KnowLine-26B-A4B-Gen2

KnowLine-26B-A4B-Gen2 is an open-weight decision model released by PelaAI, engineered specifically for deterministic routing, state evaluation, and classification rather than conversational text generation. Built on top of Google's Gemma 4 base (google/gemma-4-26B-A4B-it) and distributed under the Apache-2.0 license, the model uses a mixture-of-experts (MoE) architecture comprising 25.81B total parameters, of which only 4B are active during any individual token pass.

Rather than predicting the next sequential token in an open-ended response, KnowLine operates as a fast, "System One" decision engine. You feed it a textual context state alongside up to 64 typed target queries (such as binary yes/no questions, k-choice selections, or rubric scoring scales). The model evaluates every query in parallel in a single forward pass, returning a calibrated probability distribution directly from the logit layer. With a Decision Index 0.3 public suite score of 66.94 and an 88.3 rating on JevBench, it serves as a specialized, low-latency alternative to standard generative models for agent control loops.

For practitioners looking to run KnowLine-26B-A4B-Gen2 locally, this architectural pattern completely shifts the typical hardware calculus. Because inference bypasses multi-token autoregressive decoding, response latency depends strictly on prompt ingestion and forward pass execution over its 4B active parameters, while overall memory overhead is bounded by its 25.81B parameter footprint.

KnowLine-26B-A4B-Gen2 merges three rounds of LoRA supervised fine-tuning across the routed experts of google/gemma-4-26B-A4B-it. Its technical footprint is defined by two primary characteristics: MoE routing efficiency and pre-calibrated output layers.

• Parameter Breakdown: 25.81B total parameters across all experts, routing to 4B active parameters per token.
• Inference Mode: Pure classification and probability scoring. It does not generate text strings.
• Logit Calibration: The release folds a temperature scaling parameter (T = 1.25) directly into the final normalization weights and logit soft-capping. This eliminates manual post-hoc temperature scaling, producing an Expected Calibration Error (ECE) near 0.01 and a Brier score between 0.26 and 0.28.
• Prompt Injection Defense: Hardened against adversarial steering. In provider benchmark sets, instructions planted inside the input state altered model decisions in only 0.1% of observed cases on known patterns, and 1.0% on zero-shot injection templates.
• Context Length: Base context handling relies on the Gemma 4 foundation. While context length is not explicitly restricted in the release card, practical usage focuses on short-to-medium agent states paired with structured query arrays.

KnowLine-26B-A4B-Gen2 MoE efficiency stems from decoupling memory bandwidth needs from compute cost. While all 25.81B parameters must reside in memory, each pass processes only 4B parameters. This gives the model the capacity and task knowledge of a mid-sized network while retaining the execution speed of a compact edge model.

Traditional LLMs used for classification or routing require expensive decoding cycles, JSON formatting constraints, and post-processing parsers. KnowLine-26B-A4B-Gen2 eliminates these failure points by mapping questions directly to three decision primitives:

1. Choice Primitives: Evaluating multi-choice routing paths (1-of-k options). On the Open-Jev 1.1 test and out-of-distribution suites, it hits 88.5% and 87.9% accuracy, making it suitable for agent state navigation, API tool routing, and customer workflow dispatching.
2. Noul (Binary) Primitives: Boolean determinations (yes/no, allow/block). Ideal for zero-shot content moderation, policy enforcement, and autonomous guardrails that must reject planted prompt injections without hallucinating.
3. Score Primitives: Evaluating an input against continuous rubric grades for automated QA pipelines and code review assessments.

In specialized benchmarks, KnowLine demonstrates robust real-time utility. In game state harnesses like King of Fighters '98, it achieved a 16-2-0 record against Jev and 17-1-0 against StartLux-Decision-35B-A3B in single-bout mirror matches. For local systems requiring automated decisions under 50 milliseconds, this setup replaces fragile prompt-engineered classifiers.

