LLaDA2.1 Mini — Hardware Requirements & GPU Compatibility
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LLaDA2.1 Mini is a 16.3B-parameter open language model from Inclusion AI. It supports a context window of up to 32,768 tokens. At BF16 it needs about 32.90 GB of VRAM — see which GPUs and Macs can run it below.
40.1K downloads
99 likesSpecifications
- Publisher
- Inclusion AI
- Parameters
- 16.3B
- Architecture
- LLaDA2MoeModelLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 157,184
- Release Date
- 2026-02-12
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does LLaDA2.1 Mini Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 32.9 GB | 34.1 GB | 32.51 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run LLaDA2.1 Mini?
BF16 · 32.9 GBLLaDA2.1 Mini (BF16) requires 32.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 43+ GB is recommended. Using the full 33K context window can add up to 1.3 GB, bringing total usage to 34.1 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run LLaDA2.1 Mini?
BF16 · 32.9 GB13 devices with unified memory can run LLaDA2.1 Mini, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio M4 Max (36 GB).
Runs great
— Plenty of headroom
Related Models
Frequently Asked Questions
- How much VRAM does LLaDA2.1 Mini need?
LLaDA2.1 Mini requires 32.9 GB of VRAM at BF16. Full 33K context adds up to 1.3 GB (34.1 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 16.3B × 16 bits ÷ 8 = 32.5 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 1.6 GB (at full 33K context)
VRAM usage by quantization
BF1632.9 GBBF16 + full context34.1 GB- Can NVIDIA GeForce RTX 5090 run LLaDA2.1 Mini?
No — LLaDA2.1 Mini requires at least 32.9 GB at BF16, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- Can I run LLaDA2.1 Mini on a Mac?
LLaDA2.1 Mini requires at least 32.9 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 LLaDA2.1 Mini locally?
Yes — LLaDA2.1 Mini can run locally on consumer hardware. At BF16 quantization it needs 32.9 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is LLaDA2.1 Mini?
At BF16, LLaDA2.1 Mini can reach ~89 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 ÷ 32.9 × 0.55 = ~89 tok/s
Estimated speed at BF16 (32.9 GB)
~89 tok/s~66 tok/s~55 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of LLaDA2.1 Mini?
At BF16, the download is about 32.51 GB.
- Which GPUs can run LLaDA2.1 Mini?
No single consumer GPU has enough VRAM to run LLaDA2.1 Mini at BF16 (32.9 GB). Multi-GPU or professional hardware is required.
- Which devices can run LLaDA2.1 Mini?
13 devices with unified memory can run LLaDA2.1 Mini at BF16 (32.9 GB), including Mac Mini M4 Pro (48 GB), Mac Pro M2 Ultra (192 GB), Mac Studio M2 Ultra (192 GB), Mac Studio M4 Max (128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.