Model reference · open weights
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 by | ai9stars |
|---|---|
| Type | Language models |
| Task | Text gen |
| Parameters (lead) | 3.0B |
| Context | 131,072 tokens |
| Runs with | transformers |
| Released | 2026-07-21 |
| Popularity | 2k downloads / month |
| Weights | 6.0 GB (G9v3-3B (BF16), file size) |
| Licence | Open weights |
What it runs on
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.
| Card | Requests at once 8K tokens each | Requests at once 32K tokens each | Longest single request | Counted memory |
|---|---|---|---|---|
| RTX 3060 12 GB | 11 | 2 | 91K | 11.6 GB |
| RTX 4060 Ti 16 GB | 20 | 5 | all 128K | 15.4 GB |
| RTX 3090 24 GB | 38 | 9 | all 128K | 23.4 GB |
| RTX 4090 24 GB | 38 | 9 | all 128K | 23.4 GB |
| RTX 5090 32 GB | 55 | 13 | all 128K | 31.0 GB |
| L40S 48 GB | 85 | 21 | all 128K | 44.0 GB |
| A100 80 GB | 164 | 41 | all 128K | 78.2 GB |
| H100 80 GB | 155 | 38 | all 128K | 78.1 GB |
| RTX PRO 6000 Blackwell 96 GB | 191 | 47 | all 128K | 93.8 GB |
| DGX Spark (GB10) 128 GB unified | 222 | 55 | all 128K | 107 GB |
| H200 141 GB | 292 | 73 | all 128K | 138 GB |
| B200 180 GB | 379 | 94 | all 128K | 176 GB |
| Requests at once | 8K tokens each | 32K tokens each |
|---|---|---|
| 1 | 7.1 GB | 8.4 GB |
| 5 | 8.8 GB | 15.3 GB |
| 8 | 10.1 GB | 20.6 GB |
| 16 | 13.6 GB | 34.5 GB |
| 32 | 20.6 GB | 62.5 GB |
| 64 | 34.5 GB | 118 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
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.
LlamaForCausalLMpip 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
}'
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
}'
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:
| Mode | Recommended params | Enable |
|---|---|---|
| Think | temperature=0.9, top_p=0.95 | enable_thinking=True |
| No Think | temperature=0.7, top_p=0.95 | enable_thinking=False |
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.
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.