Grug 12B — Hardware Requirements & GPU Compatibility
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Grug 12B is a 12.0B-parameter open language model from kai-os. It supports a context window of up to 262,144 tokens. At BF16 it needs about 24.97 GB of VRAM — see which GPUs and Macs can run it below.
1.2K downloads
62 likesSpecifications
- Publisher
- kai-os
- Parameters
- 12.0B
- Architecture
- Gemma4UnifiedForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 262,144
- Release Date
- 2026-07-02
- License
- Other
Get Started
HuggingFace
How Much VRAM Does Grug 12B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 25.0 GB | 120.9 GB | 23.92 GB | Brain floating point 16 — preferred for training |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run Grug 12B?
BF16 · 25.0 GBGrug 12B (BF16) requires 25.0 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 33+ GB is recommended. Using the full 262K context window can add up to 95.9 GB, bringing total usage to 120.9 GB. 1 GPU can run it, including NVIDIA GeForce RTX 5090.
All compatible consumer-level GPUs are running near their VRAM limit. You may also want to consider professional GPUs (e.g., NVIDIA A100, H100) which offer significantly more VRAM. For more headroom and better throughput, consider a multi-GPU configuration with tensor parallelism (supported by tools like vLLM, llama.cpp, or text-generation-inference).
Decent
— Enough VRAM, may be tightWhich Devices Can Run Grug 12B?
BF16 · 25.0 GB32 devices with unified memory can run Grug 12B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio M4 Max (36 GB).
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Grug 12B need?
Grug 12B requires 25.0 GB of VRAM at BF16. Full 262K context adds up to 95.9 GB (120.9 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 12.0B × 16 bits ÷ 8 = 23.9 GB
KV Cache + Overhead ≈ 1.1 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 97 GB (at full 262K context)
VRAM usage by quantization
BF1625.0 GBBF16 + full context120.9 GB- Can I run Grug 12B on a Mac?
Grug 12B requires at least 25.0 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 Grug 12B locally?
Yes — Grug 12B can run locally on consumer hardware. At BF16 quantization it needs 25.0 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Grug 12B?
At BF16, Grug 12B can reach ~176 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 ÷ 25.0 × 0.65 = ~208 tok/s
Estimated speed at BF16 (25.0 GB)
~208 tok/s~208 tok/s~176 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Grug 12B?
At BF16, the download is about 23.92 GB.
- Which GPUs can run Grug 12B?
1 consumer GPU can run Grug 12B at BF16 (25.0 GB). Top options include NVIDIA GeForce RTX 5090.
- Which devices can run Grug 12B?
35 devices with unified memory can run Grug 12B at BF16 (25.0 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), Beelink GTR9 Pro (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.