Jais 13B Chat — Hardware Requirements & GPU Compatibility
Chat
Jais 13B Chat is a 13B-parameter open language model from inceptionai. At BF16 it needs about 28.60 GB of VRAM — see which GPUs and Macs can run it below.
Doesn't fit a 24 GB GPU
Smallest consumer fit → RTX 5090 · BF16 · 31.5 GB · ~41 tok/s
Macs from 36 GB (BF16 · ~11 tok/s)
Apache 2.0
Not set up for it?
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Jais 13B Chat
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve inceptionai/jais-13b-chat --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Other ways
each opens step-by-stepRun in cloud
Fits on A40 (48 GB) (16 GB headroom) · BF16
- Generation speed
- ~16 tok/s
- generation speed
- Cost per 1M output tokens
- $6.15
- per 1M output tokens
How Much VRAM Does Jais 13B Chat Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 28.6 GB | 34.9 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Does not fit: needs 31.5 GB |
Selected BF16
Brain floating point 1631.5GBat 16K context
- Weights
- 27.9 GB
- KV cache
- 3.4 GB
- Overhead
- 0.3 GB
Doesn't fit RTX 4090 (reference) · needs 7.5 GB more
≥30 tok/s needs ≥1.32 TB/s · RTX 5090 ~41
Which Hardware Can Run Jais 13B Chat?
BF16 · 28.6 GBJais 13B Chat (BF16) requires 28.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 38+ GB is recommended. 1 GPU can run it, including NVIDIA GeForce RTX 5090. 78 devices with unified memory can run Jais 13B Chat, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Studio M5 Max (36 GB).
32–40 GB
fits · 0.5 GB free · up to 16K ctx
1 GPU · 2 Mac classes · 1 device family
RTX 5090~41Simulate output on RTX 5090M5 Max36 GB~11Simulate output on Mac Studio M5 Max (36 GB)M3 Pro*36 GB~4Simulate output on MacBook Pro 14" M3 Pro (36 GB)Orin~5Simulate output on NVIDIA Jetson AGX Orin 32GB
By VRAM tier · typical GPUs and Macs at BF16
Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Gemma 3 12B ITGoogle | 74.2 | 10.9 GB | ~82 | same size |
| Mistral Nemo Base 2407Mistral AI | — | 11 GB | ~81 | same size |
| Phi 3 Medium 128k InstructMicrosoft | — | 12 GB | ~72 | same size |
| Baichuan2 13B Basebaichuan-inc | — | 11.5 GB | ~76 | same size |
| Llama 2 13B Chat HFMeta | — | 11.5 GB | ~76 | same size |
| Vicuna 13B V1.3LMSYS | — | 8.6 GB | ~76 | same size |
Frequently Asked Questions
How much VRAM does Jais 13B Chat need?
Jais 13B Chat requires 28.6 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 13B × 16 bits ÷ 8 = 26 GB
KV Cache + Overhead ≈ 2.6 GB (at 2K context + ~0.3 GB framework)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
VRAM usage by quantization
Can I run Jais 13B Chat on a Mac?
Yes — Mac Studio M5 Max (36 GB) and 65 other Macs can run Jais 13B Chat. Apple Silicon uses unified memory, so the model shares RAM with the system. At BF16 you need at least 28.6 GB of usable unified memory (RAM minus macOS overhead).
Can I run Jais 13B Chat locally?
Yes — Jais 13B Chat can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Jais 13B Chat?
At BF16, Jais 13B Chat can reach ~168 tok/s on AMD Instinct MI350X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 28.6 × 0.65 = ~182 tok/s
Estimated speed at BF16 (28.6 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Jais 13B Chat?
At BF16, the download is about 26.00 GB.
Which GPUs can run Jais 13B Chat?
1 consumer GPU can run Jais 13B Chat at BF16. Top options include NVIDIA GeForce RTX 5090.
Which devices can run Jais 13B Chat?
78 devices with unified memory can run Jais 13B Chat at BF16 (28.6 GB), including ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB), GMKtec EVO-X2 (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.