picollm

picoLLM Inference Engine

Made in Vancouver, Canada by Picovoice

picoLLM Inference Engine is a highly accurate and cross-platform SDK optimized for running compressed large language models. picoLLM Inference Engine is:

• Accurate; picoLLM Compression improves GPTQ by significant margins
• Private; LLM inference runs 100% locally.
• Cross-Platform
  • Linux (x86_64), macOS (arm64, x86_64), and Windows (x86_64, arm64)
  • Raspberry Pi (3, 4, 5)
  • Android and iOS
  • Chrome, Safari, Edge, and Firefox
• Runs on CPU and GPU
• Free for open-weight models

Table of Contents

• picoLLM
  • Table of Contents
  • Showcases
    • Raspberry Pi
    • Android
    • iOS
    • Cross-Browser Local LLM
    • Llama-3-70B-Instruct on GeForce RTX 4090
    • Local LLM-Powered Voice Assistant on Raspberry Pi
    • Local Llama-3-8B-Instruct Voice Assistant on CPU
  • Accuracy
  • Models
  • AccessKey
  • Demos
    • Python
    • .NET
    • Node.js
    • Android
    • iOS
    • Web
    • C
  • SDKs
    • Python
    • .NET
    • Node.js
    • Android
    • iOS
    • Web
    • C
  • Releases

Showcases

Raspberry Pi

Android

iOS

Cross-Browser Local LLM

Live Demo — Works offline!

Llama-3-70B-Instruct on GeForce RTX 4090

Local LLM-Powered Voice Assistant on Raspberry Pi

Local Llama-3-8B-Instruct Voice Assistant on CPU

Accuracy

picoLLM Compression is a novel large language model (LLM) quantization algorithm developed within Picovoice. Given a task-specific cost function, picoLLM Compression automatically learns the optimal bit allocation strategy across and within LLM's weights. Existing techniques require a fixed bit allocation scheme, which is subpar.

For example, picoLLM Compression recovers MMLU score degradation of widely adopted GPTQ by 91%, 99%, and 100% at 2, 3, and 4-bit settings. The figure below depicts the MMLU comparison between picoLLM and GPTQ for Llama-3-8b [1].

Models

picoLLM Inference Engine supports the following open-weight models. The models are on Picovoice Console.

• DeepSeek-OCR-2
  • deepseek-ocr-2
• EmbeddingGemma
  • embeddinggemma-300m
• Gemma
  • gemma-2b
  • gemma-2b-it
  • gemma-7b
  • gemma-7b-it
• Gemma3
  • gemma-3-270m
  • gemma-3-270m-it
• Llama-2
  • llama-2-7b
  • llama-2-7b-chat
  • llama-2-13b
  • llama-2-13b-chat
  • llama-2-70b
  • llama-2-70b-chat
• Llama-3
  • llama-3-8b
  • llama-3-8b-instruct
  • llama-3-70b
  • llama-3-70b-instruct
• Llama-3.2
  • llama3.2-1b-instruct
  • llama3.2-3b-instruct
• Mistral
  • mistral-7b-v0.1
  • mistral-7b-instruct-v0.1
  • mistral-7b-instruct-v0.2
• Mixtral
  • mixtral-8x7b-v0.1
  • mixtral-8x7b-instruct-v0.1
• Phi-2
  • phi2
• Phi-3
  • phi3
• Phi-3.5
  • phi3.5
• Qwen3-VL
  • qwen3-vl-2b-it

AccessKey

AccessKey is your authentication and authorization token for deploying Picovoice SDKs, including picoLLM. Anyone who is using Picovoice needs to have a valid AccessKey. You must keep your AccessKey secret. You would need internet connectivity to validate your AccessKey with Picovoice license servers, even though the LLM inference is running 100% offline.

AccessKey also verifies that your usage is within the limits of your account. You can see your usage limits and real-time usage on your Picovoice Console Profile. To continue using Picovoice after your trial or renew and adjust your usage limits, please reach out to our Enterprise Sales Team or your existing Picovoice contact.

Demos

Python Demos

Install the demo package:

pip3 install picollmdemo

Run the following in the terminal:

picollm_demo_completion --access_key ${ACCESS_KEY} --model_path ${MODEL_PATH} --prompt ${PROMPT}

Replace ${ACCESS_KEY} with yours obtained from Picovoice Console, ${MODEL_PATH} with the path to a model file downloaded from Picovoice Console, and ${PROMPT} with a prompt string.