Because the model has 25.81B parameters, your GPU memory capacity dictates whether you can run it locally without system RAM offloading. KnowLine-26B-A4B-Gen2 VRAM requirements vary significantly based on precision:

• BF16 / FP16 (Unquantized): Requires approximately 52 GB to 56 GB of VRAM. Needs a multi-GPU workstation (such as 2x RTX 3090 / 4090 24GB cards), a single 80GB enterprise GPU (A100 / H100), or a unified memory Mac (M3/M4 Max with 64GB+ RAM).
• 8-bit Quantization (Q8_0): Requires approximately 28 GB to 30 GB of VRAM. Fits cleanly on an RTX 6000 Ada, an A5000/A6000, or a dual-GPU consumer rig.
• 4-bit Quantization (Q4_K_M): Requires approximately 16 GB to 18 GB of VRAM. This is the sweet spot for consumer desktop hardware, fitting onto a single 24GB card with comfortable headroom.

If you want to run this local AI model with 25.81B parameters on a single consumer GPU, the best GPU for KnowLine-26B-A4B-Gen2 is an Nvidia GeForce RTX 4090 (24GB) or RTX 3090 (24GB) using Q4_K_M quantization. For Apple Silicon environments, an M3 Max or M4 Max Mac Studio / MacBook Pro with at least 36GB of unified memory handles 4-bit quantizations without swap penalties, while a 64GB machine can run the unquantized weights.

The best quantization for KnowLine-26B-A4B-Gen2 for most local deployments is Q4_K_M. Because the weights originate from Gemma 4 MoE layers, 4-bit quantization preserves roughly 98% of routing precision and calibration scores. Moving down to Q3 or Q2 variants is not recommended, as aggressive quantization degrades MoE gating mechanisms and spoils logit calibration curves.

Practitioners looking up KnowLine-26B-A4B-Gen2 tokens per second should note that standard text-generation metrics do not apply. The model processes prompts and outputs probabilities without auto-regressive decoding loops.

On an RTX 4090 running SGLang or optimized llama.cpp backends:

• Forward Pass Latency: A typical query array (1 to 16 questions over a 1,000-token state) finishes in 40 to 90 milliseconds.
• Throughput: Rather than waiting for 50 tokens of generated explanation text at 40 tokens per second (taking over a second), KnowLine yields definitive route classifications nearly instantaneously.

While PelaAI provides a native SGLang serving script (serve_knowline.sh) in the model repository, the easiest way to run KnowLine-26B-A4B-Gen2 locally in an existing container stack is through an OpenAI/Jev-compatible local server. If converted GGUF files are loaded through Ollama or llama.cpp, ensure your client targets the logit distribution outputs rather than requesting streaming text completions.

Evaluating KnowLine-26B-A4B-Gen2 depends on whether you compare it to sibling decision architectures or general-purpose dense LLMs.

PelaAI's larger sibling, KnowLine-35B-A3B-Gen1, scores higher on the core Decision Index suite (69.49 vs 66.94) and holds a significant advantage in Chinese language benchmarks (C-Eval 87.6 vs 74.8). However, KnowLine-26B-A4B-Gen2 displays noticeably stronger generalizability on out-of-distribution tasks from unseen domains, scoring 60.8 across a 34-task out-of-domain evaluation compared to 52.7 for the 35B model. For diverse, unpredictable agent workloads, the 26B-A4B model provides more resilient routing across varied schemas.

Using standard dense models for programmatic routing forces you to manage structured schema prompts (like Instructor or Outlines) and wait through autoregressive decoding. A dense 27B model running at 30 tokens per second introduces a 1 to 2 second delay just to emit a JSON object indicating an action choice. KnowLine-26B-A4B-Gen2 delivers decisions in under 100 milliseconds with calibrated probabilities (giving you an exact confidence score alongside the choice), while consuming only 4B active parameters of compute during inference. Choose KnowLine when your pipeline requires fast, programmatic system routing without conversational overhead.