Short version: LG AI Research just published K-EXAONE 2.0 on Hugging Face — 750 billion parameters, a 262K context window, 10 languages — and the lab that used to ship the most restrictive license among the majors made it Apache 2.0. The size is the headline; the license is the decision. This is the first frontier-class open weight you can fork, fine-tune, and sell without asking permission, and — at INT4 — the first that fits a single GPU node. Here's the math and when to actually use it.
| Option | What you run | Rough cost | When it wins |
|---|---|---|---|
| FriendliAI API | a hosted endpoint | pay-per-token (cheap) | almost everyone — validate first |
| Self-host INT4 | ~408 GB on 8×H100 | one node | sovereignty or steady volume |
| Self-host FP16 | ~1,634 GB on 16×H200 | two nodes | full-precision, near-24/7 use |
The news isn't 750 billion. It's Apache 2.0.#
Every few weeks another lab drops "open weights," and a founder's first job is to read the license, not the benchmark — because "open" describes anything from MIT to a research-only contract that forbids the one thing you need, which is shipping it in a product.
LG was, until last week, the cautionary tale. Previous EXAONE releases came under a bespoke, non-commercial license: you could measure the model, you couldn't build on it. K-EXAONE 2.0 reverses that — it's Apache 2.0, which explicitly permits commercial use, modification, and redistribution and asks only that you keep the notices (LG AI Research). No revenue share, no field-of-use carve-out, no per-seat clause. For a team of one, that erases the single most expensive hidden line item in adopting an open model: the legal review.
That's the asset even if you never touch a GPU. A permissive open weight can't be retired out from under you, can't be repriced to leave you no alternative, and can be fine-tuned on your own data and shipped. It's optionality you hold, the same hedge we laid out for Kimi K3's open weights — except this time the license itself carries no strings.
What LG actually shipped#
The model, published July 31 as LGAI-EXAONE/K-EXAONE-2.0-750B-A37B, is a sparse mixture-of-experts:
- ~750B total parameters, 256 experts, 8 active per token (≈37B active on any forward pass)
- 262,144-token context window — built for long documents and long-horizon retrieval
- 10 languages: Korean, English, Spanish, German, Japanese, Vietnamese, French, Italian, Polish, Portuguese
LG reports it scoring 94.4 on OpenAI-MRCR, a long-context retrieval test, ahead of Qwen3.5 (93.0), DeepSeek V4 Pro Max (92.9), and GLM-5.1 (71.5) (HackerNoon). Read that as a vendor number on the model's strongest axis — a 262K window is designed to win needle-in-a-haystack recall — not a general verdict. Run it on your own eval before you believe any leaderboard row.
The self-host math: for once, "one node" is a real answer#
Here's where K-EXAONE 2.0 separates from the other open giants. The barrier to self-hosting a frontier model has never been the license — it's the VRAM.
At FP16, K-EXAONE 2.0's weights need about 1,634 GB of VRAM. LG's reference SGLang and vLLM configs assume 16 H200 GPUs — two 8-GPU nodes (Spheron). That's a real cluster, but look at the lever quantization gives you: at INT4 the footprint drops to roughly 408 GB, which fits inside a single 8×H100 (640 GB) node with headroom for KV-cache.
Put that next to Kimi K3, whose 2.8-trillion-parameter weights need ~1.4 TB just to load and a 32×H100-class serving floor. K-EXAONE 2.0 is the first frontier-class open model where a founder can honestly say "one dense node" — provided you can serve MoE routing (vLLM/SGLang with expert-aware scheduling) and accept the quality trade of INT4. Rent that single node and you're in the ~$15-25k/month range depending on provider and commitment; check live rates in our CoreWeave vs Lambda vs Nebius comparison before treating any figure as gospel.
You don't have to touch the weights to try it#
The fastest path isn't self-hosting at all. FriendliAI added day-0, OpenAI-compatible serverless support for K-EXAONE (FriendliAI) — so you can point an existing client at a hosted endpoint and evaluate the model on your own task this afternoon, no GPUs, no download. The 236B sibling lists around $0.2/M input and $0.8/M output (cached input ~$0.1/M) as a reference for how cheap the hosted path runs; confirm live 750B pricing before you budget.
The right sequence is almost always the same one we keep landing on: validate on the API first, and move the weights in-house only when a compliance rule or genuinely sustained volume makes a single node cheaper than per-token pricing.
When K-EXAONE 2.0 is the right call#
Reach for it when:
- You need a no-strings commercial license. If your product embeds a model and you can't risk a research-only or field-restricted license, Apache 2.0 at this scale is rare and valuable.
- You serve non-English markets. Ten languages in one open model beats stitching together a per-language provider matrix — and a lot of today's answer-engine traffic is Asian-market assistants that a multilingual-from-the-start model fits better than an English-first flagship.
- You want an open-weight hedge you could actually run. Unlike the 2.8T-class models, INT4 K-EXAONE 2.0 on a single node is a self-host plan a small team can execute if compliance forces the move.
Stay on a closed frontier model when raw top-end capability on English coding or reasoning is the whole game and you have no license or sovereignty constraint — the best closed models still lead on the hardest agentic rows. But hold K-EXAONE 2.0 in your back pocket as the open option that, for the first time, is permissive and runnable. Keep your app model-swappable, as always, so the choice stays reversible.
