CODE

Code Explanation: batch.js

This file demonstrates parallel execution of multiple LLM prompts using separate context sequences, enabling concurrent processing for better performance.

Step-by-Step Code Breakdown

1. Import and Setup (Lines 1-10)

import {getLlama, LlamaChatSession} from "node-llama-cpp";
import path from "path";
import {fileURLToPath} from "url";
 
/**
 * Asynchronous execution improves performance in GAIA benchmarks,
 * multi-agent applications, and other high-throughput scenarios.
 */
 
const __dirname = path.dirname(fileURLToPath(import.meta.url));

• Standard imports for LLM interaction
• Comment explains the performance benefit
• GAIA benchmark: A standard for testing AI agent performance
• Useful for multi-agent systems that need to handle many requests

2. Model Path Configuration (Lines 11-16)

const modelPath = path.join(
    __dirname,
    "../",
    "models",
    "DeepSeek-R1-0528-Qwen3-8B-Q6_K.gguf"
)

• Uses DeepSeek-R1: An 8B parameter model optimized for reasoning
• Q6_K quantization: Balance between quality and size
• Model is loaded once and shared between sequences

3. Initialize Llama and Load Model (Lines 18-19)

const llama = await getLlama();
const model = await llama.loadModel({modelPath});

• Standard initialization
• Model is loaded into memory once
• Will be used by multiple sequences simultaneously

4. Create Context with Multiple Sequences (Lines 20-23)

const context = await model.createContext({
    sequences: 2,
    batchSize: 1024 // The number of tokens that can be processed at once by the GPU.
});

Key parameters:

• sequences: 2: Creates 2 independent conversation sequences

  • Each sequence has its own conversation history
  • Both share the same model and context memory pool
  • Can be processed in parallel

• batchSize: 1024: Maximum tokens processed per GPU batch

  • Larger = better GPU utilization
  • Smaller = lower memory usage
  • 1024 is a good balance for most GPUs

Why Multiple Sequences?

Single Sequence (Sequential)     Multiple Sequences (Parallel)
─────────────────────────       ──────────────────────────────
Process Prompt 1 → Response 1    Process Prompt 1 ──┐
Wait...                                              ├→ Both responses
Process Prompt 2 → Response 2    Process Prompt 2 ──┘   in parallel!

Total Time: T1 + T2              Total Time: max(T1, T2)

5. Get Individual Sequences (Lines 25-26)

const sequence1 = context.getSequence();
const sequence2 = context.getSequence();

• Retrieves two separate sequence objects from the context
• Each sequence maintains its own state
• They can be used independently for different conversations

6. Create Separate Sessions (Lines 28-33)

const session1 = new LlamaChatSession({
    contextSequence: sequence1
});
const session2 = new LlamaChatSession({
    contextSequence: sequence2
});

• Creates a chat session for each sequence
• Each session has its own conversation history
• Sessions are completely independent
• No system prompts in this example (could be added)

7. Define Questions (Lines 35-36)

const q1 = "Hi there, how are you?";
const q2 = "How much is 6+6?";

• Two completely different questions
• Will be processed simultaneously
• Different types: conversational vs. computational

8. Parallel Execution with Promise.all (Lines 38-44)

const [
    a1,
    a2
] = await Promise.all([
    session1.prompt(q1),
    session2.prompt(q2)
]);

How this works:

1. session1.prompt(q1) starts asynchronously
2. session2.prompt(q2) starts asynchronously (doesn’t wait for #1)
3. Promise.all() waits for BOTH to complete
4. Returns results in array: [response1, response2]
5. Destructures into a1 and a2

Key benefit: Both prompts are processed at the same time, not one after another!

