Model reference · open weights

G9v3

LLMs ai9stars Text gen 1 build Open weights 2k dl/mo

G9v3 is an open-weight language model from ai9stars. G9v3-3B (BF16) weighs 6.0 GB; the smallest configuration that runs it is RTX 3060 12 GB.

What it is

Released byai9stars
TypeLanguage models
TaskText gen
Parameters (lead)3.0B
Context131,072 tokens
Runs withtransformers
Released2026-07-21
Popularity2k downloads / month
Weights6.0 GB (G9v3-3B (BF16), file size)
LicenceOpen weights

What it runs on

Memory and cards for G9v3-3B (BF16)

Weights 6.0 GB (file size) · KV cache 53 MB per 1,000 tokens of context, at 16 bits (vLLM's default for this build; an 8-bit cache halves it) · runtime overhead from 647 MB on a small card · context up to 131,072 tokens.

CardRequests at once
8K tokens each
Requests at once
32K tokens each
Longest single
request
Counted
memory
RTX 3060 12 GB11291K11.6 GB
RTX 4060 Ti 16 GB205all 128K15.4 GB
RTX 3090 24 GB389all 128K23.4 GB
RTX 4090 24 GB389all 128K23.4 GB
RTX 5090 32 GB5513all 128K31.0 GB
L40S 48 GB8521all 128K44.0 GB
A100 80 GB16441all 128K78.2 GB
H100 80 GB15538all 128K78.1 GB
RTX PRO 6000 Blackwell 96 GB19147all 128K93.8 GB
DGX Spark (GB10) 128 GB unified22255all 128K107 GB
H200 141 GB29273all 128K138 GB
B200 180 GB37994all 128K176 GB
Memory needed at each load
Requests at once8K tokens each32K tokens each
17.1 GB8.4 GB
58.8 GB15.3 GB
810.1 GB20.6 GB
1613.6 GB34.5 GB
3220.6 GB62.5 GB
6434.5 GB118 GB

On one card, with vLLM's small-card settings (2,048 tokens a step). Cards of 70 GB and more reserve more per request and more overhead — each row above uses its own card's settings.

Estimates, not measurements, checked against published vLLM startup logs. The weights are the build's file size; the cache is calculated from its config (grouped-query attention); the overhead is an estimate of vLLM's own memory with that card's default settings. "Requests at once" is how many requests of that length vLLM admits — its reservation at full length, with --max-model-len set to that length; requests that stay shorter fit more. "Longest single request" is the most one request can hold there: below the model's maximum, vLLM starts only with --max-model-len set at or under it. "Counted memory" is vLLM's default 92 % of what CUDA reports for the card (the DGX Spark: about 100 GiB of its shared 128 GB). Assumes vLLM 0.10 or later.

From the model card

What ai9stars says about G9v3

Introduction

G9v3-3B is a dense 3B causal language model from the AI9Stars team, built for local deployment and resource-constrained scenarios. It targets everyday assistant use, coding, tool-use workflows, and reasoning tasks where a compact model is preferred.

Read the full model card

Model Information

  • Type: Causal Language Model
  • Architecture: Standard LlamaForCausalLM
  • Number of Parameters: ~3B
  • Context Length: 131,072

Quickstart

vLLM

pip install "vllm>=0.21"
vllm serve ai9stars/G9v3-3B --port 8000
curl http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "ai9stars/G9v3-3B",
    "messages": [{"role": "user", "content": "Who are you?"}],
    "max_tokens": 128,
    "temperature": 0.7
  }'

SGLang

pip install "sglang[srt]>=0.5.12"
python -m sglang.launch_server --model-path ai9stars/G9v3-3B --port 30000
curl http://localhost:30000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "ai9stars/G9v3-3B",
    "messages": [{"role": "user", "content": "Who are you?"}],
    "max_tokens": 128,
    "temperature": 0.7
  }'

Transformers

pip install -U "transformers>=5.6" accelerate torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "ai9stars/G9v3-3B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

messages = [{"role": "user", "content": "Who are you?"}]
inputs = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    enable_thinking=False,
    return_dict=True,
    return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=128)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))

Recommended sampling parameters:

ModeRecommended paramsEnable
Thinktemperature=0.9, top_p=0.95enable_thinking=True
No Thinktemperature=0.7, top_p=0.95enable_thinking=False

Limitations and Responsible Use

G9v3-3B is a language model that generates content based on learned statistical patterns from training data. It may produce inaccurate, biased, or unsafe outputs, and generated content should be reviewed and verified before use in high-stakes settings. Users are responsible for evaluating outputs, applying appropriate safeguards, and complying with applicable laws, regulations, and platform policies.

License

This repository and the G9v3 model weights are released under the Apache-2.0 License.

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

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