Best Local LLM for Roleplaying, or a Hosted API?

Local or hosted: the quick decision

Pick local if you already own a capable GPU, you want everything offline, or you enjoy tuning models. Pick hosted if you want long memory in a story, you use a laptop or phone, or you would rather spend minutes than weekends on setup. Many people do both: a local model for private experiments and a hosted one for long campaigns.

Factor	Local model	Hosted API
Cost model	Hardware and power, no per-token fee	Pay per token from prepaid credit
Model size	Limited by your GPU memory	Whatever the provider runs
Context length	Limited by memory, since longer context needs more of it	100,000 tokens here
Privacy	Nothing leaves your machine	Prompts travel to the provider; here they are not used for training
Setup	Runtime, weights, quantization, updates	A base URL and a key
Works from any device	Only if you expose your own machine	Yes

What actually decides the best local model

Rankings of the best local llm for roleplaying go stale fast, and no single name is right for everyone. Instead, judge candidates on the things that show up in a real session.

• Refusal behavior. Many base and instruct models were tuned to decline certain themes. Models fine-tuned for fiction or marked as uncensored usually refuse less, but quality varies, so test them on your own scenes.
• Staying in character. Watch for a model that drifts into narrating your character's actions, repeats the same phrases every turn or forgets who is in the room.
• Prose quality. Read ten replies in a row. Some models are fine for one turn and tiresome by the tenth.
• Context handling. A model may advertise a long window and still lose details from the middle of it. Run a test where a fact from early in the chat matters later.
• Size and quantization. A larger model squeezed into heavy quantization can lose more than a smaller one run at higher precision. Compare, do not assume.

The community threads and model cards for the runtime you use are the right place for current recommendations. Download two or three candidates, run the same scene on each and keep the one that makes you want to continue the story.

Where local runs out of room

The wall most roleplayers hit is memory, and it hits in two places. The first is the model itself: a bigger model writes better but needs more GPU memory. The second is context. Every message in a long story is sent again with each new turn, and the cache that holds it also grows with length, so a long campaign can squeeze a GPU that handles short chats just fine.

The usual workarounds are summarizing old turns, using lorebooks that inject only relevant facts, and lowering the context size. They work, and they are also work. A hosted endpoint with a 100,000-token window lets you keep far more of the story in plain view, which suits long campaigns, shared worlds with many characters and stories that run for weeks.

The trade-off is cost, so it helps to measure it. Suppose a chat has 30,000 tokens of history and the model writes 300 tokens per reply. One turn costs 30,000 input tokens at $0.15 per 1M, which is $0.0045, plus 300 output tokens at $0.50 per 1M, which is $0.00015. That is $0.00465 per turn, or $0.465 for 100 turns. A long evening of play costs well under a dollar. The pricing page lists the rates and the bonus levels for larger top-ups.

A working hosted setup in Python

The endpoint is OpenAI-compatible, so the official openai package works unchanged apart from the base URL and key. The model ID is uncensored. First get a key from the API key page; new accounts can claim a $0.50 trial there with one click, no payment details needed.

This script keeps a rolling history, streams the reply and trims old turns so the prompt stays small. The system prompt is the part worth your time: it sets the setting, the rules and the style.

Copyimport os
from openai import OpenAI

client = OpenAI(
    base_url="https://api.nsfwaiapi.com/v1",
    api_key=os.environ["NSFWAIAPI_KEY"],
)

SYSTEM = '''You are the narrator and all non-player characters in an adult fiction roleplay.
Setting: a rain-soaked port city, late autumn, gaslight and smuggler politics.
Rules:
- Write in third person, past tense, 2 to 4 short paragraphs per reply.
- Never write the player's actions, words or thoughts. Stop where the player decides.
- Keep each character's voice and goals consistent. Track who is present in the scene.
- All characters are adults. Do not write sexual content involving minors under any circumstances.
- Stay in the story. No disclaimers or out-of-character commentary unless the player asks with (OOC).'''

