Turion Jev 31B

Turion Jev 31B is a 31.27B parameter dense decision model developed by Turion.AI, fine-tuned from Google's google/gemma-4-31B-it foundation. Instead of operating as a conventional conversational LLM, Turion Jev functions as a dedicated System 1 fast-judgment engine. It processes an input state alongside typed queries (multiple choice, yes/no, or 0 to 5 scalar ratings) and returns calibrated probability distributions across every possible outcome in a single forward pass. Because it eliminates text generation entirely, it sidesteps the latency overhead, token costs, and parsing failures inherent to traditional autoregressive inference.

Released under a permissive Apache-2.0 license, the model establishes a high-water mark for open-weights routing and classification. On the Decision Index 0.3 public evaluation suite, Turion Jev 31B scores 66.16, proving highly effective at deterministic classification across massive label sets. For practitioners seeking a local AI model with 31.27B parameters in 2026, it occupies a specialized niche: replacing multi-step prompt engineering and fragile JSON-constrained decoders with a single, high-throughput model designed specifically for agent decision loops and automated triage.

Turion Jev 31B retains the dense transformer backbone of Gemma 4, featuring 31.27 billion active parameters with standard causal self-attention. Rather than relying on a sparse Mixture-of-Experts (MoE) topology, its dense architecture guarantees consistent compute and memory access patterns across every query, regardless of input routing paths.

The model is configured with a 65,536-token context window. This large context capacity allows entire documents, long conversation histories, or extensive JSON state logs to precede the decision prompt without aggressive truncation. The execution pipeline utilizes several proprietary optimizations:

• Single-Token Option Mapping: Choice queries format each candidate answer into single-token codes. The model supports up to 255 discrete options in a single pass using token identifiers (A through Z, followed by selected two-letter single Gemma tokens).
• Logit Readout without Generation: The output layer evaluates the next-token log-probabilities across only the valid option codes immediately following the prompt. Probabilities are normalized across the active set, producing immediate numeric confidences without decoding text.
• Empirical Calibration: Logits are scaled using per-question-type temperature calibrations stored in calibration.json (1.18 for multiple choice, 1.18 for yes/no, and 1.12 for scalar ratings).
• Order Averaging and Read-Twice Passes: To eliminate positional bias in choice fields, the inference engine supports order averaging by testing the options in reverse order. For inputs under 12,000 characters, the model architecture supports an internal repeat pass within the prompt to maximize attention coverage across complex state representations.

Because Turion Jev 31B generates zero output tokens, it excels in workflows that demand deterministic, structured decisions with quantifiable confidence scores:

• High-Fanout Ticket and Query Routing: Real-world classification problems rarely fit into three or four categories. With native support for up to 255 choice options per question, the model can categorize incoming support tickets, triage infrastructure alerts, or assign operational leads across hundreds of potential destinations in one inference pass.
• Agentic Flow Control: Autonomous agents frequently stall while waiting for heavy reasoning models to make simple control-flow decisions. Turion Jev 31B acts as a local router, evaluating intermediate tool outputs and deciding whether to proceed, loop, request human escalation, or terminate.
• Calibrated Risk and Compliance Scoring: Using 0 to 5 scalar scoring, teams can run automated fraud screenings, compliance reviews, or urgency detection. Because the engine emits raw probability distributions rather than a single argmax answer, downstream code can set rigorous thresholds (for example, routing to human operators whenever top-choice confidence drops below 80 percent).
• Pre-Filtering and Frontier Model Distillation: When logging agent traces, Turion Jev 31B can serve as a local gatekeeper, filtering out routine requests on local hardware and passing only ambiguous or low-confidence edge cases to expensive cloud frontier APIs.

To run Turion Jev 31B locally, hardware planning centers entirely on prefill processing and parameter footprint, rather than autoregressive decoding throughput.

