Overview

Provider Type

Local

API Endpoint


                                                        http://localhost:1234

Free Tier Highlights

Hardware limited

Quick Start Guide

1

Download from https://lmstudio.ai/

2

Install and Run

3

Search 'Llama 3'

4

Click Download (Recommended Quant)

5

Go to Chat -> Select Model

6

Start Chatting

Available Models

Model Name	ID	Context	Capabilities
Llama 3.1 (Any Size) Free	`llama-3.1-8b-instruct`	Varies	-
Gemma 2 (Any Size) Free	`gemma-2-9b-it`	Varies	Reasoning
Mistral (Any version) Free	`mistral-nemo-instruct`	Varies	-
Phi-3 (Any version) Free	`Phi-3-mini-4k-instruct`	Varies	-

Integration Examples

Ready-to-use code snippets for your applications.

Select a Model

Select Model

main.py

from openai import OpenAI

# LM Studio runs a local OpenAI-compatible server on port 1234
client = OpenAI(
    api_key="lm-studio",
    base_url="http://localhost:1234/v1"
)

response = client.chat.completions.create(
    model="local-model",
    messages=[
        {"role": "user", "content": "What can you help me with?"}
    ]
)

print(response.choices[0].message.content)

index.js

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: 'lm-studio',
  baseURL: 'http://localhost:1234/v1'
});

const response = await client.chat.completions.create({
  model: 'local-model',
  messages: [
    { role: 'user', content: 'What can you help me with?' }
  ]
});

console.log(response.choices[0].message.content);

terminal

# Start LM Studio server first, then:
curl http://localhost:1234/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "local-model",
    "messages": [
      {
        "role": "user",
        "content": "What can you help me with?"
      }
    ]
  }'

example.php

<?php
$apiKey = 'YOUR_API_KEY';
$url = "http://localhost:1234/models/llama-3.1-8b-instruct:generateContent?key=$apiKey";

$data = [
    "contents" => [
        ["parts" => [["text" => "Explain quantum mechanics"]]]
    ]
];

$ch = curl_init($url);
curl_setopt($ch, CURLOPT_RETURNTRANSFER, true);
curl_setopt($ch, CURLOPT_POST, true);
curl_setopt($ch, CURLOPT_POSTFIELDS, json_encode($data));
curl_setopt($ch, CURLOPT_HTTPHEADER, ['Content-Type: application/json']);

$response = curl_exec($ch);
curl_close($ch);

echo $response;
?>

main.go

package main

import (
    "context"
    "fmt"
    "log"
    "github.com/google/generative-ai-go/genai"
    "google.golang.org/api/option"
)

func main() {
    ctx := context.Background()
    client, err := genai.NewClient(ctx, option.WithAPIKey("YOUR_API_KEY"))
    if err != nil { log.Fatal(err) }
    defer client.Close()

    model := client.GenerativeModel("llama-3.1-8b-instruct")
    resp, err := model.GenerateContent(ctx, genai.Text("Explain quantum mechanics"))
    if err != nil { log.Fatal(err) }
    
    // ...
}

main.py

from openai import OpenAI

# LM Studio runs a local OpenAI-compatible server on port 1234
client = OpenAI(
    api_key="lm-studio",
    base_url="http://localhost:1234/v1"
)

response = client.chat.completions.create(
    model="local-model",
    messages=[
        {"role": "user", "content": "What can you help me with?"}
    ]
)

print(response.choices[0].message.content)

index.js

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: 'lm-studio',
  baseURL: 'http://localhost:1234/v1'
});

const response = await client.chat.completions.create({
  model: 'local-model',
  messages: [
    { role: 'user', content: 'What can you help me with?' }
  ]
});

console.log(response.choices[0].message.content);

terminal

# Start LM Studio server first, then:
curl http://localhost:1234/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "local-model",
    "messages": [
      {
        "role": "user",
        "content": "What can you help me with?"
      }
    ]
  }'

example.php

<?php
$apiKey = 'YOUR_API_KEY';
$url = "http://localhost:1234/models/gemma-2-9b-it:generateContent?key=$apiKey";

$data = [
    "contents" => [
        ["parts" => [["text" => "Explain quantum mechanics"]]]
    ]
];

$ch = curl_init($url);
curl_setopt($ch, CURLOPT_RETURNTRANSFER, true);
curl_setopt($ch, CURLOPT_POST, true);
curl_setopt($ch, CURLOPT_POSTFIELDS, json_encode($data));
curl_setopt($ch, CURLOPT_HTTPHEADER, ['Content-Type: application/json']);

$response = curl_exec($ch);
curl_close($ch);

echo $response;
?>

main.go

package main

import (
    "context"
    "fmt"
    "log"
    "github.com/google/generative-ai-go/genai"
    "google.golang.org/api/option"
)

func main() {
    ctx := context.Background()
    client, err := genai.NewClient(ctx, option.WithAPIKey("YOUR_API_KEY"))
    if err != nil { log.Fatal(err) }
    defer client.Close()

    model := client.GenerativeModel("gemma-2-9b-it")
    resp, err := model.GenerateContent(ctx, genai.Text("Explain quantum mechanics"))
    if err != nil { log.Fatal(err) }
    
