Qwen2.5 0.5B Instruct

fp8_dynamic quantized and serving-tuned for A100-40GB on vllm v0.18.0, with the benchmark receipt included. You get a self-contained kit: the optimized weights, the exact serving configuration, and the measured proof. Run it on any cloud or your own hardware. Nothing calls home, nothing expires.

$7.50

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Speedup: 3.7xfaster p50
Latency p50: 88ms
Latency p99: 92ms
Peak VRAM: 34.3GB
GPU target: A100-40GB
License: Apache-2.0

Latency p50-73%

Latency p99-72%

Peak VRAM-3%

Measured against the unoptimized baseline on A100-40GB: median latency 327 ms down to 88 ms. The full benchmark methodology and receipt ship inside the kit.

This is what it means to own your AI

You control the intelligence

The weights run on your infrastructure. No deprecation notices, no silent model swaps, no provider deciding what your product is allowed to do.

You keep the economics

One purchase instead of a meter that runs forever. A closed API charges you for every token, at whatever the price becomes; your own deployment costs what your GPU costs.

You keep the data

Prompts and outputs never leave your network. Nothing calls home, nothing is retained by someone else, and compliance stops being a negotiation.

Technical implementation

Optimizations applied

FP8 dynamic quantization
Weights and activations served in FP8 on Ampere, cutting memory traffic without a calibration pass.
Tuned serving configuration
Engine flags tuned for this model and GPU pair rather than framework defaults.

Serving stack

vllm v0.18.0, fp8_dynamic, tuned for A100-40GB (sm_80). Requires 40 GB VRAM minimum.

License

Apache-2.0, as published by the model author on Hugging Face. The full license text ships inside the kit.

Runs on any GPU provider

The kit is self-contained: bring it to any cloud with A100-40GB-class GPUs, or your own hardware.

DockerComposeKubernetesRunpodModalYour own hardware