ReadyGPU TEE

Meta: Llama 3.3 70B Instruct

Model IDmeta-llama/llama-3.3-70b-instruct

The Meta Llama 3.3 multilingual large language model (LLM) is a pretrained and instruction tuned generative model in 70B (text in/text out). The Llama 3.3 instruction tuned text only model is optimized for multilingual dialogue use cases and outperforms many of the available open source and closed chat models on common industry benchmarks.

Start building Docs

input

$2.00/M

output

$2.00/M

context

131K

created

Nov 28, 2025

Supported API shape

input

text

output

text

tools

Supported

json mode

Supported

Verification

receipt

x-receipt-id

attestation

gateway report

session

attested upstream

provider

Phala

Provider

Phala

GPU TEE

input

$2.00/M

output

$2.00/M

context

131K

Performance comparison

Last 72h · UTC

$ pip install openai

1from openai import OpenAI
2import os
3
4MODEL = "meta-llama/llama-3.3-70b-instruct"
5
6client = OpenAI(
7    base_url="https://inference.phala.com/v1",
8    api_key=os.environ["PHALA_API_KEY"],
9)
10
11response = client.chat.completions.create(
12    model=MODEL,
13    messages=[{"role": "user", "content": "Run this privately."}],
14    extra_body={"provider": {"aci_verified": True}},
15)
16
17print(response.id)
18print(response.choices[0].message.content)

$ npm install openai

1import OpenAI from "openai"
2
3const MODEL = "meta-llama/llama-3.3-70b-instruct"
4
5const openai = new OpenAI({
6  baseURL: "https://inference.phala.com/v1",
7  apiKey: process.env.PHALA_API_KEY,
8})
9
10const response = await openai.chat.completions.create({
11  model: MODEL,
12  messages: [{ role: "user", content: "Run this privately." }],
13  provider: { aci_verified: true },
14})
15
16console.log(response.id)
17console.log(response.choices[0].message.content)

1curl https://inference.phala.com/v1/chat/completions \
2  -H "Authorization: Bearer $PHALA_API_KEY" \
3  -H "Content-Type: application/json" \
4  -d '{
5    "model": "meta-llama/llama-3.3-70b-instruct",
6    "messages": [
7      { "role": "user", "content": "Run this privately." }
8    ],
9    "provider": {"aci_verified": true}
10  }'

More models

Other private inference routes.

View catalog

encrypted

Meta: Muse Glimmer 30B

Muse Glimmer 30B is a dense, open-weight multimodal model from Meta Superintelligence Labs, distilled from Muse Spark and optimized for autonomous agents on consumer hardware. It is suited for long-horizon agentic and coding workflows, with multi-step reasoning, reliable tool use, failure recovery, image understanding, and multilingual support across more than 100 languages.

context

131K

input

$0.35/M

encrypted

DeepSeek: DeepSeek V4 Flash 0731

DeepSeek V4 Flash 0731 is a sparse mixture-of-experts model from DeepSeek, with 13B active parameters out of 284B total. This re-post-trained revision is suited for coding, reasoning, and agent workflows.

context

1.0M

input

$0.20/M

encrypted

Z.ai: GLM 5.2

GLM-5.2 is Z.ai's flagship model for the era of long-horizon tasks. With a truly usable 1M-token context window, it can handle project-level engineering context and execute long-running tasks more reliably. Served as a text-only TEE deployment via Phala.

context

1.0M

input

$1.13/M

encrypted

Google: Gemma 4 31B

Gemma 4 31B Instruct is Google DeepMind's 30.7B dense model. Features a 256K token context window, configurable thinking/reasoning mode, native function calling, and strong multilingual performance. Served as a text-only TEE deployment via NEAR AI.

context

262K

input

$0.15/M