Llama.cpp Server

Run LLMs efficiently with llama.cpp server on GPU.

All examples can be run on GPU servers rented through CLORE.AI Marketplace.

Server Requirements

Parameter	Minimum	Recommended
RAM	8GB	16GB+
VRAM	6GB	8GB+
Network	200Mbps	500Mbps+
Startup Time	~2-5 minutes	-

Llama.cpp is memory-efficient due to GGUF quantization. 7B models can run on 6-8GB VRAM.

Renting on CLORE.AI

1. Visit CLORE.AI Marketplace

2. Filter by GPU type, VRAM, and price

3. Choose On-Demand (fixed rate) or Spot (bid price)

4. Configure your order:

   • Select Docker image

   • Set ports (TCP for SSH, HTTP for web UIs)

   • Add environment variables if needed

   • Enter startup command

5. Select payment: CLORE, BTC, or USDT/USDC

6. Create order and wait for deployment

Access Your Server

• Find connection details in My Orders

• Web interfaces: Use the HTTP port URL

• SSH: ssh -p <port> root@<proxy-address>

What is Llama.cpp?

Llama.cpp is the fastest CPU/GPU inference engine for LLMs:

• Supports GGUF quantized models

• Low memory usage

• OpenAI-compatible API

• Multi-user support

Quantization Levels

Format	Size (7B)	Speed	Quality
Q2_K	2.8GB	Fastest	Low
Q4_K_M	4.1GB	Fast	Good
Q5_K_M	4.8GB	Medium	Great
Q6_K	5.5GB	Slower	Excellent
Q8_0	7.2GB	Slowest	Best

Quick Deploy

Docker Image:

ghcr.io/ggerganov/llama.cpp:server-cuda

Ports:

22/tcp
8080/http

Command:

# Download model
wget https://huggingface.co/bartowski/Meta-Llama-3.1-8B-Instruct-GGUF/resolve/main/Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf

# Run server
./llama-server \
    -m Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf \
    --host 0.0.0.0 \
    --port 8080 \
    -ngl 35 \
    -c 4096

Accessing Your Service

After deployment, find your http_pub URL in My Orders:

1. Go to My Orders page

2. Click on your order

3. Find the http_pub URL (e.g., abc123.clorecloud.net)

Use https://YOUR_HTTP_PUB_URL instead of localhost in examples below.

Verify It's Working

# Check health
curl https://your-http-pub.clorecloud.net/health

# Get server info
curl https://your-http-pub.clorecloud.net/props

If you get HTTP 502, the service may still be starting or downloading the model. Wait 2-5 minutes and retry.

Complete API Reference

Standard Endpoints

Endpoint	Method	Description
/health	GET	Health check
/v1/models	GET	List models
/v1/chat/completions	POST	Chat (OpenAI compatible)
/v1/completions	POST	Text completion (OpenAI compatible)
/v1/embeddings	POST	Generate embeddings
/completion	POST	Native completion endpoint
/tokenize	POST	Tokenize text
/detokenize	POST	Detokenize tokens
/props	GET	Server properties
/metrics	GET	Prometheus metrics

Tokenize Text

curl https://your-http-pub.clorecloud.net/tokenize \
    -H "Content-Type: application/json" \
    -d '{"content": "Hello world"}'

Response:

{"tokens": [15496, 1917]}

Server Properties

curl https://your-http-pub.clorecloud.net/props

Response:

{
  "total_slots": 1,
  "chat_template": "...",
  "default_generation_settings": {...}
}

Build from Source

# Clone repo
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp

# Build with CUDA
make LLAMA_CUDA=1

# Or with CMake
mkdir build && cd build
cmake .. -DLLAMA_CUDA=ON
cmake --build . --config Release

Download Models

# Llama 3.1 8B
wget https://huggingface.co/bartowski/Meta-Llama-3.1-8B-Instruct-GGUF/resolve/main/Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf

