EXAONE 4.0 1.2B — Hardware Requirements & GPU Compatibility
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EXAONE 4.0 1.2B is a 1.3B-parameter open language model from LGAI-EXAONE in the EXAONE family. It supports a context window of up to 65,536 tokens. At Q4_K_M it needs about 1.19 GB of VRAM — see which GPUs and Macs can run it below.
16.3K downloads
184 likesSpecifications
- Publisher
- LGAI-EXAONE
- Family
- EXAONE
- Parameters
- 1.3B
- Architecture
- Exaone4ForCausalLM
- Context Length
- 65,536 tokens
- Vocabulary Size
- 102,400
- Release Date
- 2025-07-11
- License
- Other
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HuggingFace
How Much VRAM Does EXAONE 4.0 1.2B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 1.0 GB | 4.9 GB | 0.54 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 1.1 GB | 5.0 GB | 0.62 GB | 3-bit medium quantization |
| IQ4_XS | 4.30 | 1.1 GB | 5.0 GB | 0.69 GB | Importance-weighted 4-bit, compact |
| Q4_K_M | 4.80 | 1.2 GB | 5.1 GB | 0.77 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 1.3 GB | 5.2 GB | 0.91 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 1.5 GB | 5.4 GB | 1.06 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 1.7 GB | 5.6 GB | 1.28 GB | 8-bit quantization, near-lossless |
| BF16 | 16.00 | 3.0 GB | 6.9 GB | 2.56 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 EXAONE 4.0 1.2B?
Q4_K_M · 1.2 GBEXAONE 4.0 1.2B (Q4_K_M) requires 1.2 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 2+ GB is recommended. Using the full 66K context window can add up to 3.9 GB, bringing total usage to 5.1 GB. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run EXAONE 4.0 1.2B?
Q4_K_M · 1.2 GB33 devices with unified memory can run EXAONE 4.0 1.2B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomWhere to Download EXAONE 4.0 1.2B
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Related Models
Frequently Asked Questions
- How much VRAM does EXAONE 4.0 1.2B need?
EXAONE 4.0 1.2B requires 1.2 GB of VRAM at Q4_K_M, or 3.0 GB at BF16. Full 66K context adds up to 3.9 GB (5.1 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 1.3B × 4.8 bits ÷ 8 = 0.8 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 4.3 GB (at full 66K context)
VRAM usage by quantization
Q4_K_M1.2 GBQ4_K_M + full context5.1 GB- What's the best quantization for EXAONE 4.0 1.2B?
For EXAONE 4.0 1.2B, Q4_K_M (1.2 GB) offers the best balance of quality and VRAM usage. Q5_K_M (1.3 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 1.0 GB.
VRAM requirement by quantization
Q2_K1.0 GBIQ4_XS1.1 GBQ4_K_M ★1.2 GBQ5_K_M1.3 GBQ6_K1.5 GBBF163.0 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run EXAONE 4.0 1.2B on a Mac?
EXAONE 4.0 1.2B requires at least 1.0 GB at Q2_K, 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 EXAONE 4.0 1.2B locally?
Yes — EXAONE 4.0 1.2B can run locally on consumer hardware. At Q4_K_M quantization it needs 1.2 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is EXAONE 4.0 1.2B?
At Q4_K_M, EXAONE 4.0 1.2B can reach ~2450 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~551 tok/s. 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 ÷ 1.2 × 0.55 = ~2450 tok/s
Estimated speed at Q4_K_M (1.2 GB)
~2450 tok/s~551 tok/s~1831 tok/s~1515 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of EXAONE 4.0 1.2B?
At Q4_K_M, the download is about 0.77 GB. The full-precision BF16 version is 2.56 GB. The smallest option (Q2_K) is 0.54 GB.
- Which GPUs can run EXAONE 4.0 1.2B?
35 consumer GPUs can run EXAONE 4.0 1.2B at Q4_K_M (1.2 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 35 GPUs have plenty of headroom for comfortable inference.
- Which devices can run EXAONE 4.0 1.2B?
33 devices with unified memory can run EXAONE 4.0 1.2B at Q4_K_M (1.2 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.