Model reference · open weights

openchat-3.5-0106

LLMs openchat Text gen 1 build Open weights 10k dl/mo

openchat-3.5-0106 is an open-weight language model from openchat. openchat-3.5-0106 (BF16) weighs 14.5 GB; the smallest configuration that runs it is 2× RTX 3060 12 GB.

For self-hosting, compare this build's requirements below with the GPU you plan to rent.

  • OpenChat-3.5-0106 is a 7.2B parameter text-generation model developed by openchat.
  • It supports a context length of 8192 tokens and is designed for general tasks, coding, and mathematical reasoning.
  • The model is distributed under the Apache-2.0 license.

Summary of the openchat/openchat-3.5-0106 model card, 2026-10-01

What it is

Released byopenchat
Released2024-01-07
Parameters7.2B
VRAM14.5 GB for the weights

What it runs on

Memory and cards for openchat-3.5-0106 (BF16)

14.5 GBweights, file size
0 MBcache per 1K tokens
1.1 GBwindow cache per request
647 MBruntime overhead, at least
8,192 tokenscontext max
CardRequests at onceContext maxMemory
8K each
RTX 3060 12 GB11.6 GB
RTX 4060 Ti 16 GB15.4 GB
RTX 3090 24 GB7all 8K23.4 GB
RTX 4090 24 GB7all 8K23.4 GB
RTX 5090 32 GB14all 8K31.0 GB
L40S 48 GB26all 8K44.0 GB
A100 80 GB58all 8K78.2 GB
H100 80 GB55all 8K78.1 GB
RTX PRO 6000 Blackwell 96 GB70all 8K93.8 GB
DGX Spark (GB10) 128 GB unified83all 8K107 GB
H200 141 GB111all 8K138 GB
B200 180 GB146all 8K176 GB
2× RTX 3060 12 GB
tensor parallel
6all 8K11.6 GB a card
2× RTX 4060 Ti 16 GB
tensor parallel
14all 8K15.4 GB a card
2× RTX 4090 24 GB
tensor parallel
28all 8K23.4 GB a card
2× RTX 3090 24 GB
tensor parallel
28all 8K23.4 GB a card
Memory needed at each load
Requests at once8K tokens each
116.2 GB
520.5 GB
823.7 GB
1632.3 GB
3249.5 GB
6483.8 GB

One card, with vLLM's small-card settings.

From the model card

What openchat says about openchat-3.5-0106

Read the model card

font-family: 'Helvetica'; color: black; font-weight: bold;">OpenChat-3.5🚀

  1. Usage
  2. Benchmarks
  3. Limitations
  4. License
  5. Citation
  6. Acknowledgements

To use this model, we highly recommend installing the OpenChat package by following the installation guide in our repository and using the OpenChat OpenAI-compatible API server by running the serving command from the table below. The server is optimized for high-throughput deployment using vLLM and can run on a consumer GPU with 24GB RAM. To enable tensor parallelism, append --tensor-parallel-size N to the serving command.

Once started, the server listens at localhost:18888 for requests and is compatible with the OpenAI ChatCompletion API specifications. Please refer to the example request below for reference. Additionally, you can use the OpenChat Web UI for a user-friendly experience.

If you want to deploy the server as an online service, you can use --api-keys sk-KEY1 sk-KEY2 ... to specify allowed API keys and --disable-log-requests --disable-log-stats --log-file openchat.log for logging only to a file. For security purposes, we recommend using an HTTPS gateway in front of the server.

ModelSizeContextWeightsServing
OpenChat-3.5-01067B8192Huggingfacepython -m ochat.serving.openai_api_server --model openchat/openchat-3.5-0106 --engine-use-ray --worker-use-ray

💡 Default Mode (GPT4 Correct): Best for coding, chat and general tasks

curl http://localhost:18888/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "openchat_3.5",
    "messages": [{"role": "user", "content": "You are a large language model named OpenChat. Write a poem to describe yourself"}]
  }'

🧮 Mathematical Reasoning Mode: Tailored for solving math problems

curl http://localhost:18888/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "openchat_3.5",
    "condition": "Math Correct",
    "messages": [{"role": "user", "content": "10.3 − 7988.8133 = "}]
  }'

Conversation templates

💡 Default Mode (GPT4 Correct): Best for coding, chat and general tasks

GPT4 Correct User: HelloGPT4 Correct Assistant: HiGPT4 Correct User: How are you today?GPT4 Correct Assistant:

🧮 Mathematical Reasoning Mode: Tailored for solving math problems

Math Correct User: 10.3 − 7988.8133=Math Correct Assistant:

⚠️ Notice: Remember to set `` as end of generation token.

The default (GPT4 Correct) template is also available as the integrated tokenizer.chat_template, which can be used instead of manually specifying the template:

messages = [
    {"role": "user", "content": "Hello"},
    {"role": "assistant", "content": "Hi"},
    {"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)
assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747, 15359, 32000, 420, 6316, 28781, 3198, 3123, 1247, 28747, 1602, 460, 368, 3154, 28804, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747]

We've included evaluator capabilities in this release to advance open-source models as evaluators. You can use Default Mode (GPT4 Correct) with the following prompt (same as Prometheus) to evaluate a response.

###Task Description:
An instruction (might include an Input inside it), a response to evaluate, a reference answer that gets a score of 5, and a score rubric representing a evaluation criteria are given.
1. Write a detailed feedback that assess the quality of the response strictly based on the given score rubric, not evaluating in general.
2. After writing a feedback, write a score that is an integer between 1 and 5. You should refer to the score rubric.
3. The output format should look as follows: "Feedback: (write a feedback for criteria) [RESULT] (an integer number between 1 and 5)"
4. Please do not generate any other opening, closing, and explanations.

###The instruction to evaluate:
{orig_instruction}

###Response to evaluate:
{orig_response}

###Reference Answer (Score 5):
{orig_reference_answer}

###Score Rubrics:
[{orig_criteria}]
Score 1: {orig_score1_description}
Score 2: {orig_score2_description}
Score 3: {orig_score3_description}
Score 4: {orig_score4_description}
Score 5: {orig_score5_description}

###Feedback:
Model# ParamsAverageMT-BenchHumanEvalBBH MCAGIEvalTruthfulQAMMLUGSM8KBBH CoT
OpenChat-3.5-01067B64.57.871.351.549.161.065.877.462.2
OpenChat-3.5-12107B63.87.7668.949.548.061.865.377.361.8
OpenChat-3.57B61.67.8155.547.647.459.164.377.363.5
ChatGPT (March)*

Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.

© 2026 AxForge · EU-hosted AI infrastructure Pricing Docs Trust Privacy Terms