granite-ttm-1536-96-r2

What is IBM Granite TTM 1536-96-R2?

IBM Granite TTM 1536-96-R2 is a compact foundation model for time-series forecasting. A time series is a sequence of measurements collected over time, such as hourly electricity demand, daily product sales, machine temperature, website traffic, or transaction volume. The model examines historical observations and estimates what the next observations are likely to be.

The name identifies the model's main forecasting configuration. It can use a context of up to 1,536 historical data points per channel and is designed to forecast up to 96 future data points per channel. A channel can represent one measured variable, such as sales for a product, while a multivariate dataset can contain several related channels, such as sales, price, inventory, and promotions.

Unlike a general-purpose large language model, Granite TTM 1536-96-R2 does not generate prose, answer questions, write code, or analyze images. Its output is a numerical forecast. That specialization is important: the model's value comes from applying a relatively small model to structured temporal data, not from supporting a broad range of conversational tasks.

Provider and position in the Granite model family

The provider is IBM. Granite TTM 1536-96-R2 is part of IBM's Granite Tiny Time Mixer R2 family, which IBM positions among its foundation models for specialized enterprise workloads. The model was released on February 26, 2025, as part of an updated Granite family announcement.

It is available through IBM watsonx.ai, IBM's platform for foundation-model access, generative AI development, machine learning, and model deployment. IBM also distributes the model through its Granite time-series repositories, including a repository branch identified as 1536-96-r2. The distributed model weights are listed under the Apache 2.0 license.

IBM describes the Granite TTM models as compact pretrained models with approximately 1 million parameters and training on nearly a billion time-series samples. These are provider descriptions rather than independent benchmark results. They indicate the model's intended design goal: useful forecasting with a much smaller computational footprint than many general-purpose foundation models.

Core specifications

The 1,536-point context is the most important practical limit. If the data is recorded hourly, it represents up to 64 days of history. If it is recorded every 15 minutes, it represents up to 16 days. If it is recorded every minute, it represents approximately 25.6 hours. The model's usefulness therefore depends on both the number of observations and the sampling interval.

The 96-point forecast horizon should also be interpreted in the same way. Ninety-six hourly predictions cover four days, while 96 fifteen-minute predictions cover one day. The model does not define a universal calendar period; the real-world duration depends on how frequently the source system records observations.

How the model is used

A typical workflow provides historical observations for one or more channels, identifies the forecast horizon, and receives numerical predictions for the next points in the sequence. The input is structured time-series data rather than text prompts. This makes the model suitable for systems that already collect operational or business measurements.

For example, a retailer could supply historical hourly demand for several stores and forecast the next 96 hours. A manufacturer could use sensor readings to estimate near-term operating conditions. An energy operator could forecast electricity load across several related locations. These examples are use-case illustrations based on the model's supported forecasting purpose; they are not claims of a particular benchmark result.

Multivariate forecasting can be useful when channels are related. Demand at several locations, temperature readings from multiple sensors, or sales for several products may contain patterns that are more informative together than in isolation. The available research identifies the model as a multivariate forecaster, but it does not provide a universal rule for how many channels a deployment should use or guarantee that adding channels will improve accuracy.

Main strengths and trade-offs

• Long historical context for its size: the model uses up to 1,536 observations per channel, making the 1536-96 configuration suitable for workloads that need more history than shorter-context forecasting variants.
• Specialized output: it directly targets numerical time-series forecasting instead of requiring a general language model to interpret tabular data and produce an indirect answer.
• Small computational footprint: IBM describes the Granite TTM models as compact models with approximately 1 million parameters. This supports use cases where inference efficiency and deployment cost matter.
• Open distribution: the model weights are distributed under the Apache 2.0 license, which can be useful for teams that need more control over deployment than a hosted-only service provides.
• Enterprise access: watsonx.ai provides a managed route for organizations that want IBM platform integration, while the open model distribution provides another route for technical teams.

The trade-off is specialization. A compact forecasting model cannot replace a general-purpose language model, statistical forecasting package, or domain-specific anomaly-detection system in every situation. It is designed to forecast sequences, not to explain business context, retrieve documents, call tools, or generate a natural-language report.

Pricing and API access

The supplied IBM watsonx.ai pricing information lists an input price of $0.00013 per 1,000 data points and an output price of $0.00038 per 1,000 data points. These are usage prices, not monthly subscription fees. Input data points refer to the historical observations sent for forecasting, while output data points refer to forecast observations returned by the service.

For a simple illustration, a request containing 1,536 input points and producing 96 output points would be priced according to the applicable input and output units. The exact bill depends on how IBM counts points across channels and requests, so production teams should verify the current platform pricing and metering rules before estimating costs. The supplied research does not establish a separate monthly plan, minimum commitment, or guaranteed total cost.