history = [{"role": "system", "content": SYSTEM}]
MAX_TURNS = 40  # keep the last 40 messages besides the system prompt

def say(user_text):
    history.append({"role": "user", "content": user_text})
    trimmed = [history[0]] + history[1:][-MAX_TURNS:]
    stream = client.chat.completions.create(
        model="uncensored",
        messages=trimmed,
        temperature=0.9,
        top_p=0.95,
        frequency_penalty=0.3,
        max_tokens=600,
        stream=True,
    )
    reply = ""
    for chunk in stream:
        if chunk.choices and chunk.choices[0].delta.content:
            piece = chunk.choices[0].delta.content
            reply += piece
            print(piece, end="", flush=True)
    print()
    history.append({"role": "assistant", "content": reply})

say("I push open the door of the Gull and Anchor and look for the harbor master.")

Because the window is 100,000 tokens, you can raise MAX_TURNS a lot before trimming matters. Trimming is still a good habit: shorter prompts cost less and keep the model focused on recent events.

Client settings for SillyTavern and similar apps

Most roleplay front ends have a connection screen for an OpenAI-compatible or custom chat completion source. SillyTavern, for example, lets you add an OpenAI-compatible endpoint. The fields are the same everywhere:

• Base or endpoint URL: https://api.nsfwaiapi.com/v1. Some apps want the full path ending in /chat/completions; if the app asks for a base, stop at /v1.
• API key: your key, sent as a bearer token.
• Model: uncensored. If the app can fetch a model list, it can call GET /v1/models.
• Streaming: on. Replies appear as they are written, and the app can stop early if you dislike the direction.

For sampling, a temperature around 0.8 to 1.0 suits narrative text, with top_p near 0.95. A small frequency or presence penalty reduces the repeated phrases that long chats tend to collect. Raise the penalty only if you see repetition, since high values make prose oddly avoidant. Stop sequences are passed through, so adding the player's name followed by a colon as a stop string helps if a model tries to speak for you. Set the app's context size to whatever you want to pay for; you do not need to fill all 100,000 tokens.

Keep concurrency in mind if you run several chat tabs or a bot for friends. A full key allows 8 concurrent requests and 300 per minute, and a trial key allows 2 and 60. A streamed reply holds its slot until the stream ends, so a stuck tab can use up a slot. The docs list the limits and the response headers, including X-Balance-USD, which shows your remaining credit.

A small routine that keeps long stories healthy

Long roleplay sessions go wrong in predictable ways, and a few habits prevent most of it. Write a short story summary every 20 or 30 turns and keep it near the top of the prompt, after the system message. Update it by hand or ask the model to produce it, then check that names, places and promises survived. Pin the facts that must never be forgotten, such as relationships and open threads, in the system prompt itself.

If a reply goes in a direction you dislike, regenerate it instead of arguing with it in the chat. Arguments pollute the history and teach the model that the scene is about the argument. When you do want to steer, add one clear line in brackets, such as a note about the next scene's mood, and let the next reply carry it. Keep character names consistent and give every non-player character a one-line voice note, because models blur characters that sound alike.

Honest limits of this model

It is one open-weight model, run by an independent provider and tuned to answer without content refusals for lawful adult use. It is not the official API of any model vendor and not a router across several. You cannot pick between models, and you should not expect it to match the best closed models on every task.

It generates text only. There are no images for your characters, no voice and no embeddings for a lorebook search, so if you want portraits or speech you need to add a separate service for each. There is no fine-tuning either. Quality will also depend on your prompt: a vague system prompt gives vague fiction, and a good character profile with example dialogue does more than any sampling tweak.

One rule is fixed. Sexual content involving minors is not allowed, and a setup that tries to produce it will not be supported. Keep characters adults, and keep your use lawful. For fiction, companion apps and character chat built for adults, the combination of fewer refusals and a long window is the useful part.

More guides

• What LLM API Is Free? Options, Limits and a Real Trial
• NSFW LLM API in Python: Streaming, JSON and Tools
• Uncensored LLM API: OpenAI Compatible Text Model