Llama 3.3 70B

Run Meta's Llama 3.3 70B model on Clore.ai GPUs

Newer version available! Meta released Llama 4 in April 2025 with MoE architecture — Scout (17B active, fits on RTX 4090) delivers similar quality at a fraction of the VRAM. Consider upgrading.

Meta's latest and most efficient 70B model on CLORE.AI GPUs.

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

Why Llama 3.3?

• Best 70B model - Matches Llama 3.1 405B performance at fraction of cost

• Multilingual - Supports 8 languages natively

• 128K context - Long document processing

• Open weights - Free for commercial use

Model Overview

Spec	Value
Parameters	70B
Context Length	128K tokens
Training Data	15T+ tokens
Languages	EN, DE, FR, IT, PT, HI, ES, TH
License	Llama 3.3 Community License

Performance vs Other Models

Benchmark	Llama 3.3 70B	Llama 3.1 405B	GPT-4o
MMLU	86.0	87.3	88.7
HumanEval	88.4	89.0	90.2
MATH	77.0	73.8	76.6
Multilingual	91.1	91.6	-

GPU Requirements

Multi-GPU 80GB-class rigs are not listed on the Clore.ai marketplace. The biggest boxes listed today are 4× RTX PRO 6000 Blackwell (96GB each, 380GB total) and 8–11× RTX 5090 (32GB each). A100 / H200 / B200 capacity is sold as bare metal on request. Check GPU Pricing & Availability before sizing a deployment.

Setup	VRAM	Performance	Cost
Q4 quantized	40GB	Good	A100 40GB (bare metal)
Q8 quantized	70GB	Better	A100 80GB (bare metal)
FP16 full	140GB	Best	2x A100 80GB (bare metal)

Recommended: A100 40GB with Q4 quantization for best price/performance.

Quick Deploy on CLORE.AI

Using Ollama (Easiest)

Docker Image:

ollama/ollama

Ports:

22/tcp
11434/http

After deploy:

ollama pull llama3.3
ollama run llama3.3

Using vLLM (Production)

Docker Image:

vllm/vllm-openai:latest

Ports:

22/tcp
8000/http

Command:

python -m vllm.entrypoints.openai.api_server \
    --model meta-llama/Llama-3.3-70B-Instruct \
    --tensor-parallel-size 1 \
    --max-model-len 32768 \
    --host 0.0.0.0

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.

Installation Methods

Method 1: Ollama (Recommended for Testing)

# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh

# Pull Llama 3.3 (auto-downloads Q4 version)
ollama pull llama3.3

# Run interactively
ollama run llama3.3

# Or serve API
ollama serve

API usage:

curl http://localhost:11434/api/generate -d '{
  "model": "llama3.3",
  "prompt": "Explain quantum computing in simple terms"
}'

Method 2: vLLM (Production)

pip install vllm

# Single GPU (A100 40GB with AWQ quantization)
python -m vllm.entrypoints.openai.api_server \
    --model casperhansen/llama-3.3-70b-instruct-awq \
    --quantization awq \
    --max-model-len 16384 \
    --host 0.0.0.0

# Multi-GPU (2x A100 for full precision)
python -m vllm.entrypoints.openai.api_server \
    --model meta-llama/Llama-3.3-70B-Instruct \
    --tensor-parallel-size 2 \
    --max-model-len 32768 \
    --host 0.0.0.0

API usage (OpenAI-compatible):

from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1", api_key="dummy")

response = client.chat.completions.create(
    model="meta-llama/Llama-3.3-70B-Instruct",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Write a Python function to calculate fibonacci numbers"}
    ],
    temperature=0.7,
    max_tokens=1024
)

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

Method 3: Transformers + bitsandbytes

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig

# 4-bit quantization config
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16
)

model_id = "meta-llama/Llama-3.3-70B-Instruct"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    quantization_config=bnb_config,
    device_map="auto"
)

# Generate
messages = [
    {"role": "system", "content": "You are a helpful coding assistant."},
    {"role": "user", "content": "Write a Python web scraper using BeautifulSoup"}
]

input_ids = tokenizer.apply_chat_template(
    messages,
    return_tensors="pt"
).to("cuda")

outputs = model.generate(
    input_ids,
    max_new_tokens=512,
    temperature=0.7,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Method 4: llama.cpp (CPU+GPU hybrid)

# Clone and build
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
make LLAMA_CUDA=1

# Download GGUF model
wget https://huggingface.co/bartowski/Llama-3.3-70B-Instruct-GGUF/resolve/main/Llama-3.3-70B-Instruct-Q4_K_M.gguf