For more information about Python demos go to demo/python.

.NET Demos

From demo/dotnet/PicoLLMDemo build and run the demo:

dotnet build -c CompletionDemo.Release
dotnet run -c CompletionDemo.Release -- --access_key ${ACCESS_KEY} --model_path ${MODEL_PATH} --prompt ${PROMPT}

Replace ${ACCESS_KEY} with yours obtained from Picovoice Console, ${MODEL_PATH} with the path to a model file downloaded from Picovoice Console, and ${PROMPT} with a prompt string.

For more information about .NET demos go to demo/dotnet.

Node.js Demos

Install the demo package:

yarn global add @picovoice/picollm-node-demo

Run the following in the terminal:

picollm-completion-demo --access_key ${ACCESS_KEY} --model_path ${MODEL_PATH} --prompt ${PROMPT}

Replace ${ACCESS_KEY} with yours obtained from Picovoice Console, ${MODEL_PATH} with the path to a model file downloaded from Picovoice Console, and ${PROMPT} with a prompt string.

For more information about Node.js demos go to Node.js demo.

Android Demos

Using Android Studio, open the Completion demo as an Android project, copy your AccessKey into MainActivity.java, and run the application.

To learn about how to use picoLLM in a chat application, try out the Chat demo.

For more information about Android demos go to demo/android.

iOS Demos

To run the completion demo, go to demo/ios/Completion and run:

pod install

Replace let ACCESS_KEY = "${YOUR_ACCESS_KEY_HERE}" in the file VieModel.swift with your AccessKey obtained from Picovoice Console.

Then, using Xcode, open the generated PicoLLMCompletionDemo.xcworkspace and run the application.

To learn about how to use picoLLM in a chat application, try out the Chat demo.

For more information about iOS demos go to demo/ios.

Web Demos

From demo/web run the following in the terminal:

yarn
yarn start

(or)

npm install
npm run start

Open http://localhost:5000 in your browser to try the demo.

C Demos

Build the demo:

cmake -S demo/c/ -B demo/c/build && cmake --build demo/c/build

Run the demo:

./demo/c/build/picollm_demo_completion -a ${ACCESS_KEY} -l ${LIBRARY_PATH} -m ${MODEL_FILE_PATH} -p ${PROMPT}

Replace ${ACCESS_KEY} with yours obtained from Picovoice Console, ${LIBRARY_PATH} with the path to the shared library file located in the lib directory, ${MODEL_FILE_PATH} with the path to a model file downloaded from Picovoice Console, and ${PROMPT} with a prompt string.

For more information about C demos go to demo/c.

SDKs

Python SDK

Install the Python SDK:

pip3 install picollm

Create an instance of the engine and generate a prompt completion:

import picollm

pllm = picollm.create(
    access_key='${ACCESS_KEY}',
    model_path='${MODEL_PATH}')

res = pllm.generate('${PROMPT}')
print(res.completion)

Replace ${ACCESS_KEY} with yours obtained from Picovoice Console, ${MODEL_PATH} to the path to a model file downloaded from Picovoice Console, and ${PROMPT} to a prompt string. Finally, when done be sure to explicitly release the resources using pllm.release().

.NET SDK

Install the .NET SDK using NuGet or the dotnet CLI:

dotnet add package PicoLLM

Create an instance of the engine and generate a prompt completion:

using Pv;

PicoLLM pllm = PicoLLM.Create("${ACCESS_KEY}", "${MODEL_PATH}");

PicoLLMCompletion res = pllm.Generate('${PROMPT}');
Console.WriteLine(res.Completion);

Replace ${ACCESS_KEY} with yours obtained from Picovoice Console, ${MODEL_PATH} to the path to a model file downloaded from Picovoice Console, and ${PROMPT} to a prompt string.

PicoLLM will have its resources freed by the garbage collector, but to have resources freed immediately after use, wrap it in a using statement or call .Dispose() directly:

using(PicoLLM pllm = PicoLLM.Create(accessKey, modelPath))
{
    // .. picoLLM usage here
}

Node.js SDK

Install the Node.js SDK:

yarn add @picovoice/picollm-node

Create instances of the picoLLM class:

const { PicoLLM } = require("@picovoice/picollm-node");
const pllm = new PicoLLM('${ACCESS_KEY}', '${MODEL_PATH}');

const res = await pllm.generate('${PROMPT}');
console.log(res.completion);

Replace ${ACCESS_KEY} with yours obtained from Picovoice Console, ${MODEL_PATH} to the path to a model file downloaded from Picovoice Console, and ${PROMPT} to a prompt string. Finally, when done be sure to explicitly release the resources using pllm.release().