# Mistral 7B
wget https://huggingface.co/bartowski/Mistral-7B-Instruct-v0.3-GGUF/resolve/main/Mistral-7B-Instruct-v0.3-Q4_K_M.gguf

# Mixtral 8x7B
wget https://huggingface.co/bartowski/Mixtral-8x7B-Instruct-v0.1-GGUF/resolve/main/Mixtral-8x7B-Instruct-v0.1-Q4_K_M.gguf

# Phi-2
wget https://huggingface.co/bartowski/Phi-4-GGUF/resolve/main/Phi-4-Q4_K_M.gguf

# CodeLlama 7B
wget https://huggingface.co/bartowski/CodeLlama-7B-Instruct-GGUF/resolve/main/CodeLlama-7B-Instruct-Q4_K_M.gguf

Server Options

Basic Server

./llama-server \
    -m model.gguf \
    --host 0.0.0.0 \
    --port 8080

Full GPU Offload

./llama-server \
    -m model.gguf \
    --host 0.0.0.0 \
    --port 8080 \
    -ngl 99 \           # GPU layers (99 = all)
    -c 4096 \           # Context size
    -t 8 \              # CPU threads
    --parallel 4        # Concurrent requests

All Options

./llama-server \
    -m model.gguf \           # Model file
    --host 0.0.0.0 \          # Bind address
    --port 8080 \             # Port
    -ngl 35 \                 # GPU layers
    -c 4096 \                 # Context size
    -t 8 \                    # Threads
    -b 512 \                  # Batch size
    --parallel 4 \            # Parallel requests
    --mlock \                 # Lock memory
    --no-mmap \               # Disable mmap
    --cont-batching \         # Continuous batching
    --flash-attn \            # Flash attention
    --metrics                 # Enable metrics endpoint

API Usage

Chat Completions (OpenAI Compatible)

import openai

client = openai.OpenAI(
    base_url="http://localhost:8080/v1",
    api_key="not-needed"
)

response = client.chat.completions.create(
    model="llama-3.1-8b",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "What is machine learning?"}
    ],
    temperature=0.7,
    max_tokens=500
)

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

Streaming

stream = client.chat.completions.create(
    model="llama-3.1-8b",
    messages=[{"role": "user", "content": "Write a story"}],
    stream=True
)

for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="", flush=True)

Text Completion

response = client.completions.create(
    model="llama-3.1-8b",
    prompt="The future of AI is",
    max_tokens=100,
    temperature=0.8
)

print(response.choices[0].text)

Embeddings

response = client.embeddings.create(
    model="llama-3.1-8b",
    input="Hello, world!"
)

print(f"Embedding: {response.data[0].embedding[:5]}...")

cURL Examples

Chat

curl http://localhost:8080/v1/chat/completions \
    -H "Content-Type: application/json" \
    -d '{
        "model": "llama-3.1-8b",
        "messages": [
            {"role": "user", "content": "Hello!"}
        ]
    }'

Completion

curl http://localhost:8080/completion \
    -H "Content-Type: application/json" \
    -d '{
        "prompt": "Building a website requires",
        "n_predict": 128,
        "temperature": 0.7
    }'

Health Check

curl http://localhost:8080/health

Metrics

curl http://localhost:8080/metrics

Multi-GPU

# Split across GPUs
./llama-server \
    -m model.gguf \
    -ngl 99 \
    --tensor-split 0.5,0.5 \  # Split between 2 GPUs
    --main-gpu 0              # Primary GPU

Memory Optimization

For Limited VRAM

# Partial offload
./llama-server -m model.gguf -ngl 20 -c 2048

# Use smaller quantization

# Download Q2_K or Q3_K instead of Q4_K

For Maximum Speed

./llama-server \
    -m model.gguf \
    -ngl 99 \
    --flash-attn \
    --cont-batching \
    --parallel 8 \
    -b 1024

Model-Specific Templates

Llama 2 Chat

./llama-server -m Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf \
    --chat-template llama2

