Trendyol Asure 12B
Turkish-language multimodal instruct model built on Gemma 3 12B for e-commerce text, chat, and image-text tasks
Trendyol Asure 12B
Turkish-language multimodal instruct model built on Gemma 3 12B for e-commerce text, chat, and image-text tasks
- • FP16 Weights = 12.0B × 2B = 24.00 GB
- • INT4 Weights = 12.0B × 0.55B = 6.60 GB
- • KV Cache (131072 ctx, FP16) ≈ 16.00 GB
- • Activation Buffer = ~20% overhead
Hardware & Execution ParametersMultimodal
Genealogical Graph & Evolutionary Provenance
Tracing foundational base architecture ancestry, architectural successors, scale siblings, and reasoning distillation derivatives.
Trendyol Asure 12B operates as an autonomous foundation model architecture without direct precursor derivatives in this catalog.
Architecture Engineering & Capability Deep-Dive
An objective architectural evaluation of Trendyol Asure 12B by Trendyol, analyzing underlying compute dynamics, memory constraints, and deployment economics.
Topology & Attention Mechanics
A robust autoregressive transformer utilizing standard attention patterns for predictable and coherent token generation.
Evaluation Profile & Reasoning
Exhibits frontier-tier behavior in reasoning and coding.
LLM Hardware Sizing & Serving
For open deployments via vLLM/SGLang, quantization (INT4/AWQ) is heavily recommended to fit dense memory constraints, or multi-GPU pipeline parallelism for full FP16.
Inference Economics & Workflows
Well-suited for enterprise pipelines where capability is balanced against per-million token costs.
Architectural Strengths vs. Considerations
An objective balance sheet analyzing the operational advantages and production constraints of deploying Trendyol Asure 12B.
Key Architectural Strengths
- Massive 131,072-token context allows full-repository and book-length ingestion.
Operational Considerations
- 128k+ token prefill stages become heavily compute-bound and balloon KV cache without PagedAttention chunking.
Inference Runtimes & Hardware Sizing
Deployment targets, inference engines, and memory requirements for Trendyol Asure 12B.
python3 -m vllm.entrypoints.openai.api_server --model Trendyol Asure 12B --tensor-parallel-size 1 --gpu-memory-utilization 0.95 --max-model-len 4096 --enable-chunked-prefillollama run trendyol asure 12bpython3 -m sglang.launch_server --model-path Trendyol Asure 12B --tp 1 --trust-remote-codetext-generation-launcher --model-id Trendyol Asure 12B --num-shard 1 --max-batch-prefill-tokens 32000High-throughput PagedAttention server
One-click CLI & local desktop serving
Fast multi-turn structured decoding
Text Generation Inference
GGUF CPU/Apple Silicon execution
~28.8 GB VRAM required
~14.4 GB VRAM (Hopper speedup)
~7.9 GB VRAM
CPU RAM / Apple Silicon optimized
API & Deployment Pricing
Open-weights model available for local and private cloud deployment. Compute costs depend on the target GPU hardware instance.
| Deployment Tier | Pricing Structure |
|---|---|
| Open Checkpoint Weights | $0.00 (Free Download) |
| Inference Token Consumption | $0.00 / Token |
Comparable Foundation Architectures
Alternative models in the Multimodal class with similar capabilities, context windows, or deployment profiles.
Llama Nemotron Rerank VL 1B v2
import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ.get("TRENDYOL_KEY", "EMPTY"),
base_url="http://localhost:8000/v1"
)
response = client.chat.completions.create(
model="trendyol-asure-12b",
messages=[{"role": "user", "content": "Explain quantum superposition in 2 sentences."}]
)
print(response.choices[0].message.content)Frequently Asked Questions about Trendyol Asure 12B
Essential facts, architectural specs, hardware constraints, and pricing answers for Trendyol Asure 12B.
To run Trendyol Asure 12B (12B) locally, you generally need Depends on quantization. We recommend using quantized GGUF/AWQ formats with Ollama or vLLM to optimize memory footprint.
All technical specifications, parameter distributions, context architectures, and benchmark evaluations for Trendyol Asure 12B are audited against primary source release documentation, research whitepapers, and verified vendor API endpoints.