granite-4.2-3b

reasoning

Granite-4.2-3B is the most compact model in the family, aimed at scenarios with strict computational-resource constraints, while still retaining the key feature of the 4.2 generation — built-in reasoning. Architecture: a dense transformer with 3 billion parameters, 40 layers, 40 query heads and 8 KV heads, and an MLP hidden size of 8192. The model uses the same GQA scheme with full attention within a 128K-token context, RoPE, SwiGLU and RMSNorm. Full attention is especially important here because it allows even a compact model to build logical connections across the entire declared input length.

The 3B model supports three thinking modes — full, non-thinking and low-effort — as well as native tool calling, which makes it suitable for lightweight agentic scenarios and conversational interfaces. However, unlike the 8B and 30B models, it does not go through the agentic RL stage (SWE, terminal, search) and relies on basic RLVR for code and instruction following. This is not a drawback but a deliberate choice: the 3B model is intended for cases where autonomous solving of complex engineering tasks is not required, but a reliable reasoning model is needed that can be run on an edge device or in a memory-constrained environment.

Among the benchmarks, AIME25 (78.33) stands out — for a model of this size, this is a very high result. MMLU-Pro (67.84) reflects broad knowledge and instruction-following ability at a level comparable to larger models, which is important for a general-purpose assistant. RULER 128K (55.30) shows that the model handles long context, although this is not a high score, it is an acceptable result that allows it to be used in summarization and question-answering systems that explore medium-length documents.

Granite-4.2-3B is best suited for edge deployment, embedded assistants, document summarization, code/text autocomplete suggestions, and other lightweight tasks in memory-constrained environments. All models are distributed under the Apache 2.0 license, which removes concerns about commercial use.


Announce Date: 07.08.2026
Parameters: 4B
Context: 132K
Layers: 40
Attention Type: Full Attention
Developer: IBM
Transformers Version: 4.57.1
vLLM Version: >=0.17.0
License: Apache 2.0

Public endpoint

Use our pre-built public endpoints for free to test inference and explore granite-4.2-3b capabilities. You can obtain an API access token on the token management page after registration and verification.
Model Name Context Type GPU Status Link

API access to granite-4.2-3b endpoints

There are no public endpoints for this model yet.

Private endpoint

An endpoint is a server with pre-installed software that automatically downloads neural network weights and launches an OpenAI-compatible API to handle incoming requests.

A private endpoint is created within the user's project, belongs exclusively to the user, and is billed per server configuration in accordance with the infrastructure pricing.

We recommend creating private endpoints when you need to:

  • achieve maximum endpoint performance,
  • access the full context window for processing large requests and conversations,
  • ensure maximum security by processing data in a dedicated, isolated environment.

Recommended server configurations for creating a private inference endpoint granite-4.2-3b

Prices:
Name GPU Price, hour TPS Max Concurrency
teslaa10-1.16.32.160
131,072.0
1 $0.53 1.957 Launch
teslaa2-2.16.32.160
131,072.0
tensor
2 $0.57 2.322 Launch
rtx3090-1.16.24.160
131,072.0
1 $0.83 2.087 Launch
rtx3080-2.16.32.160
131,072.0
tensor
2 $0.97 1.098 Launch
rtx4090-1.16.32.160
131,072.0
1 $1.02 2.082 Launch
rtx3090-2.16.64.160.nvlink
131,072.0
tensor
2 $1.56 4.521 Launch
rtx5090-1.16.64.160
131,072.0
1 $1.59 3.042 Launch
teslaa100-1.16.64.160
131,072.0
1 $2.37 8.918 Launch
h100-1.16.64.160
131,072.0
1 $3.83 8.909 Launch
h100nvl-1.16.96.160
131,072.0
1 $4.11 10.616 Launch
teslaa100-2.24.96.160.nvlink
131,072.0
tensor
2 $4.61 18.183 Launch
h200-1.16.128.160
131,072.0
1 $4.74 16.345 Launch
h200-2.24.256.160.nvlink
131,072.0
tensor
2 $9.40 33.038 Launch
Prices:
Name GPU Price, hour TPS Max Concurrency
teslaa10-1.16.32.160
131,072.0
1 $0.53 1.785 Launch
teslaa2-2.16.32.160
131,072.0
tensor
2 $0.57 2.151 Launch
rtx3090-1.16.24.160
131,072.0
1 $0.83 1.915 Launch
rtx4090-1.16.32.160
131,072.0
1 $1.02 1.911 Launch
rtx3080-3.16.64.160
131,072.0
pipeline
3 $1.43 1.547 Launch
rtx3090-2.16.64.160.nvlink
131,072.0
tensor
2 $1.56 4.350 Launch
rtx5090-1.16.64.160
131,072.0
1 $1.59 2.871 Launch
teslaa100-1.16.64.160
131,072.0
1 $2.37 8.747 Launch
h100-1.16.64.160
131,072.0
1 $3.83 8.738 Launch
h100nvl-1.16.96.160
131,072.0
1 $4.11 10.445 Launch
teslaa100-2.24.96.160.nvlink
131,072.0
tensor
2 $4.61 18.012 Launch
h200-1.16.128.160
131,072.0
1 $4.74 16.174 Launch
h200-2.24.256.160.nvlink
131,072.0
tensor
2 $9.40 32.867 Launch
Prices:
Name GPU Price, hour TPS Max Concurrency
teslaa10-1.16.32.160
131,072.0
1 $0.53 1.395 Launch
teslaa2-2.16.32.160
131,072.0
tensor
2 $0.57 1.761 Launch
rtx3090-1.16.24.160
131,072.0
1 $0.83 1.525 Launch
rtx4090-1.16.32.160
131,072.0
1 $1.02 1.520 Launch
rtx3080-3.16.64.160
131,072.0
pipeline
3 $1.43 1.156 Launch
rtx3090-2.16.64.160.nvlink
131,072.0
tensor
2 $1.56 3.959 Launch
rtx5090-1.16.64.160
131,072.0
1 $1.59 2.481 Launch
teslaa100-1.16.64.160
131,072.0
1 $2.37 8.356 Launch
h100-1.16.64.160
131,072.0
1 $3.83 8.348 Launch
h100nvl-1.16.96.160
131,072.0
1 $4.11 10.054 Launch
teslaa100-2.24.96.160.nvlink
131,072.0
tensor
2 $4.61 17.622 Launch
h200-1.16.128.160
131,072.0
1 $4.74 15.784 Launch
h200-2.24.256.160.nvlink
131,072.0
tensor
2 $9.40 32.477 Launch

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