Kai 30B Instruct — Hardware Requirements & GPU Compatibility
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Kai 30B Instruct is a 32.8B-parameter open language model from NoesisLab. It supports a context window of up to 32,768 tokens. At BF16 it needs about 66.36 GB of VRAM — see which GPUs and Macs can run it below.
490 downloads
21 likesSpecifications
- Publisher
- NoesisLab
- Parameters
- 32.8B
- Architecture
- Qwen2ForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 152,064
- Release Date
- 2026-03-04
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Kai 30B Instruct Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 66.4 GB | 74.4 GB | 65.53 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run Kai 30B Instruct?
BF16 · 66.4 GBKai 30B Instruct (BF16) requires 66.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 87+ GB is recommended. Using the full 33K context window can add up to 8.1 GB, bringing total usage to 74.4 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run Kai 30B Instruct?
BF16 · 66.4 GB5 devices with unified memory can run Kai 30B Instruct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Related Models
Frequently Asked Questions
- How much VRAM does Kai 30B Instruct need?
Kai 30B Instruct requires 66.4 GB of VRAM at BF16. Full 33K context adds up to 8.1 GB (74.4 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 32.8B × 16 bits ÷ 8 = 65.5 GB
KV Cache + Overhead ≈ 0.9 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 8.9 GB (at full 33K context)
VRAM usage by quantization
BF1666.4 GBBF16 + full context74.4 GB- Can NVIDIA GeForce RTX 5090 run Kai 30B Instruct?
No — Kai 30B Instruct requires at least 66.4 GB at BF16, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- Can I run Kai 30B Instruct on a Mac?
Kai 30B Instruct requires at least 66.4 GB at BF16, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run Kai 30B Instruct locally?
Yes — Kai 30B Instruct can run locally on consumer hardware. At BF16 quantization it needs 66.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Kai 30B Instruct?
At BF16, Kai 30B Instruct can reach ~44 tok/s on AMD Instinct MI300X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 66.4 × 0.55 = ~44 tok/s
Estimated speed at BF16 (66.4 GB)
~44 tok/s~33 tok/s~27 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Kai 30B Instruct?
At BF16, the download is about 65.53 GB.
- Which GPUs can run Kai 30B Instruct?
No single consumer GPU has enough VRAM to run Kai 30B Instruct at BF16 (66.4 GB). Multi-GPU or professional hardware is required.
- Which devices can run Kai 30B Instruct?
5 devices with unified memory can run Kai 30B Instruct at BF16 (66.4 GB), including Mac Pro M2 Ultra (192 GB), Mac Studio M2 Ultra (192 GB), Mac Studio M4 Max (128 GB), NVIDIA DGX A100 640GB. Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.