows
- No official Ollama distribution
- Weaker than Jais Adapted 70B for hardest Arabic tasks
Typical workloads
In our catalog grid, Jais 30B Chat v3 is filed under Arabic, MENA Sovereignty — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: multilingual workloads.
Note the 8k-token context window — fine for short interactions, limiting for long documents or big retrieval contexts. The Apache 2.0 license is permissive, so shipping it inside a commercial product raises no special legal questions.
Architecture & training
Architecture: Native decoder · SwiGLU · ALiBi · Arabic-first
Training: MBZUAI + Core42 + Cerebras.
The default open Arabic LLM at the 30B class — pick it for native Arabic quality without the Llama license.
Quick start
# HuggingFace : core42/jais-30b-chat-v3Or use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.
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Frequently asked questions
How much VRAM does Jais 30B Chat v3 need?
At the recommended Q4_K_M quantization, Jais 30B Chat v3 needs about 18 GB of VRAM. Q8_0 takes 33 GB, and unquantized FP16 weights take 60 GB.
Can Jais 30B Chat v3 run without a GPU?
Yes — with roughly 28 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.
What context window does Jais 30B Chat v3 support?
Jais 30B Chat v3 supports a 8k-token context window (8,192 tokens).
Can I use Jais 30B Chat v3 commercially?
Yes. Jais 30B Chat v3 is released under Apache 2.0, a permissive open-source license that allows commercial use, modification and redistribution.
How fast is Jais 30B Chat v3 on consumer hardware?
Our compatibility engine estimates on the order of 13 tokens/sec on a mid-range GPU and up to 32 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of Jais 30B Chat v3 should I download first?
Start with Q4_K_M (18 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. On a 24 GB card you can run up to Q5_K_M.