InferX Catalog | Qwen3.5-9B-AWQ

Qwen3.5-9B-AWQ

a 9B Qwen3.5 model quantized with AWQ for efficient, low-memory inference.

QuantTrio text text2text reasoning coding chat

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QuantTrio/Qwen3.5-9B-AWQ is a 9B Qwen3.5 model optimized using AWQ (Activation-aware Weight Quantization), reducing memory usage and improving throughput while maintaining strong reasoning, coding, and chat performance, making it ideal for cost-efficient deployments and high-concurrency inference. These weights are compatible with common inference frameworks such as Transformers, vLLM, and SGLang.

Metadata

Provider

QuantTrio

Modality

text

API type

text2text

Source

huggingface / QuantTrio/Qwen3.5-9B-AWQ

Created

2026-04-03 19:28:55 UTC

Updated

2026-04-20 11:44:33 UTC

Catalog version

3

Visibility

Published

Specifications

Parameters

9.00B

MoE

No

Max model length

65536

Image

inferx/vllm-openai:v0.19.1

Default Deploy Config

GPU count

1

vRAM

50000 MB

Summary

1xGPU 50000 MB

Recommended Use Cases

—

Model Spec

{
    "image": "inferx/vllm-openai:v0.19.1",
    "commands": [
        "--model",
        "QuantTrio/Qwen3.5-9B-AWQ",
        "--reasoning-parser",
        "qwen3",
        "--gpu-memory-utilization",
        "0.85",
        "--max-model-len",
        "65536",
        "--enable-auto-tool-choice",
        "--tool-call-parser",
        "qwen3_coder",
        "--enable-prefix-caching",
        "--tensor-parallel-size",
        "1"
    ],
    "resources": {
        "GPU": {
            "Count": 1,
            "vRam": 50000
        }
    },
    "envs": [],
    "policy": {
        "Obj": {
            "min_replica": 0,
            "max_replica": 1,
            "standby_per_node": 1,
            "parallel": 50,
            "queue_len": 100,
            "queue_timeout": 30.0,
            "scalein_timeout": 1.0,
            "scaleout_policy": {
                "WaitQueueRatio": {
                    "wait_ratio": 0.1
                }
            },
            "runtime_config": {
                "graph_sync": false
            }
        }
    },
    "sample_query": {
        "body": {
            "stream": "true",
            "max_tokens": "1000",
            "temperature": "0"
        },
        "path": "v1/completions",
        "prompt": "write a quick sort algorithm.",
        "apiType": "text2text",
        "dataUrl": "",
        "prompts": [],
        "loadingTimeout": 90
    }
}