Memory requirements depend on the precision level you deploy. When calculating capacity, ensure you account for the 65,536-token KV cache if you plan to feed large input states:

• FP16 / BF16 (Unquantized): Requires ~65 GB to 70 GB of VRAM just for weights. Accounting for KV cache at long context depths, you need at least 80 GB to 96 GB of total memory.
• FP8 (Recommended for Enterprise/Workstation): Requires ~34 GB to 38 GB of VRAM for weights. A 48 GB card or dual 24 GB setup comfortably accommodates the model and long-context activations.
• Q4_K_M (Recommended for Consumer Hardware): Weighs in at approximately 19 GB to 22 GB. This fits squarely within the memory limits of single 24 GB consumer cards.

Understanding how to run a 31.27B model on consumer GPU setups comes down to target context length:

• Single RTX 3090 / RTX 4090 (24 GB): The most accessible route. Running Q4_K_M quantization gives you 2 GB to 4 GB of headroom for context buffers. This is ideal for ticket routing and short-to-medium states up to 8,192 tokens.
• Dual RTX 3090 / 4090 (48 GB Total): Enables native FP8 execution with full 65,536 context length support via tensor parallelism in vLLM.
• Apple Silicon (M3/M4 Max with 64 GB+ Unified Memory): An excellent local development platform. Unified memory allows running the model unquantized or at Q8 with substantial context overhead.
• Workstation GPUs: The best GPU for Turion Jev 31B in high-throughput enterprise environments is an NVIDIA RTX PRO 6000 (Blackwell or Ada Generation, 48 GB to 96 GB) or an NVIDIA A100/H100, which matches the official reference testing configuration.

While many developers use Ollama as the quickest way to get started with generalist GGUF models, Turion Jev 31B uses a specialized inference pattern. Because it relies on reading log-probabilities across single-token option codes rather than generating a token sequence, standard autoregressive chat templates do not expose its full functionality.

The recommended deployment path is using the model's included turion_jev.kit_engine with vLLM as the backend, or launching its TypeSafe-compatible server via:

1python -m turion_jev.serve --model /path/to/turion-jev-31b --backend vllm --quantization fp8

For users deploying via quantized GGUF formats in standard runtimes, Q4_K_M remains the best quantization for Turion Jev 31B, maintaining high accuracy across calibration suites while minimizing memory pressure.

Traditional metrics like Turion Jev 31B tokens per second do not apply in the standard sense because the model generates exactly 0 output tokens. Measuring Turion Jev 31B performance requires evaluating prefill speed and single-pass request latency:

• Workstation FP8 (RTX PRO 6000 96 GB): Achieves a median latency of 116 ms, a mean of 288 ms, and an 80th-percentile latency of 352 ms on the official benchmark suite.
• Consumer Hardware (RTX 4090, Q4_K_M): Yields single-request routing decisions in 200 ms to 450 ms for typical input contexts (1,000 to 4,000 tokens).

Because there is no decoding phase, you can process dozens of complex routing decisions per second on modest local hardware.

Practitioners often attempt routing tasks using general-purpose instruct models like Qwen 2.5 32B-Instruct through JSON schemas or instructor libraries.

The differences in production are stark:

• Latency and Throughput: A generalist model must decode 20 to 80 tokens to emit valid JSON, taking anywhere from 800 ms to 2,500 ms per request. Turion Jev completes the operation in a single forward pass (100 ms to 350 ms).
• Reliability: General-purpose models occasionally hallucinate invalid schema keys or produce malformed strings under heavy load. Turion Jev operates purely on internal logits, entirely preventing schema corruption.
• Confidence Calibration: Standard LLMs are notoriously overconfident and poorly calibrated. Turion Jev outputs mathematically calibrated log-probabilities, enabling deterministic thresholds for human fallback.

Within the dedicated Decision Index leaderboard, Turion Jev 31B ranks among the top performers, tied at rank 2 on the Decision Index 0.4.1 suite (70.47) and scoring 66.16 on Decision Index 0.3.

• Versus KnowLine 31B / Sieve 31B: Models like KnowLine 31B Gen1 and Sieve 31B v3 hold slight benchmark leads on specialized private test splits (scoring ~71.10 to 71.86). However, Turion Jev 31B provides clean open-source licensing (Apache-2.0), standard Gemma-compatible attention blocks, and drop-in compatibility with the TypeSafe /v1/systemone standard API, making it far simpler to integrate into existing production pipelines.