ReadyGPU TEE

Phala: Qwen3.8 27B Uncensored (Aggressive)

Model IDphala/qwen3.8-27b-uncensored

HauhauCS Qwen3.8-27B Uncensored Aggressive, using the pinned Q8_K_P GGUF and BF16 vision projector. Served with SGLang on a Phala TDX-attested H200, with text and image input and a 262144-token context.

Start building Docs

input

$0.30/M

output

$1.50/M

context

262K

created

Sep 16, 2026

Supported API shape

input

text · image

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

$0.30/M

output

$1.50/M

context

262K

Performance comparison

Last 72h · UTC

$ pip install openai

1from openai import OpenAI
2import os
3
4MODEL = "phala/qwen3.8-27b-uncensored"
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 = "phala/qwen3.8-27b-uncensored"
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": "phala/qwen3.8-27b-uncensored",
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

NVIDIA: Nemotron 3.5 Lightning

NVIDIA Nemotron 3.5 Lightning is an open mixture-of-experts model from NVIDIA, with 3B active parameters out of 30B total. It is suited for high-throughput agentic workloads and specialized tasks that...

context

262K

input

$0.08/M

encrypted

Z.ai: GLM 5.3

GLM-5.3 is a large-scale reasoning model from Z.ai, built for complex software engineering and long-horizon agent tasks. It supports text input and output with a 1M-token context window. Served as a text-only TEE deployment via Phala.

context

1.0M

input

$1.40/M

encrypted

Qwen: Qwen3.8 27B

Qwen3.8 27B is an open-weight dense vision-language model from Qwen. It is suited for coding, professional workflows, research, multimodal interaction, and long-running agent tasks, with flexible thinking that can be enabled or disabled. Served on Phala in a TDX-attested enclave.

context

262K

input

$0.24/M

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

$0.30/M