EXAONE 3.5 2.4B on NVIDIA GeForce RTX 4070 SUPER
Can the NVIDIA GeForce RTX 4070 SUPER run EXAONE 3.5 2.4B?
Yes, it runs well
Runs well. At F16 it needs about 6.6 GB of 12.0 GB usable, with an estimated speed of 60.2 tok/s.
- Best quant
F16- Generation speed
- 60.2 tok/s
- Prompt processing
- 10,606 tok/s
- First token (1K prompt)
- 0.3 s
- Largest safe context
- 32K
Download EXAONE 3.5 2.4B
Recommended for the NVIDIA GeForce RTX 4070 SUPER: F16. Every other quantization has its own download link in the table below.
Quickest · Ollama
One command downloads a ready-to-run version and starts a chat.
ollama run exaone3.5:2.4b-instruct-fp16Quantized files · GGUF
Works in LM Studio, llama.cpp, Jan and Ollama. No sign-in needed.
Download the F16 file (opens in a new tab)
huggingface.co/LGAI-EXAONE/EXAONE-3.5-2.4B-Instruct-GGUF (opens in a new tab)
ollama run hf.co/LGAI-EXAONE/EXAONE-3.5-2.4B-Instruct-GGUF:BF16Original weights · Hugging Face
Full-precision files from LG AI Research, for fine-tuning, vLLM, Transformers.
huggingface.co/LGAI-EXAONE/EXAONE-3.5-2.4B-Instruct (opens in a new tab)
Official repo · GitHub
Reference code, inference scripts and docs from LG AI Research. The weights themselves download from Hugging Face.
github.com/LG-AI-EXAONE/EXAONE-3.5 (opens in a new tab)
git clone https://github.com/LG-AI-EXAONE/EXAONE-3.5How the B grade is scored
Every result gets up to 100 points from four parts. The points decide the letter grade.
| Model quality | 13.5 / 45 | |
|---|---|---|
| Memory fit | 25.0 / 25 | |
| Speed | 15.0 / 15 | |
| Memory use | 12.8 / 15 | |
| Total | 66.3 / 100 | 66 ptsGood (B) · 66 of 100 points: Model quality 13.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 12.8/15 |
Every quantization on the NVIDIA GeForce RTX 4070 SUPER
| Quant | Memory needed | Available | Speed | Fit | Grade · points | Download |
|---|---|---|---|---|---|---|
Q2_K | 2.8 GB | 12.0 GB | 422 tok/s | Runs well | 54 ptsUsable (C) · 54 of 100 points: Model quality 9.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 5.4/15 | GGUF files (opens in a new tab) |
Q3_K_M | 3.0 GB | 12.0 GB | 332 tok/s | Runs well | 57 ptsUsable (C) · 57 of 100 points: Model quality 11.3/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 5.8/15 | GGUF files (opens in a new tab) |
Q4_K_M | 3.3 GB | 12.0 GB | 257 tok/s | Runs well | 59 ptsGood (B) · 59 of 100 points: Model quality 12.8/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 6.3/15 | GGUF (opens in a new tab) |
Q5_K_M | 3.5 GB | 12.0 GB | 212 tok/s | Runs well | 59 ptsGood (B) · 59 of 100 points: Model quality 13.1/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 6.8/15 | GGUF (opens in a new tab) |
Q6_K | 3.8 GB | 12.0 GB | 178 tok/s | Runs well | 60 ptsGood (B) · 60 of 100 points: Model quality 13.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 7.3/15 | GGUF (opens in a new tab) |
Q8_0 | 4.4 GB | 12.0 GB | 131 tok/s | Runs well | 61 ptsGood (B) · 61 of 100 points: Model quality 13.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 8.4/15 | GGUF (opens in a new tab) |
F16 | 6.6 GB | 12.0 GB | 60.2 tok/s | Runs well | 66 ptsGood (B) · 66 of 100 points: Model quality 13.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 12.8/15 | GGUF (opens in a new tab) Original weights (opens in a new tab) |
Memory figures assume 8K of context. Longer conversations or documents grow the KV cache.
Other hardware that runs it
- 66 ptsGood (B) · 66 of 100 points: Model quality 13.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 12.8/15 Intel Arc B580
- 66 ptsGood (B) · 66 of 100 points: Model quality 13.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 12.8/15 NVIDIA GeForce RTX 3060 12GB
- 65 ptsGood (B) · 65 of 100 points: Model quality 13.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 12.1/15 NVIDIA GeForce RTX 4060
- 63 ptsGood (B) · 63 of 100 points: Model quality 13.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 9.9/15 AMD Radeon RX 9070 XT
- 63 ptsGood (B) · 63 of 100 points: Model quality 13.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 9.9/15 NVIDIA GeForce RTX 4070 Ti SUPER
Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.