9. Display Results (Lines 46-50)

console.log("User: " + q1);
console.log("AI: " + a1);
 
console.log("User: " + q2);
console.log("AI: " + a2);

• Outputs both question-answer pairs
• Results appear in order despite parallel processing

Key Concepts Demonstrated

1. Parallel Processing

Instead of:

// Sequential (slow)
const a1 = await session1.prompt(q1);  // Wait
const a2 = await session2.prompt(q2);  // Wait again

We use:

// Parallel (fast)
const [a1, a2] = await Promise.all([
    session1.prompt(q1),
    session2.prompt(q2)
]);

2. Context Sequences

A context can hold multiple independent sequences:

┌─────────────────────────────────────┐
│          Context (Shared)           │
│  ┌───────────────────────────────┐  │
│  │  Model Weights (8B params)    │  │
│  └───────────────────────────────┘  │
│                                     │
│  ┌─────────────┐  ┌─────────────┐  │
│  │ Sequence 1  │  │ Sequence 2  │  │
│  │ "Hi there"  │  │ "6+6?"      │  │
│  │ History...  │  │ History...  │  │
│  └─────────────┘  └─────────────┘  │
└─────────────────────────────────────┘

Performance Comparison

Sequential Execution

Request 1: 2 seconds
Request 2: 2 seconds
Total: 4 seconds

Parallel Execution (This Example)

Request 1: 2 seconds ──┐
Request 2: 2 seconds ──┤ Both running
Total: ~2 seconds      └─ simultaneously

Speedup: ~2x for 2 sequences, scales with more sequences

Use Cases

1. Multi-User Applications

// Handle multiple users simultaneously
const [user1Response, user2Response, user3Response] = await Promise.all([
    session1.prompt(user1Query),
    session2.prompt(user2Query),
    session3.prompt(user3Query)
]);

2. Multi-Agent Systems

// Multiple agents working on different tasks
const [
    plannerResponse,
    analyzerResponse,
    executorResponse
] = await Promise.all([
    plannerSession.prompt("Plan the task"),
    analyzerSession.prompt("Analyze the data"),
    executorSession.prompt("Execute step 1")
]);

3. Benchmarking

// Test multiple prompts for evaluation
const results = await Promise.all(
    testPrompts.map(prompt => session.prompt(prompt))
);

4. A/B Testing

// Test different system prompts
const [responseA, responseB] = await Promise.all([
    sessionWithPromptA.prompt(query),
    sessionWithPromptB.prompt(query)
]);

Resource Considerations

Memory Usage

Each sequence needs memory for:

• Conversation history
• Intermediate computations
• KV cache (key-value cache for transformer attention)

Rule of thumb: More sequences = more memory needed

GPU Utilization

• Single sequence: May underutilize GPU
• Multiple sequences: Better GPU utilization
• Too many sequences: May exceed VRAM, causing slowdown

Optimal Number of Sequences

Depends on:

• Available VRAM
• Model size
• Context length
• Batch size

Typical: 2-8 sequences for consumer GPUs

Limitations & Considerations

1. Shared Context Limit

All sequences share the same context memory pool:

Total context size: 8192 tokens
Sequence 1: 4096 tokens
Sequence 2: 4096 tokens
Maximum distribution!

2. Not True Parallelism for CPU

On CPU-only systems, sequences are interleaved, not truly parallel. Still provides better overall throughput.

3. Model Loading Overhead

The model is loaded once and shared, which is efficient. But initial loading still takes time.

Why This Matters for AI Agents

Efficiency in Production

Real-world agent systems need to:

• Handle multiple requests concurrently
• Respond quickly to users
• Make efficient use of hardware

Multi-Agent Architectures

Complex agent systems often have:

• Planner agent: Thinks about strategy
• Executor agent: Takes actions
• Critic agent: Evaluates results

These can run in parallel using separate sequences.

Scalability

This pattern is the foundation for:

• Web services with multiple users
• Batch processing of data
• Distributed agent systems

Best Practices

1. Match sequences to workload: Don’t create more than you need
2. Monitor memory usage: Each sequence consumes VRAM
3. Use appropriate batch size: Balance speed vs. memory
4. Clean up resources: Always dispose when done
5. Handle errors: Wrap Promise.all in try-catch

Expected Output

Running this script should output something like:

User: Hi there, how are you?
AI: Hello! I'm doing well, thank you for asking...

User: How much is 6+6?
AI: 12

Both responses appear quickly because they were processed simultaneously!