Model reference · open weights
EXAONE-4.5 is an open-weight language model from LGAI-EXAONE, listed in the AxForge catalogue. AxForge can bring it up on EU-owned hardware for you on request — with the licence handled where one is required.
About
EXAONE 4.5 We introduce EXAONE 4.5, the first open-weight vision language model developed by LG AI Research. Integrating a dedicated visual encoder into the existing EXAONE 4.0 framework, we expand the model's capability toward multimodality. EXAONE 4.5 features 33 billion parameters in total, including 1.2 billion parameters from the vision encoder. EXAONE 4.5 achieves competitive performance in general benchmark while outperforming SOTA models of similar size in document understanding and Korean contextual reasoning, inheriting powerful language capabilities from our previous language models. For more details, please refer to the technical report, blog and GitHub. Model Configuration - Model Type: Causal Language Model + Vision Encoder - Number of Parameters (Language Model): 31.7B - Number of Parameters (Vision Encoder): 1.29B - Hidden Dimension: 5,120 - Intermediate size: 27,392 - Number of Layers: 64 Main layers + 1 MTP layers - Hybrid Attention Pattern: 16 x (3 Sliding window attention + 1 Global attention) - Reordered Norm: Apply normalization after Attention/MLP, and before residual connection - Sliding Window Attention - Number of Attention Heads: 40 Q-heads and 8 KV-heads - Head Dimension: 128 for both Q/KV - Sliding Window Size: 4096 - Global Attention - Number of Attention Heads: 40 Q-heads and 8 KV-heads - Head Dimension: 128 for both Q/KV - No Rotary Positional Embedding Used (NoPE) - Vision Encoder - Grouped Query Attention (GQA) - 2D RoPE for vision embeddings - Vocab Size: 153,600 - Context Length: 262,144 tokens - Knowledge Cutoff: Dec 2024 (2024/12) Evaluation Results Vision-Language Tasks Language-only Tasks Quickstart Serving EXAONE 4.5 For better inference speed and memory usage, it is preferred to serve the model using optimized inference engines. The EXAONE 4.5 model is supported by various frameworks, including TensorRT-LLM, vLLM, SGLang, and llama.cpp. Support will be expanded in the future. Practically, you can serve the EXAONE 4.5 model with 256K context length on single H200 GPU, or 4x A100-40GB GPUs by using a tensor-parallelism. TensorRT-LLM TensorRT-LLM provides zero day support for EXAONE 4.5. Please refer to the official install
Summarised from the published model card. Read the full card on the HuggingFace links below.
Specifications
| Maker | LGAI-EXAONE |
|---|---|
| Type | Language models |
| Parameters (lead) | 34.4B |
| Variants | 4 |
| Runs with | transformers |
| Released | 2026-04-04 |
| Popularity | 88k downloads / month |
| Likes | 183 |
| Licence | Commercial licence needed |
How it works
Variants
Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.
Using it via the API
Once AxForge deploys exaone-4-5 for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (exaone-4-5 below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/chat/completions \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"exaone-4-5","messages":[{"role":"user","content":"Hello"}]}'
Licence
The weights are open but its licence needs a commercial agreement for business use. AxForge can arrange that licence and host the model for you — you pay AxForge, we settle with the model’s maker. Ask us for a quote. Read the licence ↗