    // ...
}

main.py

from openai import OpenAI

# LM Studio runs a local OpenAI-compatible server on port 1234
client = OpenAI(
    api_key="lm-studio",
    base_url="http://localhost:1234/v1"
)

response = client.chat.completions.create(
    model="local-model",
    messages=[
        {"role": "user", "content": "What can you help me with?"}
    ]
)

print(response.choices[0].message.content)

index.js

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: 'lm-studio',
  baseURL: 'http://localhost:1234/v1'
});

const response = await client.chat.completions.create({
  model: 'local-model',
  messages: [
    { role: 'user', content: 'What can you help me with?' }
  ]
});

console.log(response.choices[0].message.content);

terminal

# Start LM Studio server first, then:
curl http://localhost:1234/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "local-model",
    "messages": [
      {
        "role": "user",
        "content": "What can you help me with?"
      }
    ]
  }'

example.php

<?php
$apiKey = 'YOUR_API_KEY';
$url = "http://localhost:1234/models/mistral-nemo-instruct:generateContent?key=$apiKey";

$data = [
    "contents" => [
        ["parts" => [["text" => "Explain quantum mechanics"]]]
    ]
];

$ch = curl_init($url);
curl_setopt($ch, CURLOPT_RETURNTRANSFER, true);
curl_setopt($ch, CURLOPT_POST, true);
curl_setopt($ch, CURLOPT_POSTFIELDS, json_encode($data));
curl_setopt($ch, CURLOPT_HTTPHEADER, ['Content-Type: application/json']);

$response = curl_exec($ch);
curl_close($ch);

echo $response;
?>

main.go

package main

import (
    "context"
    "fmt"
    "log"
    "github.com/google/generative-ai-go/genai"
    "google.golang.org/api/option"
)

func main() {
    ctx := context.Background()
    client, err := genai.NewClient(ctx, option.WithAPIKey("YOUR_API_KEY"))
    if err != nil { log.Fatal(err) }
    defer client.Close()

    model := client.GenerativeModel("mistral-nemo-instruct")
    resp, err := model.GenerateContent(ctx, genai.Text("Explain quantum mechanics"))
    if err != nil { log.Fatal(err) }
    
    // ...
}

main.py

from openai import OpenAI

# LM Studio runs a local OpenAI-compatible server on port 1234
client = OpenAI(
    api_key="lm-studio",
    base_url="http://localhost:1234/v1"
)

response = client.chat.completions.create(
    model="local-model",
    messages=[
        {"role": "user", "content": "What can you help me with?"}
    ]
)

print(response.choices[0].message.content)

index.js

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: 'lm-studio',
  baseURL: 'http://localhost:1234/v1'
});

const response = await client.chat.completions.create({
  model: 'local-model',
  messages: [
    { role: 'user', content: 'What can you help me with?' }
  ]
});

console.log(response.choices[0].message.content);

terminal

# Start LM Studio server first, then:
curl http://localhost:1234/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "local-model",
    "messages": [
      {
        "role": "user",
        "content": "What can you help me with?"
      }
    ]
  }'

example.php

<?php
$apiKey = 'YOUR_API_KEY';
$url = "http://localhost:1234/models/Phi-3-mini-4k-instruct:generateContent?key=$apiKey";

$data = [
    "contents" => [
        ["parts" => [["text" => "Explain quantum mechanics"]]]
    ]
];

$ch = curl_init($url);
curl_setopt($ch, CURLOPT_RETURNTRANSFER, true);
curl_setopt($ch, CURLOPT_POST, true);
curl_setopt($ch, CURLOPT_POSTFIELDS, json_encode($data));
curl_setopt($ch, CURLOPT_HTTPHEADER, ['Content-Type: application/json']);

$response = curl_exec($ch);
curl_close($ch);

echo $response;
?>

main.go

package main

import (
    "context"
    "fmt"
    "log"
    "github.com/google/generative-ai-go/genai"
    "google.golang.org/api/option"
)

func main() {
    ctx := context.Background()
    client, err := genai.NewClient(ctx, option.WithAPIKey("YOUR_API_KEY"))
    if err != nil { log.Fatal(err) }
    defer client.Close()

    model := client.GenerativeModel("Phi-3-mini-4k-instruct")
    resp, err := model.GenerateContent(ctx, genai.Text("Explain quantum mechanics"))
    if err != nil { log.Fatal(err) }
    
    // ...
}

Free Tier Pricing & Limits

Rate Limit

Requests per minute

Hardware limited

Daily Quota

Requests per day

Unlimited

Token Limit

Tokens per minute

Unlimited

Monthly Quota

Per month limit

Free

Use Cases

First time local LLM users

Developers needing a local API mock

Privacy-conscious chatting

Testing different model sizes

Offline productivity

Limitations & Considerations

Closed source application

Large downloads

Hardware dependent performance

No model training features

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LM Studio

Overview

Provider Type

API Endpoint

Free Tier Highlights

Why Choose LM Studio?

Quick Start Guide

Download from https://lmstudio.ai/

Install and Run

Search 'Llama 3'

Click Download (Recommended Quant)

Go to Chat -> Select Model

Start Chatting

Available Models

Integration Examples

Select Model

Free Tier Pricing & Limits

Rate Limit

Daily Quota

Token Limit

Monthly Quota

Use Cases

Limitations & Considerations

Community Hub

Quick Reactions

Add Discussion

Recent Activity

Ready to Get Started?

Cookie Consent

LM Studio

Overview

Provider Type

API Endpoint

Free Tier Highlights

Why Choose LM Studio?

Quick Start Guide

Download from https://lmstudio.ai/

Install and Run

Search 'Llama 3'

Click Download (Recommended Quant)

Go to Chat -> Select Model

Start Chatting

Available Models

Integration Examples

Select Model

Free Tier Pricing & Limits

Rate Limit

Daily Quota

Token Limit

Monthly Quota

Use Cases

Limitations & Considerations

Community Hub

Quick Reactions

Add Discussion

Recent Activity

Suggest an Edit

Ready to Get Started?