A Japanese LLM GPU server has a few needs that an English chatbot does not. Japanese text tokenizes differently, the best open models for Japanese come from specific families and fine-tunes, and users in Japan notice delay quickly in a chat interface. This guide covers those three points in order: choosing a Japanese-capable model, sizing VRAM with Japanese text in mind, and finding a host near Japan on Cloudj by filtering the live GPU list with the JP and KR country codes, with a Tokyo VPS as the stable edge.
For the location side in more depth, see the guide to renting a GPU in Japan.
Step 1: choose a Japanese-capable model family
Model quality in Japanese varies a lot, and size alone does not predict it. Rather than chase a leaderboard, shortlist by family:
- Multilingual open families with strong Asian-language data. Several widely used open model families are trained on large amounts of Chinese, Japanese and Korean text and handle Japanese well out of the box.
- Japanese continual pre-training of global models. Japanese research groups and companies have released versions of popular open models that were further trained on Japanese corpora, sometimes with an extended vocabulary.
- Japanese-first models. Some organisations publish models trained with Japanese as a primary language, often in smaller sizes that suit a single GPU.
Test two or three candidates on your own prompts: customer support phrasing, keigo, product names in katakana, and mixed Japanese and English. Read each model's licence before commercial use; terms differ between families and fine-tunes.
Step 2: size VRAM with Japanese tokens in mind
Japanese has no spaces between words and mixes kanji, hiragana, katakana and Latin script. A tokenizer built mostly for English often splits Japanese into more tokens than the same meaning in English. Models with Japanese-extended vocabularies usually need fewer tokens per sentence. This matters for two reasons: more tokens mean a larger KV cache for the same conversation, and longer generation time per answer.
As rough, hedged guidance for planning:
| Model size (approx.) | Precision | Card to start with |
|---|---|---|
| 7B to 9B | 8-bit or 16-bit | RTX 4090, 24 GB |
| 13B to 14B | 4-bit or 8-bit | RTX 4090 for 4-bit; 48 GB for 8-bit with long context |
| 30B to 35B | 4-bit | RTX A6000 or L40S, 48 GB |
| 70B class | 4-bit | Two 48 GB cards or one larger card |
Leave headroom for the KV cache, especially if you plan long Japanese documents or many concurrent users. Measure with nvidia-smi under a realistic load rather than trusting a table. The RTX 4090 is open to new accounts; the RTX A6000 and L40S need a higher account level, which the console will tell you.
Step 3: find a host near Japan
Open the deploy page with the RTX 4090, or change the model, and type JP into the Country field. If a machine is listed, compare reliability, max duration and bandwidth price. If nothing suits you, try JP, KR; Korea is a common second choice for Japanese traffic. Each row reports its country at country level only, and stock changes during the day, so check the console when you are ready to deploy. The country filter walkthrough explains every field.
If neither code returns a machine and you need to launch, consider TW or HK, and measure latency from Japanese users before switching traffic. For an interactive chat app, the guide to real-time AI latency on GPUs explains why streaming tokens and time to first token matter more than raw distance.
Step 4: serve the model
Pick the Ubuntu 22.04 + CUDA template or bring a public Docker image (custom images need a higher account level). Install your serving stack over SSH. A common choice is an OpenAI-compatible server such as vLLM:
pip install vllm
vllm serve <hf-model-id> --host 127.0.0.1 --port 8000 --max-model-len 8192Binding to 127.0.0.1 keeps the API off the public internet. The vLLM documentation covers quantized models and memory flags. For quick local testing with smaller models, Ollama is another option; the Ollama on a GPU server in Asia guide shows how to install and secure it.
Step 5: put a Tokyo VPS at the edge
The GPU host has an "available until" date, and there is no automatic move if it goes offline. Your users should not depend on one machine. Run a VPS in Japan, located in Tokyo, as the public edge: domain, TLS, authentication, rate limits and a queue. Connect it to the GPU through an SSH tunnel and proxy requests with Nginx. If you change GPU hosts, you update the tunnel and Japanese users see no change. The Tokyo edge reverse proxy guide has the full configuration.
Common pitfalls
- Testing only in English. A model that looks great in English may produce stiff Japanese. Test with real user phrasing.
- Ignoring the context budget. Long Japanese documents fill the KV cache faster than you expect.
- Disk too small. Disk cannot be resized later; keep room for two or three candidate models.
- No backups. Keep prompts, fine-tunes and configuration in your own storage.
FAQ
Are there GPUs in Japan on Cloudj?
Search with JP in the console; if a machine is listed you can deploy it. Availability changes during the day, so add KR as a fallback.
Which GPU is enough for a Japanese chatbot?
Many 7B to 14B Japanese-capable models run on a 24 GB RTX 4090 when quantized. Larger models need 48 GB cards.
Do I need a VPS in Tokyo?
For a public service, it is recommended. It keeps your endpoint stable while GPU hosts change.
Where can I see prices?
Live on GPU cloud and each model page.
Next step
Shortlist two Japanese-capable models, open the deploy page with JP, KR, and test them on a 24 GB card first. When you are happy, put a Tokyo VPS in front and keep backups of everything you build.