KnowLine-35B-A3B-Gen1

KnowLine-35B-A3B-Gen1 is an open-weight decision model developed by PelaAI, engineered specifically for fast, structured classification rather than generative chat. Built on top of the Qwen3.5-35B-A3B architecture, it pairs a 35.95B total parameter Mixture-of-Experts (MoE) foundation with an active compute footprint of approximately 3B parameters per token. Released under the Apache-2.0 license, it targets developers building autonomous agents, intent routers, and programmatic evaluation pipelines who need deterministic, calibrated probability distributions rather than autoregressive text streaming.

Instead of generating freeform tokens one by one, KnowLine evaluates an input state (such as a customer ticket, system event, or conversation log) alongside up to 64 typed questions simultaneously. These questions cover binary decisions, multiple-choice selection, or rubric-based scoring. The model resolves all questions in a single forward pass, returning probability distributions over the target token candidates. This approach yields a score of 69.49 on the Decision Index 0.3 public suite, placing it well ahead of established commercial baselines like Jev 1.13 (57.96) and competitive 27B-class decision architectures.

For engineers seeking a dedicated local AI model with 35.95B parameters in 2026, KnowLine-35B-A3B-Gen1 bridges the gap between lightweight edge classifiers and resource-intensive generative giants. Its architecture eliminates the decoding loop entirely, delivering sub-second decisions on complex multi-attribute states while maintaining open commercial licensing.

The underlying architecture relies on Qwen3.5-35B-A3B, utilizing a sparse Mixture of Experts design. While the entire model holds 35.95B parameters in memory, the routing mechanism directs each token to a specialized subset of experts, activating only around 3B parameters per forward pass.

This structure grants KnowLine-35B-A3B-Gen1 MoE efficiency during inference. Computational latency and FLOP demands match a lightweight 3B dense model, but the model retains the broad representational capacity and world knowledge of a 36B network. Training was conducted via LoRA fine-tuning (rank 32, alpha 64) applied across the language model and its routed experts, subsequently merged directly into the base weights.

Unlike standard text generators, KnowLine modifies the final norm layer to fold in a fitted readout temperature ($T = 1.20$). This baked-in calibration adjusts the raw logit outputs so that returned probabilities match empirical real-world likelihoods without requiring external post-processing. Evaluated against standard calibration metrics, the model demonstrates an Expected Calibration Error (ECE) between 0.04 and 0.05 and a Brier score between 0.25 and 0.27.

The model also retains the base architecture's vision tower and multi-token prediction (MTP) heads unchanged. While PelaAI's decision tuning focused strictly on text data across English, Simplified Chinese, and Traditional Chinese, the intact vision tower allows zero-shot multimodal scoring on supported harnesses. Context length handling follows the base Qwen3.5 backbone, though typical operational states for decision routing fit comfortably inside standard production context windows.

KnowLine-35B-A3B-Gen1 acts as a System One controller in agentic architectures. Rather than waiting for an LLM to generate JSON strings that require parsing and validation, developers use KnowLine to execute instant, type-safe policy branches.

Key operational capabilities include:

• Multi-Attribute State Triage: Ingest a raw customer service transcript and query refund eligibility, sentiment category, priority level, and escalation paths in a single pass.
• Prompt Injection Resistance: Fine-tuned specifically to resist malicious user steering. In standard benchmark evaluations, planted instructions in the state changed outputs in 0.0% of test cases, rising to only 0.8% on completely novel injection templates.
• High-Volume Agent Routing: Acts as a high-precision dispatcher that evaluates tool selection criteria or sub-agent handoffs with deterministic probability scores.
• Multilingual Evaluation: Retains strong reasoning performance across English and Chinese datasets, scoring 87.6 on C-Eval alongside strong marks on BBH (64.9) and MMLU-Pro (66.6).

Because the output is a native probability distribution, applications can implement dynamic thresholding. For example, an automated banking agent can auto-approve an action if the model's confidence exceeds 98%, while routing any decision falling between 50% and 97% to human review.

To run KnowLine-35B-A3B-Gen1 locally, deployment architecture must account for the difference between total memory storage and active execution compute.

