MODEL PROFILE

Ministral-3-3B-Instruct-2512

litert-community · Ministral-3-3B-Instruct-2512

LiteRT is Google's on-device runtime, the new name for TensorFlow Lite (Android: `com.google.ai.edge.litert:litert`), and `litert-torch`, the renamed `ai-edge-torch`, is its PyTorch converter: a PyTorch model converted unmodified with `litert_torch.convert` matched the original to 4e-7 on a Galaxy S26 (measured, LiteRT 2.2.0, Android 16, 2026-09-05). Measured on device (edge-compat): Galaxy S26 · LiteRT-LM prebuilt-adac974b · GPU · decode 22.1 tok/s · prefill 338 tok/s · TTFT 3.07 s · all 1210 ops delegated (2026-10-05); Pixel 8a · LiteRT-LM 0.16.0 · GPU · decode 6.8 tok/s · prefill 8 tok/s · TTFT 1.68 s · all 1187 ops delegated (2026-08-17); Mac Studio M4 Max · LiteRT-LM 0.17.1 · GPU · decode 129.5 tok/s · prefill 1340 tok/s · TTFT 201 ms · all 1210 ops delegated (2026-10-05). Record: https://github.com/john-rocky/edge-compat/blob/main/cards/ministral3-3b/CARD.md mistralai/Ministral-3-3B-Instruct-2512 converted to the **LiteRT-LM** (`.litertlm`) format for on-device inference with Google's LiteRT-LM runtime (the engine behind the official `litert-community/*` models).

Text generationlicense:apache-2.0
Context windowNot disclosed
Input → OutputText → Text
Model weightsNot verified
Profile updated2026/11/2 19:16:50
OVERVIEW

Model overview

LiteRT is Google's on-device runtime, the new name for TensorFlow Lite (Android: `com.google.ai.edge.litert:litert`), and `litert-torch`, the renamed `ai-edge-torch`, is its PyTorch converter: a PyTorch model converted unmodified with `litert_torch.convert` matched the original to 4e-7 on a Galaxy S26 (measured, LiteRT 2.2.0, Android 16, 2026-09-05). Measured on device (edge-compat): Galaxy S26 · LiteRT-LM prebuilt-adac974b · GPU · decode 22.1 tok/s · prefill 338 tok/s · TTFT 3.07 s · all 1210 ops delegated (2026-10-05); Pixel 8a · LiteRT-LM 0.16.0 · GPU · decode 6.8 tok/s · prefill 8 tok/s · TTFT 1.68 s · all 1187 ops delegated (2026-08-17); Mac Studio M4 Max · LiteRT-LM 0.17.1 · GPU · decode 129.5 tok/s · prefill 1340 tok/s · TTFT 201 ms · all 1210 ops delegated (2026-10-05). Record: https://github.com/john-rocky/edge-compat/blob/main/cards/ministral3-3b/CARD.md mistralai/Ministral-3-3B-Instruct-2512 converted to the **LiteRT-LM** (`.litertlm`) format for on-device inference with Google's LiteRT-LM runtime (the engine behind the official `litert-community/*` models).

Key features

特点摘要待依据来源补齐,不凭模型名称推断能力。

Limitations & considerations

Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.

PRICING & COST

Source prices & cost analysis

Prices vary by channel, region and configuration. Channel quotes are listed independently and are not treated as a single official provider price.

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Estimate monthly base token costs

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INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

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Frequently asked questions

Do open weights permit free commercial use?

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Sources & traceability

许可证:apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。