ReadyGPU TEE

Sentence Transformers: all-MiniLM-L6-v2

Model IDsentence-transformers/all-minilm-l6-v2

The all-MiniLM-L6-v2 embedding model maps sentences and short paragraphs into a 384-dimensional dense vector space, enabling high-quality semantic representations that are ideal for downstream tasks such as information retrieval, clustering, similarity scoring, and text ranking.

Start building Docs

input

$0.005/M

output

Free/M

context

512

added

Nov 25, 2025

Supported API shape

input

text · embeddings

output

embeddings

tools

Not listed

json mode

Not listed

Verification

Available evidence. Use a verified client to authenticate the quote, connection, and receipt.

Verify private inference

receipt

signed receipt + hashes

attestation

gateway report

session

attested upstream

serving provider

read from receipt session

Provider

Phala

GPU TEE

input

$0.005/M

output

Free/M

context

512

Performance comparison

Last 72h · UTC

$ pip install openai

1from openai import OpenAI
2import os
3
4MODEL = "sentence-transformers/all-minilm-l6-v2"
5
6client = OpenAI(
7    base_url="https://inference.phala.com/v1",
8    api_key=os.environ["PHALA_AI_API_KEY"],
9)
10
11response = client.embeddings.create(
12    model=MODEL,
13    input="Phala keeps inference data inside a confidential runtime.",
14)
15
16print(response.data[0].embedding[:8])

$ npm install openai

1import OpenAI from "openai"
2
3const MODEL = "sentence-transformers/all-minilm-l6-v2"
4
5const openai = new OpenAI({
6  baseURL: "https://inference.phala.com/v1",
7  apiKey: process.env.PHALA_AI_API_KEY,
8})
9
10const response = await openai.embeddings.create({
11  model: MODEL,
12  input: "Phala keeps inference data inside a confidential runtime.",
13})
14
15console.log(response.data[0].embedding.slice(0, 8))

1curl https://inference.phala.com/v1/embeddings \
2  -H "Authorization: Bearer $PHALA_AI_API_KEY" \
3  -H "Content-Type: application/json" \
4  -d '{
5    "model": "sentence-transformers/all-minilm-l6-v2",
6    "input": "Phala keeps inference data inside a confidential runtime."
7  }'

More models

Other private inference routes.

View catalog

encrypted

MoonshotAI: Kimi K3.1 Pro

Kimi K3.1 Pro is Moonshot AI's flagship open-weight multimodal reasoning model and a post-trained successor to Kimi K3, with a 1M-token context window. It is built for long-horizon coding, 3D creation, financial research, and end-to-end agentic knowledge work, with native image and video understanding.

context

1.0M

input

$2.00/M

$0.20/M cache read

encrypted

Jared Palmer: Kev 4B

Kev 4B is an open-weight System One model from Jared Palmer, fine-tuned from Qwen3.5 4B Base. It makes fast, structured decisions for software: given a state and typed questions, it returns calibrated answers instead of text, as a yes/no probability (noul), a pick among options (choice), or a level on a scale (score). Call it through POST /v1/systemone, compatible with the TypeSafe SDK; it does not serve chat. Served on Phala in a TDX-attested enclave with an 8,192-token context.

context

input

$0.042/M

encrypted

Phala: Qwen3.8 27B Uncensored (Aggressive)

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.

context

262K

input

$0.30/M

$0.05/M cache read

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

$0.26/M cache read