Android SDK

Create an instance of the inference engine and generate a prompt completion:

import ai.picovoice.picollm.*;

try {
    PicoLLM picollm = new PicoLLM.Builder()
        .setAccessKey("${ACCESS_KEY}")
        .setModelPath("${MODEL_PATH}")
        .build();
    PicoLLMCompletion res = picollm.generate(
        "${PROMPT}",
        new PicoLLMGenerateParams.Builder().build());
} catch (PicoLLMException e) { }

Replace ${ACCESS_KEY} with your AccessKey from Picovoice Console, ${MODEL_PATH} to the path to a model file downloaded from Picovoice Console, and ${PROMPT} to a prompt string. Finally, when done be sure to explicitly release the resources using picollm.delete().

iOS SDK

Create an instance of the engine and generate a prompt completion:

import PicoLLM

let pllm = try PicoLLM(
    accessKey: "${ACCESS_KEY}",
    modelPath: "${MODEL_PATH}")

let res = pllm.generate(prompt: "${PROMPT}")
print(res.completion)

Replace ${ACCESS_KEY} with yours obtained from Picovoice Console, ${MODEL_PATH} to the path to a model file downloaded from Picovoice Console, and ${PROMPT} to a prompt string.

Web SDK

Install the web SDK using yarn:

yarn add @picovoice/picollm-web

or using npm:

npm install --save @picovoice/picollm-web

Create an instance of the engine using PicoLLMWorker and transcribe an audio file:

import { PicoLLMWorker } from "@picovoice/picollm-web";

const picoLLMModel = {
  modelFile: '${MODEL_FILE}'
}

const picoLLM = await PicoLLMWorker.create(
  "${ACCESS_KEY}",
  picoLLMModel
);

const res = await picoLLM.generate(`${PROMPT}`);
console.log(res.completion);

Replace ${ACCESS_KEY} with yours obtained from Picovoice Console, ${MODEL_FILE} with the contents of the model file as File, Blob or URL (path to model file) format and ${PROMPT} with a prompt string. Finally, when done release the resources using picoLLM.release().

C SDK

Create an instance of the engine and generate a prompt completion:

pv_picollm_t *pllm = NULL;
pv_picollm_init(
    "${ACCESS_KEY}",
    "${MODEL_PATH}",
    "best",
    false,
    &pllm);

pv_picollm_usage_t usage;
pv_picollm_endpoint_t endpoint;
int32_t num_completion_tokens;
pv_picollm_completion_token_t *completion_tokens;
char *output;
pv_picollm_generate(
    pllm,
    "${PROMPT}",
    -1,    // completion_token_limit
    NULL,  // stop_phrases
    0,     // num_stop_phrases
    -1,    // seed
    0.f,   // presence_penalty
    0.f,   // frequency_penalty
    0.f,   // temperature
    1.f,   // top_p
    0,     // num_top_choices
    NULL,  // stream_callback
    NULL,  // stream_callback_context
    &usage,
    &endpoint,
    &completion_tokens,
    &num_completion_tokens,
    &output);
printf("%s\n", output);

Replace ${ACCESS_KEY} with yours obtained from Picovoice Console, ${MODEL_PATH} to the path to a model file downloaded from Picovoice Console, and ${PROMPT} to a prompt string.

Finally, when done, be sure to release the resources explicitly:

pv_picollm_delete(pllm);

Releases

v2.1.0 - April 27, 2025

• Added support for embedding, vision, and OCR models

v2.0.0 - December 18, 2025

• Improved engine performance
• Improved support for running on GPU or multiple CPU cores

v1.3.0 - March 14th, 2025

• Performance improvements
• Significant performance improvements for iOS and Web

v1.2.0 - November 26th, 2024

• Performance improvements
• Added support for phi3.5

v1.1.0 - October 1st, 2024

• Added interrupt() function for halting completion generation early
• Performance improvements
• Added support for phi3
• Bug fixes

v1.0.0 - May 28th, 2024

• Initial release