🔥 Qwen/Qwen3-32B
High-performance 32B parameter LLM. Excellent for reasoning, coding, and multilingual tasks.
Input: $0.15/M tokensOutput: $0.44/M tokens
33.54M runs in 7 days
Ultra-fast, cost-efficient 8B model perfect for high-throughput and latency-sensitive applications.
Parameters
8B
Context
128,000 tokens
Organization
Meta
Start using Llama 3.1 8B in minutes. VoltageGPU provides an OpenAI-compatible API — just change the base_url.
from openai import OpenAI
client = OpenAI(
base_url="https://api.voltagegpu.com/v1",
api_key="YOUR_VOLTAGE_API_KEY"
)
response = client.chat.completions.create(
model="meta-llama/Llama-3.1-8B-Instruct",
messages=[
{"role": "system", "content": "Extract entities as JSON."},
{"role": "user", "content": "John Smith from Acme Corp signed a $50,000 contract on March 15, 2026."}
],
max_tokens=512,
temperature=0.0
)
print(response.choices[0].message.content)curl -X POST https://api.voltagegpu.com/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_VOLTAGE_API_KEY" \
-d '{
"model": "meta-llama/Llama-3.1-8B-Instruct",
"messages": [
{"role": "system", "content": "Extract entities as JSON."},
{"role": "user", "content": "John Smith from Acme Corp signed a $50,000 contract on March 15, 2026."}
],
"max_tokens": 512,
"temperature": 0.0
}'| Component | Price | Unit |
|---|---|---|
| Input tokens | $0.1 | per 1M tokens |
| Output tokens | $0.15 | per 1M tokens |
New accounts receive $5 free credit. No credit card required to start.
Llama 3.1 8B delivers strong performance for its size class: MMLU (73.0%), HumanEval (72.6%), and GSM8K (84.5%). It excels at instruction following, text summarization, entity extraction, classification, and simple reasoning. With 128K context support and fast inference speeds, it processes thousands of requests per second at minimal cost.
Llama 3.1 8B is Meta's most efficient small language model, offering impressive capabilities at minimal cost. With 8 billion parameters and a 128K context window, it delivers fast inference with low latency, making it ideal for real-time applications, high-throughput batch processing, and cost-sensitive deployments. Despite its compact size, it performs remarkably well on instruction following, summarization, and simple coding tasks. It was trained on over 15 trillion tokens and fine-tuned with RLHF.
Build responsive chatbots with sub-100ms latency for consumer-facing applications.
Classify documents, sentiment, intent, and topics at high throughput and low cost.
Summarize articles, emails, meeting notes, and documents efficiently at scale.
Extract structured data from unstructured text: names, dates, amounts, entities.
Process millions of records affordably for data enrichment and annotation.
https://api.voltagegpu.com/v1/chat/completions| Authorization | Bearer YOUR_VOLTAGE_API_KEY | Required |
| Content-Type | application/json | Required |
meta-llama/Llama-3.1-8B-InstructUse this value as the model parameter in your API requests.
curl -X POST https://api.voltagegpu.com/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_VOLTAGE_API_KEY" \
-d '{
"model": "meta-llama/Llama-3.1-8B-Instruct",
"messages": [
{"role": "system", "content": "Extract entities as JSON."},
{"role": "user", "content": "John Smith from Acme Corp signed a $50,000 contract on March 15, 2026."}
],
"max_tokens": 512,
"temperature": 0.0
}'Great price-performance for smaller models with 24GB VRAM.
Enterprise-grade GPU for production inference at scale.
Access this model and 140+ others through our OpenAI-compatible API.
Compare GPU cloud pricing and model hosting features.
View GPU compute and AI inference pricing with no hidden fees.
Deploy a GPU pod in under 60 seconds to run models locally.
Use Llama 3.1 8B when you need fast responses, high throughput, or low cost. It excels at classification, summarization, extraction, and simple Q&A. Switch to a larger model (70B+) for complex reasoning, creative writing, or tasks requiring deep domain knowledge.
Llama 3.1 8B delivers extremely fast inference with typical time-to-first-token under 50ms. It can process thousands of requests per second on VoltageGPU's infrastructure, making it ideal for real-time applications.
Yes, Llama 3.1 8B supports a 128K context window, allowing it to process documents up to ~96,000 words. However, for complex analysis of very long documents, a larger model may provide better results.
Llama 3.1 8B costs $0.10 per million input tokens and $0.15 per million output tokens on VoltageGPU. This means processing 1 million words costs approximately $0.13, making it one of the most affordable models available.
Get $5 free credit when you sign up. No credit card required. Deploy in under 30 seconds with our OpenAI-compatible API.