LLaDA 2.0 Uni 16B
By Ant Group / inclusionAI · China
Updated 2026-07-13
Overview
Ant Group's first open Apache 2.0 diffusion LLM: a 16B/1B MoE paired with a 6.2B diffusion decoder, unifying text and vision generation and editing. Released April 2026.
When to pick this model
- Research on diffusion-based language models
- Unified text + image generation and editing in one stack
- Interleaved thinking workflows during generation
- Apache 2.0 commercial use of dLLM architectures
- Experiments comparing diffusion vs. autoregressive decoding
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 18 GB |
| Q5_K_M | 22 GB |
| Q8_0 | 30 GB |
| FP16 (no quantization) | 47 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, LLaDA 2.0 Uni 16B wants a 24 GB card at Q4_K_M (18 GB). Stepping up to Q8_0 raises the footprint to 30 GB, and unquantized FP16 weights take 47 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, LLaDA 2.0 Uni 16B needs roughly 36 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 25 tokens/sec on entry-level GPUs, on the order of 60 tokens/sec on a mid-range card, and up to 130 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.
What hardware do you need
The table below matches LLaDA 2.0 Uni 16B 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 LLaDA 2.0 Uni 16B |
|---|---|---|
| 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 | Q8_0 (30 GB used) |
Which GPU should you buy to run LLaDA 2.0 Uni 16B?
To run LLaDA 2.0 Uni 16B 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
- The first Apache 2.0 open diffusion LLM
- Unified text, vision, generation, and editing
- Interleaved 'thinking' mode during diffusion
- Decoder-turbo distillation runs 8 diffusion steps instead of 50
- Apache 2.0 commercial license
Limitations
- Diffusion architecture not supported by Ollama or llama.cpp
- Requires Flash Attention 2 and CUDA 12.4
- Around 47 GB VRAM during active generation
- Only 8k context window
Typical workloads
In our catalog grid, LLaDA 2.0 Uni 16B is filed under Native Image Generation, Unified Vision+Text, dLLM Research — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: vision-language work — screenshots, charts, scanned documents.
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: MoE 16B/1B active + Discrete Semantic Tokenizer (SigLIP-VQ) + Decoder Diffusion 6.2B + VAE
Training: Masked Token Prediction paradigm. Distilled decoder-turbo (10× speedup, 8 steps instead of 50). SPRINT acceleration.
A research-first release that proves Apache 2.0 dLLMs are real — production users should wait for tooling to catch up.
Quick start
# HuggingFace : inclusionAI/LLaDA2.0-Uni (Flash Attn 2 + CUDA 12.4 requis)Or 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 LLaDA 2.0 Uni 16B need?
At the recommended Q4_K_M quantization, LLaDA 2.0 Uni 16B needs about 18 GB of VRAM. Q8_0 takes 30 GB, and unquantized FP16 weights take 47 GB.
Can LLaDA 2.0 Uni 16B run without a GPU?
Yes — with roughly 36 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 LLaDA 2.0 Uni 16B support?
LLaDA 2.0 Uni 16B supports a 8k-token context window (8,192 tokens).
Can I use LLaDA 2.0 Uni 16B commercially?
Yes. LLaDA 2.0 Uni 16B is released under Apache 2.0, a permissive open-source license that allows commercial use, modification and redistribution.
How fast is LLaDA 2.0 Uni 16B on consumer hardware?
Our compatibility engine estimates on the order of 60 tokens/sec on a mid-range GPU and up to 130 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of LLaDA 2.0 Uni 16B 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.