EXAONE 3.5 32B

EXAONE 3.5 32B is LG AI Research's 32B dense model. EXAONE is LG's model family optimized for Korean + English bilingual performance with competitive general reasoning. Run at Q4_K_M via Ollama (ollama pull exaone:32b) or llama.cpp with -ngl 999 -fa -c 8192. Q4_K_M file size ~18 GB on disk. Minimum VRAM: 16 GB — RTX 4080 (16GB) at Q4_K_M with KV offload. RTX 4090 24GB: Q4_K_M comfortably at 16K context. Recommended: RTX 4090 24GB at Q4_K_M. Throughput: ~35-55 tok/s on RTX 4090 at Q4_K_M. EXAONE architecture — compatible with standard inference stacks (llama.cpp support verified). EXAONE 3.5 is LG's most recent release — strong Korean performance, competitive English. Less benchmark coverage than Qwen/Mistral same-tier models, but quality is solid. Use for: Korean language tasks, bilingual (KO+EN) applications, general reasoning, coding. Not as strong on: non-Korean/English languages, niche domains. Context: 32K advertised; practical at Q4 on 24 GB is 16-32K. EXAONE is Apache 2.0 licensed — commercial-friendly.

Minimum: RTX 3060 12GB at Q3_K_M with KV offload. Recommended: RTX 4090 24GB at Q4_K_M (16K context). Optimal: RTX 5090 32GB at Q4_K_M (32K+ context). VRAM math: 32B dense, Q4_K_M ≈ 18 GB. KV cache at 16K: ~8 GB. Total: ~26 GB. RTX 4090 24GB: Q4 + 8-12K context fits on-GPU. 16K: offload KV. RTX 3090 24GB: same profile. RTX 4080 16GB: Q4 + 2K on-GPU. MacBook Pro M4 Pro 24GB+: Q4 at 10-20 tok/s. Cloud: A10 24GB at Q4_K_M. AWQ-INT4 drops to ~16 GB. EXAONE's 32B is hardware-efficient — one of the best Korean-capable models at consumer GPU size.

1. Korean-centric tokenizer. EXAONE's tokenizer is optimized for Korean + English. Other languages (Japanese, Chinese, European) have higher token counts, reducing effective context. Test your language's token efficiency. 2. English quality vs Korean. English performance is competitive but lower than Korean. For English-only tasks, same-tier models like Qwen 3 32B may outperform. 3. LG licensing updates. LG may update EXAONE's license between versions. Verify Apache 2.0 status on the specific 3.5 release before commercial use. 4. Ecosystem maturity. EXAONE has less community quant coverage than Qwen/Mistral. Pre-converted GGUFs may be harder to find. Check bartowski/TheBloke for availability.

llama.cpp for local use (verify EXAONE architecture support). Ollama if EXAONE tag exists. vLLM for serving. EXAONE uses standard transformer architecture — broad compatibility expected. For Korean-specific optimizations, LG may provide reference inference code.

Mistake: Assuming EXAONE matches Qwen/Mistral in English-only benchmarks. Fix: EXAONE is Korean-optimized. English quality is good but not best-in-class. Benchmark your English task against same-tier models before committing. Mistake: Using EXAONE for non-Korean/English languages. Fix: Tokenizer and training distribution are KO+EN heavy. Other languages underperform. Test your specific language. Mistake: Expecting broad GGUF availability. Fix: EXAONE has less community coverage. You may need to convert from hf yourself. Check bartowski's repo first. Mistake: Using Llama chat template. Fix: EXAONE uses LG's chat template. Verify on hf tokenizer_config.json. Using Llama template produces garbled Korean and awkward English.