Mistral Instruct

./llama-server -m mistral-7b-instruct.gguf \
    --chat-template mistral

ChatML (Many Models)

./llama-server -m model.gguf \
    --chat-template chatml

Python Server Wrapper

import subprocess
import requests
import time

class LlamaCppServer:
    def __init__(self, model_path, port=8080, gpu_layers=35):
        self.port = port
        self.process = subprocess.Popen([
            "./llama-server",
            "-m", model_path,
            "--host", "0.0.0.0",
            "--port", str(port),
            "-ngl", str(gpu_layers),
            "-c", "4096"
        ])
        self._wait_for_ready()

    def _wait_for_ready(self, timeout=60):
        start = time.time()
        while time.time() - start < timeout:
            try:
                r = requests.get(f"http://localhost:{self.port}/health")
                if r.status_code == 200:
                    return
            except:
                pass
            time.sleep(1)
        raise TimeoutError("Server didn't start")

    def chat(self, messages, **kwargs):
        response = requests.post(
            f"http://localhost:{self.port}/v1/chat/completions",
            json={"messages": messages, **kwargs}
        )
        return response.json()

    def stop(self):
        self.process.terminate()

# Usage
server = LlamaCppServer("llama-3.1-8b.gguf")
result = server.chat([{"role": "user", "content": "Hello!"}])
print(result["choices"][0]["message"]["content"])
server.stop()

Benchmarking

# Built-in benchmark
./llama-bench -m model.gguf -ngl 99

# Output includes:

# - Tokens per second

# - Memory usage

# - Load time

Performance Comparison

Model	GPU	Quantization	Tokens/sec
Llama 3.1 8B	RTX 3090	Q4_K_M	~100
Llama 3.1 8B	RTX 4090	Q4_K_M	~150
Llama 3.1 8B	RTX 3090	Q4_K_M	~60
Mistral 7B	RTX 3090	Q4_K_M	~110
Mixtral 8x7B	A100	Q4_K_M	~50

Troubleshooting

CUDA Not Detected

# Rebuild with CUDA
make clean
make LLAMA_CUDA=1

# Check CUDA
nvidia-smi

Out of Memory

# Reduce GPU layers
-ngl 20  # Instead of 99

# Reduce context
-c 2048  # Instead of 4096

# Use smaller quant

# Q4_K_S instead of Q4_K_M

Slow Generation

# Increase batch size
-b 1024

# Enable flash attention
--flash-attn

# Enable continuous batching
--cont-batching

Production Setup

Systemd Service

# /etc/systemd/system/llama.service
[Unit]
Description=Llama.cpp Server
After=network.target

[Service]
Type=simple
ExecStart=/opt/llama.cpp/llama-server -m /models/model.gguf -ngl 99 --host 0.0.0.0 --port 8080
Restart=always

[Install]
WantedBy=multi-user.target

With nginx

upstream llama {
    server localhost:8080;
}

server {
    listen 80;

    location / {
        proxy_pass http://llama;
        proxy_http_version 1.1;
        proxy_set_header Connection "";
    }
}

Cost Estimate

Typical CLORE.AI marketplace rates (as of 2024):

GPU	Hourly Rate	Daily Rate	4-Hour Session
RTX 3060	~$0.03	~$0.70	~$0.12
RTX 3090	~$0.06	~$1.50	~$0.25
RTX 4090	~$0.10	~$2.30	~$0.40
A100 40GB	~$0.17	~$4.00	~$0.70
A100 80GB	~$0.25	~$6.00	~$1.00

Prices vary by provider and demand. Check CLORE.AI Marketplace for current rates.

Save money:

• Use Spot market for flexible workloads (often 30-50% cheaper)

• Pay with CLORE tokens

• Compare prices across different providers

Next Steps

• vLLM Inference - Higher throughput

• ExLlamaV2 - Faster inference

• Text Generation WebUI - Web interface