# Run server
./llama-server \
    -m Llama-3.3-70B-Instruct-Q4_K_M.gguf \
    -c 8192 \
    -ngl 80 \
    --host 0.0.0.0 \
    --port 8080

Benchmarks

Throughput (tokens/second)

GPU	Q4	Q8	FP16
A100 40GB	25-30	-	-
A100 80GB	35-40	25-30	-
2x A100 80GB	50-60	40-45	30-35
H100 80GB	60-70	45-50	35-40

Time to First Token (TTFT)

GPU	Q4	FP16
A100 40GB	0.8-1.2s	-
A100 80GB	0.6-0.9s	-
2x A100 80GB	0.4-0.6s	0.8-1.0s

Context Length vs VRAM

Context	Q4 VRAM	Q8 VRAM
4K	38GB	72GB
8K	40GB	75GB
16K	44GB	80GB
32K	52GB	90GB
64K	68GB	110GB
128K	100GB	150GB

Use Cases

Code Generation

messages = [
    {"role": "system", "content": "You are an expert programmer. Write clean, efficient, well-documented code."},
    {"role": "user", "content": "Create a REST API in FastAPI with user authentication using JWT tokens"}
]

Document Analysis (Long Context)

# Load long document
with open("large_document.txt") as f:
    document = f.read()

messages = [
    {"role": "system", "content": "You are a document analyst. Provide detailed, accurate analysis."},
    {"role": "user", "content": f"Analyze this document and provide a summary with key points:\n\n{document}"}
]

Multilingual Tasks

messages = [
    {"role": "system", "content": "You are a multilingual assistant."},
    {"role": "user", "content": "Translate this to German, French, and Spanish: 'The quick brown fox jumps over the lazy dog'"}
]

Reasoning & Analysis

messages = [
    {"role": "system", "content": "Think step by step. Show your reasoning."},
    {"role": "user", "content": "A train leaves Station A at 9:00 AM traveling at 60 mph. Another train leaves Station B (300 miles away) at 10:00 AM traveling toward Station A at 90 mph. When and where do they meet?"}
]

Optimization Tips

Memory Optimization

# vLLM with memory optimization
python -m vllm.entrypoints.openai.api_server \
    --model casperhansen/llama-3.3-70b-instruct-awq \
    --quantization awq \
    --gpu-memory-utilization 0.95 \
    --max-model-len 8192

Speed Optimization

# Enable Flash Attention
python -m vllm.entrypoints.openai.api_server \
    --model meta-llama/Llama-3.3-70B-Instruct \
    --tensor-parallel-size 2 \
    --enable-prefix-caching

Batch Processing

# Process multiple requests efficiently
responses = client.chat.completions.create(
    model="meta-llama/Llama-3.3-70B-Instruct",
    messages=messages,
    n=4,  # Generate 4 responses
    temperature=0.8
)

Comparison with Other Models

Feature	Llama 3.3 70B	Llama 3.1 70B	Qwen 2.5 72B	Mixtral 8x22B
MMLU	86.0	83.6	85.3	77.8
Coding	88.4	80.5	85.4	75.5
Math	77.0	68.0	80.0	60.0
Context	128K	128K	128K	64K
Languages	8	8	29	8
License	Open	Open	Open	Open

Verdict: Llama 3.3 70B offers the best overall performance in its class, especially for coding and reasoning tasks.

Troubleshooting

Out of Memory

# Use AWQ quantization (most memory efficient)
--model casperhansen/llama-3.3-70b-instruct-awq --quantization awq

# Reduce context length
--max-model-len 8192

# Use tensor parallelism
--tensor-parallel-size 2

Slow First Response

• First request loads model to GPU - wait 30-60 seconds

• Use --enable-prefix-caching for faster subsequent requests

• Pre-warm with dummy request

Hugging Face Access

# Login to HF (required for gated model)
huggingface-cli login

# Or set environment variable
export HUGGING_FACE_HUB_TOKEN=hf_xxxxx

Cost Estimate

Setup	GPU	$/hour	tokens/$
Budget	A100 40GB (Q4)	~$0.17	~530K
Balanced	A100 80GB (Q4)	~$0.25	~500K
Performance	2x A100 80GB	~$0.50	~360K
Maximum	H100 80GB	~$0.50	~500K

Next Steps

• vLLM Guide - Production deployment

• Ollama Guide - Easy local setup

• Multi-GPU Setup - Scale to larger models

• API Integration - Build applications