LG AI Research · exaone
Can I run exaone-deep?
EXAONE Deep exhibits superior capabilities in various reasoning tasks including math and coding benchmarks, ranging from 2.4B to 32B parameters developed and released by LG AI Research.
Parameter counts and memory belong to each build below. A family can contain several sizes and tasks; fitting in memory is not a quality ranking.
Calculate memory for a build and contextexaone-deep system requirements
Memory needed at 4K context, counting model weights, KV cache, compute buffers and runtime overhead. Download sizes come from the official Ollama registry.
| Build | Quantization | Download | Memory needed |
|---|---|---|---|
| exaone-deep:2.4b2.4B parameters (tag)32K documented maximum contextBuild sourceCatalog checked 2026-10-13 | Q4_K_M | 1.5 GB | ~2.3 GB |
| exaone-deep:2.4b-q8_02.4B parameters (tag)32K documented maximum contextBuild sourceCatalog checked 2026-10-13 | Q8_0 | 2.6 GB | ~3.5 GB |
| exaone-deep:7.8b7.8B parameters (tag)32K documented maximum contextBuild sourceCatalog checked 2026-10-13 | Q4_K_M | 4.4 GB | ~5.7 GB |
| exaone-deep:2.4b-fp162.4B parameters (tag)32K documented maximum contextBuild sourceCatalog checked 2026-10-13 | F16 | 5.0 GB | ~6.1 GB |
| exaone-deep:7.8b-q8_07.8B parameters (tag)32K documented maximum contextBuild sourceCatalog checked 2026-10-13 | Q8_0 | 7.7 GB | ~9.3 GB |
| exaone-deep:7.8b-fp167.8B parameters (tag)32K documented maximum contextBuild sourceCatalog checked 2026-10-13 | F16 | 14.6 GB | ~16.6 GB |
| exaone-deep:32b32B parameters (tag)32K documented maximum contextBuild sourceCatalog checked 2026-10-13 | Q4_K_M | 18.0 GB | ~20.8 GB |
| exaone-deep:32b-q8_032B parameters (tag)32K documented maximum contextBuild sourceCatalog checked 2026-10-13 | Q8_0 | 31.7 GB | ~35.6 GB |
Showing the 8 smallest non-alias builds. Choose any build and change the context. A context above the build's documented maximum is a hypothetical calculation, not a supported setting.
How to run exaone-deep locally
- 1.Install Ollama for Windows, macOS or Linux.
- 2.Download this exact build in a terminal:
ollama pull exaone-deep:2.4b - 3.Check the source for the build's task. For text generation, run
ollama run exaone-deep:2.4b. Base/text builds may need a prompt template; an instruct build is usually the appropriate starting point for a conversation.
Frequently asked questions about exaone-deep
- How much VRAM do I need to run exaone-deep?
- At 4K context, the smallest published build of exaone-deep (Q4_K_M) needs roughly 2.3 GB of GPU memory once weights, KV cache, compute buffers and runtime overhead are counted. Bigger quantizations and longer context windows need more. It can also run partly or entirely on the CPU using system RAM, more slowly.
- Can I run exaone-deep without a dedicated GPU?
- A supported CPU runtime can use system RAM. The smallest listed build needs an estimated 2.3 GB at 4K context, plus headroom for your operating system. Check the build's runtime requirements. We have not measured its speed on your CPU.
- How big is the exaone-deep download?
- The smallest published build is 1.5 GB. There are 9 builds in total, the largest being 59.6 GB. Leave some extra free disk space beyond the download itself.
- How do I run exaone-deep locally?
- Install Ollama, then download a specific build with "ollama pull exaone-deep:2.4b". For a build that supports text generation, use ollama run with the same tag. Check the model source for its task and prompt format.
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Model data from the official Ollama registry · verified 13/10/2026