README.md

---
license: other
license_name: embedl-models-community-licence-v1.0
license_link: https://github.com/embedl/embedl-models/blob/main/LICENSE
base_model: amazon/chronos-2
quantized_from: amazon/chronos-2
tags:
- time-series
- time-series-forecasting
- chronos
- chronos-2
- int8
- tensorrt
- quantization
- edge
- jetson
- orin
library_name: onnx
pipeline_tag: time-series-forecasting
gated: true
extra_gated_heading: Acknowledge Embedl Models Community Licence v1.0
extra_gated_description: |
  By requesting access you agree to the Embedl Models Community
  Licence v1.0 (no redistribution as a hosted service) and to the
  upstream chronos-2 license terms.
extra_gated_button_content: Request access
---

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# Embedl Chronos-2 (Quantized for TensorRT)

Deployable INT8-quantized version of
[`amazon/chronos-2`](https://huggingface.co/amazon/chronos-2),
optimized with
[embedl-deploy](https://github.com/embedl/embedl-deploy) for
low-latency NVIDIA TensorRT inference on edge GPUs. Two
static-context variants ship: **ctx=512** for short-history
forecasting and **ctx=2048** for long-history use cases.

## Upstream Model

<a href="https://hfviewer.com/amazon/chronos-2?utm_source=huggingface&amp;utm_medium=embedded_model_card&amp;utm_campaign=amazon__chronos-2_card" target="_blank" rel="noopener">
  <img
    src="https://hfviewer.com/api/card.svg?source=amazon%2Fchronos-2&amp;v=20260501clipcard"
    alt="Open amazon/chronos-2 in hfviewer"
    width="100%"
  />
</a>

## Highlights

- **Per-tensor INT8** activations + **per-channel INT8** weights via
  embedl-deploy's PTQ flow on top of TensorRT's fused MHA kernel.
  No QAT or distillation needed.
- **Drop-in replacement** for `amazon/chronos-2` inference: same
  `(context, group_ids) → quantile_preds` signature; 21 evenly
  spaced quantile levels with the median at index 10.
- **Validated** on the [GIFT-Eval](https://huggingface.co/datasets/Salesforce/GiftEval)
  benchmark across 125 task configurations. See Accuracy below.
- **Two ctx variants** so you can pick the latency/history-window
  trade-off that fits your deployment.

## Quick Start

```bash
pip install tensorrt pycuda numpy
python infer_trt.py --ctx 512    # 1.2× faster than FP16 on Orin
python infer_trt.py --ctx 2048   # 1.3× faster than FP16 on Orin
```

The `infer_trt.py` helper script builds a TensorRT engine from the
ONNX on first run (cached as `*.engine` next to the artifact) and
feeds a synthetic seasonal context for demonstration. Replace the
context generator with your own series of the right length.

## Files

| File | Purpose |
|---|---|
| `embedl_chronos_2_ctx512_int8.onnx` | INT8 ONNX with Q/DQ — ctx=512, 1024-step horizon. |
| `embedl_chronos_2_ctx2048_int8.onnx` | INT8 ONNX with Q/DQ — ctx=2048, 1024-step horizon. |
| `infer_trt.py` | ONNX Runtime / TensorRT inference example. |

Both artifacts emit a `(1, 21, 1024)` quantile tensor (21 quantile
levels × 64 output patches × 16 steps-per-patch = 1024 horizon
steps). Slice the median (`preds[0, 10]`) for a point forecast and
clip to your needed prediction length.

## Performance

Latency measured with TensorRT + `trtexec`, GPU compute time only
(`--noDataTransfers`), CUDA Graph + Spin Wait enabled, clocks locked
(`nvpmodel -m 0 && jetson_clocks` on Jetson).

### Jetson AGX Orin (MAXN)

#### ctx=512

<p align="center">
  <img src="https://huggingface.co/datasets/embedl/documentation-images/resolve/main/chronos-2-quantized-trt/chronos-2-quantized-trt__orin-mountain-view__latency_ctx512.svg" alt="Chronos-2 INT8 latency, ctx=512" width="640">
</p>

| Build | Mean latency (ms) |
|---|---|
| TensorRT FP16 | **2.977** |
| TensorRT `--best` | 2.974 |
| **embedl INT8** | **2.432** |
| Speedup (FP16 → embedl INT8) | **1.22×** |

#### ctx=2048

<p align="center">
  <img src="https://huggingface.co/datasets/embedl/documentation-images/resolve/main/chronos-2-quantized-trt/chronos-2-quantized-trt__orin-mountain-view__latency_ctx2048.svg" alt="Chronos-2 INT8 latency, ctx=2048" width="640">
</p>

| Build | Mean latency (ms) |
|---|---|
| TensorRT FP16 | **4.482** |
| TensorRT `--best` | 4.482 |
| **embedl INT8** | **3.482** |
| Speedup (FP16 → embedl INT8) | **1.29×** |

## Accuracy

Evaluated on the
[GIFT-Eval](https://huggingface.co/datasets/Salesforce/GiftEval)
benchmark — 125 task configurations spanning 50 datasets ×
{short, medium, long} horizons. Aggregate WQL (weighted quantile
loss, lower is better) reported using the
[TIME-paper normalization](https://arxiv.org/html/2602.12147v2):
geomean of per-task ratio against the Seasonal-Naive baseline.

| Metric | FP32 baseline | **embedl INT8 ctx=512** | **embedl INT8 ctx=2048** |
|---|---|---|---|
| Geomean WQL / Seasonal-Naive | 0.549 | **0.634** | **0.618** |
| Geomean WQL / FP32 | 1.000 | **1.156×** | **1.126×** |
| Median WQL / FP32 | 1.000 | 1.074× | 1.045× |
| Cells within 10 % of FP32 | — | 71 / 125 (57 %) | 79 / 125 (63 %) |
| Cells within 20 % of FP32 | — | 96 / 125 (77 %) | 98 / 125 (78 %) |
| Cells beating FP32 | — | 14 / 125 | 19 / 125 |

**How to read the headline number.** Geomean WQL/S-Naive 0.634
(ctx=512) and 0.618 (ctx=2048) means the INT8 model retains the
bulk of `chronos-2`'s skill margin over the no-model Seasonal-Naive
baseline. The FP32 model sits at 0.549 by the same convention; the
INT8 versions are 15-16 % closer to S-Naive but still convincingly
beat it on the geomean.

**Where the regression concentrates.** Worst-case cells are
out-of-distribution low-frequency series (`us_births/M`,
`m4_hourly/{medium,long}`) and high-frequency long-horizon
forecasts (`solar/10T/{medium,long}`). The full per-task CSVs
ship with the artifacts; check them before deploying to a domain
that resembles those outliers.

## Creating Your Own Optimized Models

This artifact was produced with
[embedl-deploy](https://github.com/embedl/embedl-deploy), Embedl's
open-source PyTorch → TensorRT deployment library. The same workflow
applies to your own models — see
[the documentation](https://github.com/embedl/embedl-deploy#readme)
for installation and usage.

## License

| Component | License |
|---|---|
| Optimized model artifacts (this repo) | [Embedl Models Community Licence v1.0](https://github.com/embedl/embedl-models/blob/main/LICENSE) — no redistribution as a hosted service |
| Upstream architecture and weights | [Amazon Chronos-2 License](https://huggingface.co/amazon/chronos-2/blob/main/LICENSE) |

## Contact

We offer engineering support for on-prem/edge deployments and partner
co-marketing opportunities. Reach out at
[contact@embedl.com](mailto:contact@embedl.com), or open an issue on
[GitHub](https://github.com/embedl/embedl-deploy).

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