The canonical watsonx.ai model identifier is ibm/granite-ttm-1536-96-r2. Availability, supported deployment modes, and pricing can change across IBM services and regions. The model is also represented in IBM's open Granite time-series repositories, but hosted API pricing should not be assumed to apply to self-managed use of the distributed weights.

Modalities and capability boundaries

Granite TTM 1536-96-R2 accepts time-series observations and produces time-series forecasts. It is not a text, image, audio, or video model. The research records no text output, image output, video output, audio output, embedding output, or speech output for this item.

There is no verified reasoning score or general reasoning mode for this model. Its forecasting behavior should not be described as chain-of-thought reasoning. It learns temporal patterns and generates numerical estimates, but it does not provide the sort of explanation or multi-step natural-language analysis associated with chat-oriented models.

Speed and cost profile

The supplied catalog assigns Granite TTM 1536-96-R2 a speed score of 9 and a cost score of 9. These are editorial estimates for a specialized forecasting model, not IBM-published benchmark results. They suggest that the model is expected to be attractive when low computational overhead and low usage cost are more important than broad functionality.

Those scores should not be compared directly with scores for general-purpose language models. A forecasting request and a text-generation request use different data units, workloads, and quality criteria. In practice, the model's efficiency advantage comes partly from its narrow purpose and compact architecture. A team that needs a conversational explanation, document retrieval, coding, or tool execution would need additional components even if the forecast itself is inexpensive.

Best use cases

Granite TTM 1536-96-R2 is a strong candidate when the data is regularly sampled, historical observations are available, and the required forecast horizon fits within 96 points. Suitable workloads include:

• short-term demand forecasting for products, stores, or services;
• traffic and usage forecasting for digital or physical infrastructure;
• electricity-load and other operational energy forecasts;
• manufacturing and equipment measurements collected at regular intervals;
• financial or business series where near-term numerical forecasts are required;
• multichannel forecasting pipelines that need a compact model and predictable input/output limits.

The model is particularly relevant when at least 1,536 historical observations per channel are available and the observations represent minute- or hour-level behavior. Teams should still validate accuracy on their own data. Forecast quality can depend on missing values, changes in seasonal patterns, unusual events, data frequency, channel relationships, and the difference between historical conditions and future conditions.

When to choose this model

Choose Granite TTM 1536-96-R2 when the primary problem is numerical forecasting, the available history is substantial, and a compact model is preferable to a broader but more expensive system. It is a sensible option for an organization that wants the same forecasting model available through watsonx.ai and through IBM's open model distribution, subject to the deployment and licensing requirements of the chosen route.

Another forecasting approach may be more appropriate when the dataset contains far fewer than 1,536 historical points per channel, when the required horizon is substantially different from 96 points, or when the task is not forecasting at all. A conventional statistical method may be easier to operate for a small, stable, single-series problem. A different time-series model may be preferable if its context or horizon better matches the sampling frequency and planning window. A general-purpose language model is more appropriate when the main requirement is text generation, conversational analysis, coding, document understanding, or tool use.

Granite TTM 1536-96-R2 should also not be selected merely because it is part of the Granite brand. Its benefit comes from matching its input format, context length, and forecast horizon to the operational problem. If a team needs a complete forecasting application, it will still need data preparation, validation, monitoring, handling for missing or abnormal values, and a way to present forecasts to users.

Limitations to check before deployment

The model's fixed configuration creates clear boundaries. The maximum historical context is 1,536 data points per channel, and the documented forecast capability extends up to 96 future points per channel. The supplied research does not specify a larger context option, a larger maximum horizon for this exact item, or a general-purpose fallback mode.

It also does not provide independent accuracy benchmarks, domain-specific guarantees, or a universal recommendation for sampling frequency. IBM's description says the model works best with minute- or hour-level data, but each deployment should test whether that frequency captures the patterns that matter. A model that performs well on regular operational data may not behave the same way after a major market change, equipment failure, policy change, or other event outside the training distribution.

Finally, the model does not explain forecasts in natural language by itself. If users need reasons for a prediction, confidence reporting, scenario analysis, or business recommendations, those functions must be supplied by the surrounding forecasting and analytics system rather than assumed to come from Granite TTM 1536-96-R2.

Bottom line

IBM Granite TTM 1536-96-R2 is a focused, compact forecasting model for multivariate time series. Its defining specification is the combination of a 1,536-point historical context and a forecast horizon of up to 96 points per channel. That makes it most useful for near-term, regularly sampled business and operational data where efficient inference and low usage cost matter.

It is not a chatbot or general AI assistant. The best reason to choose it is the fit between its specialized forecasting design and a workload with enough history, a compatible sampling interval, and a forecast window within its documented limits. For those conditions, it offers IBM watsonx.ai access, open Apache 2.0 model distribution, and a compact alternative to broader AI systems.