4-bit AWQ-quantized version of Qwen/Qwen3-VL-30B-A3B-Instruct, produced with llmcompressor and intended for efficient inference with vLLM on a single 24 GB GPU (RTX 4090 / 3090 / A5000 / L4 / etc.).

Same architecture and behaviour as the base model, with the language-model weights compressed from bf16 to 4-bit per-channel-grouped integers. The vision tower, MoE router, and final projection (lm_head) are kept at full precision.

Quantization details

Field	Value
Method	AWQ (Activation-aware Weight Quantization)
Scheme	W4A16 — 4-bit weights, 16-bit activations
Group size	128
Format	`compressed-tensors` (vLLM `CompressedTensorsWNA16MarlinMoEMethod`)
MoE calibration	`moe_calibrate_all_experts=True` — every expert receives calibration data, not only routed-to experts
Ignored layers	`lm_head` (full precision), `visual.*` (vision tower full precision), `mlp.gate$` (MoE router full precision)
Tool	llmcompressor (`AWQModifier`, sequential pipeline)
Calibration dataset	`HuggingFaceH4/ultrachat_200k` (`train_sft` split) — text-only
Calibration samples	256
Max sequence length	1024 tokens

Total on-disk size: ~17.8 GB across four safetensors shards.

Serving with vLLM

Recipe validated on an RTX 4090 (24 GB) running vLLM 0.21:

bash

vllm serve dark-side-of-the-code/Qwen3-VL-30B-A3B-Instruct-AWQ \
  --max-model-len 49152 \
  --max-num-seqs 8 \
  --gpu-memory-utilization 0.95 \
  --kv-cache-dtype fp8 \
  --trust-remote-code \
  --limit-mm-per-prompt '{"image": 8, "video": 0}' \
  --enable-auto-tool-choice \
  --tool-call-parser hermes \
  --host 0.0.0.0 \
  --port 8001

Notes:

--kv-cache-dtype fp8 lifts the context ceiling on a 24 GB card from ~32K to 48K with no observable quality regression on text / structured-output / vision-OCR / tool-calling / 14K-token tasks (single-stream decode is actually marginally faster). Drop it if you'd rather keep KV cache in fp16.
--tool-call-parser hermes is the correct parser for Qwen3-VL's tool-call format.
The served model id is the repo id you passed to vllm serve (dark-side-of-the-code/Qwen3-VL-30B-A3B-Instruct-AWQ) — use that as the model field in API requests. Add --served-model-name <short-label> if you'd rather expose a shorter id.
The vision tower runs at full precision regardless of the weight quant — image (and, if enabled, video) understanding is unaffected by 4-bit compression.

Python (OpenAI client)

python

from openai import OpenAI

client = OpenAI(base_url="http://localhost:8001/v1", api_key="EMPTY")

resp = client.chat.completions.create(
    model="dark-side-of-the-code/Qwen3-VL-30B-A3B-Instruct-AWQ",
    messages=[{"role": "user", "content": "Briefly: what is photosynthesis?"}],
    max_tokens=120,
)
print(resp.choices[0].message.content)

Multi-image example

python

import base64
from pathlib import Path
from openai import OpenAI

client = OpenAI(base_url="http://localhost:8001/v1", api_key="EMPTY")

def as_data_url(path: str) -> str:
    data = Path(path).read_bytes()
    return f"data:image/jpeg;base64,{base64.b64encode(data).decode()}"

resp = client.chat.completions.create(
    model="dark-side-of-the-code/Qwen3-VL-30B-A3B-Instruct-AWQ",
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "Describe each frame and any text visible."},
            {"type": "image_url", "image_url": {"url": as_data_url("frame_0.jpg")}},
            {"type": "image_url", "image_url": {"url": as_data_url("frame_1.jpg")}},
        ],
    }],
    max_tokens=300,
)
print(resp.choices[0].message.content)

Throughput

On a single RTX 4090 with the recipe above:

Metric	Value
Decode (single-stream)	~225 tok/s
TTFT (small prompt)	~0.1 s

Validation

Five end-to-end checks against an OpenAI-compatible vLLM endpoint serving this checkpoint (fp8 KV cache, 48K context):

Check	Result
text coherence	pass — coherent answer to a knowledge question
structured JSON	pass — valid JSON with all expected keys
vision + OCR	pass — reads on-image text and names a drawn shape
tool calling	pass — emits a correct function call
14K-token context	pass — coherent reply with full prompt context loaded

Limitations and accuracy

Quantization introduces a small accuracy degradation compared to the bf16 base model. The checks above confirm task-level competence on common multimodal workloads (vision, structured output, tool calling, long context) but do not constitute a formal benchmark suite (MMLU, MMMU, etc.).
The vision tower and MoE router are kept at full precision — image / video quality and routing behaviour should be unchanged.
The optional --kv-cache-dtype fp8 serve flag carries a small theoretical accuracy risk on very long contexts; the 14K-token bench check did not show degradation, but be cautious for >32K context workloads.
Inherits all limitations and intended-use restrictions from the base model.

License

Inherits from the base model — see Qwen/Qwen3-VL-30B-A3B-Instruct for the authoritative terms (Apache 2.0 as of publication). Quantized weights are a derivative work; verify the base model's licence applies to your intended use before commercial deployment.