The full unquantized weights in bfloat16 format require roughly 67 GB of storage. However, standard serving for this model relies on SGLang, using PelaAI's provided serving scripts which automatically convert the weights to FP8 precision at load time.

• FP8 Execution (Recommended): The loaded model occupies approximately 35 GB of VRAM. Accounting for KV cache allocation and batch buffers, the minimum hardware requirement is 48 GB of GPU memory.
• BF16 Execution (Native): Requires at least 80 GB to 96 GB of VRAM for comfortable inference headroom.

Practitioners evaluating the best GPU for KnowLine-35B-A3B-Gen1 setups have several practical deployment paths:

• Workstation / Datacenter Cards: A single 48 GB card, such as an NVIDIA RTX 6000 Ada or an A40, comfortably fits the FP8 runtime. For enterprise servers, single 80 GB A100 or 96 GB H20/H100 accelerators provide maximum throughput.
• Multi-Consumer GPU Rigs: If you are planning how to run 35.95B model on consumer GPU setups, two 24 GB cards (such as two RTX 3090 or RTX 4090 GPUs) pooled via PCIe or NVLink can serve the model in FP8 across a split tensor-parallel context.
• Apple Silicon: A Mac Studio or MacBook Pro equipped with an M3/M4 Max or Ultra chip (64 GB or 128 GB unified memory) can run the model entirely in memory without offloading penalties.

The best quantization for KnowLine-35B-A3B-Gen1 in production is FP8 via SGLang. Because the model operates on single forward passes rather than autoregressive decoding loops, standard metrics like tokens per second do not capture its actual throughput.

Instead of generating 50 tokens at 40 tokens per second (taking over a second per query), KnowLine processes the prompt and evaluates up to 64 decisions in a single forward inference step. Across a typical 48 GB VRAM setup running FP8, single-query decision latency typically ranges between 30 ms and 90 ms depending on state length. If quantized to GGUF (such as Q4_K_M) for low-memory local experimentation via llama.cpp or Ollama wrappers, expect slight degradations in probability calibration, making FP8 the strongly preferred target for mission-critical routing.

To launch the native server:

1pip install "sglang==0.5.21" "transformers==5.12.1" requests
2bash serve_knowline.sh PelaAI/KnowLine-35B-A3B-Gen1 0 8080

This command hosts the FP8 SGLang backend on port 9080 while exposing the /v1/systemone decision API on port 8080.

Understanding where KnowLine fits requires contrasting it both with smaller members of its own family and with competing decision architectures.

PelaAI previously established its decision framework with the KnowLine-4B series. KnowLine-4B-Gen4 achieved a Decision Index 0.3 public score of 64.90, functioning as an agile, low-resource classifier runnable on single 16 GB GPUs.

KnowLine-35B-A3B jumps to 69.49 on the same index. The 4.59 point overall increase is driven almost entirely by complex knowledge retrieval and multi-step reasoning tasks where the 4B model struggled. On MMLU-Pro, Gen1 outscores Gen4 by 15.0 points (66.6 vs 51.6); on Big-Bench Hard (BBH), it gains 11.8 points (64.9 vs 53.1). If your pipeline only requires simple keyword-adjacent sentiment scoring, KnowLine-4B is far lighter on VRAM. If your decision logic requires real-world common sense and domain comprehension, the 35B model is noticeably superior.

Compared to proprietary and specialized baselines on the Decision Index:

• Jev 1.13: Scores 57.96 on the public index. In head-to-head mirror matches across standard game and decision harnesses, KnowLine-35B-A3B dominates with an overwhelming win rate (17-0-1 on the KOF '98 test suite).
• RSI-Jev v6.1 VL (27B): Scores 65.64 on the public Decision Index suite. KnowLine's 69.49 score provides a clear accuracy margin, particularly on out-of-domain evaluation suites (56.4 vs 49.3 on novel tasks).

For practitioners needing production-grade decision mechanics without cloud dependencies, KnowLine-35B-A3B-Gen1 delivers elite accuracy, enterprise-level calibration, and transparent Apache-2.0 licensing on accessible hardware configurations.