Laplace 3B

Overview

Laplace 3B is a 3-billion-parameter text-generation model from i14 built around linear attention rather than the conventional transformer attention pattern. Its headline differentiator is inference memory behavior: the model maintains a recurrent state of 29.5 MB per sequence regardless of context length, avoiding the key-value cache that grows with prompt length in standard transformer models. This makes Laplace 3B especially notable for long-context and streaming-style workloads where memory pressure, batching efficiency, and predictable serving cost matter.

Unlike full-attention transformers, which typically store increasingly large KV caches as conversations or documents grow, Laplace 3B uses a fixed-size state to summarize prior tokens. In practical terms, this can make long-running sessions easier to serve because each active sequence has a bounded state footprint. The model was trained and is served on AWS Trainium2, indicating that i14 optimized it for accelerator infrastructure designed for high-throughput model training and inference.

Capabilities and features

Laplace 3B is primarily a text model. Its stated capabilities include text generation, efficient inference, linear attention, and long-context processing. Suitable use cases may include drafting, summarization over long inputs, log or document analysis, coding assistance, agent memory compression, and applications where many concurrent long conversations must remain active without KV cache growth.

The fixed 29.5 MB recurrent state is the core architectural feature. It can improve memory predictability and may enable better throughput for long prompts than similarly sized transformer models whose memory use increases with context length. The 3B-parameter scale also positions Laplace 3B as a relatively compact model: large enough for general-purpose language tasks, but small enough to target lower-latency and cost-sensitive deployments compared with frontier-scale systems.

Technical specifications

• Provider: i14
• Model size: 3 billion parameters
• Architecture: linear-attention / recurrent-state language model
• Recurrent state: 29.5 MB per sequence, fixed regardless of context length
• Modalities: text input and text output; no image, audio, or video capability has been specified
• Training and serving hardware: AWS Trainium2
• Release date: 2026-10-20
• Context window: not publicly specified in the provided model information; the architecture is designed for long-context efficiency, but the supported maximum context length should be confirmed from i14 documentation
• Maximum output length: not specified
• Pricing: not specified
• Weights and license: no open-weight status or license terms have been specified
• Availability: described as trained and served on Trainium2; specific API, regional, or self-hosting availability is not stated

Strengths and benefits

Laplace 3B’s biggest advantage is predictable memory use for long sequences. For applications that keep persistent sessions open, process large documents, or maintain agent state, avoiding a growing KV cache can reduce infrastructure complexity and make latency/cost planning more reliable. The model’s compact size may also make it easier to deploy at scale than larger alternatives, especially if workloads prioritize throughput and long-context handling over maximum reasoning depth.

Limitations and caveats

Linear attention is not automatically superior to transformer attention. Fixed-state models must compress history into a bounded representation, which can create tradeoffs in exact recall, detailed retrieval from very long inputs, or tasks requiring precise token-level references far back in the context. At 3B parameters, Laplace 3B may also underperform larger transformer models on complex reasoning, multilingual breadth, coding depth, and instruction-following nuance. Key deployment details—context limit, pricing, rate limits, safety tuning, benchmark results, and license terms—remain unspecified, so production evaluation is essential.

Comparison

Compared with conventional transformer LLMs, Laplace 3B’s primary advantage is memory scaling: fixed recurrent state instead of a growing KV cache. Compared with other efficient-sequence approaches such as state-space, recurrent, or hybrid attention models, its significance depends on empirical quality, latency, and long-context recall benchmarks, which have not been detailed here. It should be viewed as an efficiency-focused alternative rather than a guaranteed replacement for larger frontier models.

Advances like fixed-state long-context inference can also help software maintenance tools that need to scan lengthy dependency manifests, changelogs, or audit reports, but that is a secondary application rather than the model’s main identity.