What is the difference between Llama Nemotron Rerank VL 1B v2 and Trendyol Asure 12B?

Llama Nemotron Rerank VL 1B v2 (NVIDIA) features ~1B with a 8k context window, while Trendyol Asure 12B (Trendyol) features 12B with a 131k context window.

Which is cheaper for production inference, Llama Nemotron Rerank VL 1B v2 or Trendyol Asure 12B?

Llama Nemotron Rerank VL 1B v2 input pricing is Open / Free per 1M tokens vs Trendyol Asure 12B at Open / Free per 1M tokens.

Can I run Llama Nemotron Rerank VL 1B v2 or Trendyol Asure 12B locally on private GPU hardware?

Llama Nemotron Rerank VL 1B v2 is an open-weights model and can be hosted locally using Ollama or vLLM. Trendyol Asure 12B is an open-weights model and can be hosted locally.

Back to LLM Benchmark & Hardware Sizing Engine

NVIDIAvsTrendyol

Llama Nemotron Rerank VL 1B v2 vs Trendyol Asure 12B

Side-by-side technical showdown between Llama Nemotron Rerank VL 1B v2 and Trendyol Asure 12B on TheModelverse. Compare verified LLM benchmark scores, quantization compression, local GPU hardware sizing, and API inference pricing.

Executive Showdown & Winner Breakdown

Who Wins Where: Llama Nemotron Rerank VL 1B v2 vs Trendyol Asure 12B

Verified across benchmarks, pricing & local VRAM footprint

VRAM Efficiency (INT4)

7 GB lighter

Leader:Llama Nemotron Rerank VL 1B v2(0.6 GB vs 7.7 GB)

Llama Nemotron Rerank VL 1B v2 requires substantially less memory to run at scale, fitting on more accessible GPU tiers.

Hardware Sizing & Model Compression

LLM Hardware Sizing & Quantization Sizing

Simulate weight compression levels and dynamic KV-cache expansion to verify whether these models fit on your local hardware or cloud GPU cluster.

Quantization Level

Active Precision

4 bits / parameter

VRAM Reduction

-72.5% vs FP16

Benchmark Retention

96–98% (Sweet Spot)

Context Simulator

Total VRAM Footprint @ 8k Context (INT4 (GGUF / AWQ))

Model 1Open Weights

Llama Nemotron Rerank VL 1B v2

0.6GB

Weights: ~0.6 GBKV Cache: ~0 GB+15% CUDA Buffer

Total Params:

~1B

Active Compute:

Dense

Attention Scheme:

GQA (Grouped-Query)

Max Context:

8k tokens

Target Hardware: 1x RTX 4070 / 4080 (16GB) or Mac (16GB Unified)

Model 2Open Weights

Trendyol Asure 12B

7.7GB

Weights: ~6.6 GBKV Cache: ~0.1 GB+15% CUDA Buffer

Total Params:

12B

Active Compute:

Dense

Attention Scheme:

GQA (Grouped-Query)

Max Context:

131k tokens

Target Hardware: 1x RTX 4070 / 4080 (16GB) or Mac (16GB Unified)

GPU & Hardware Tier Compatibility Matrix

Hardware Configuration	Available VRAM	Llama Nemotron Rerank VL 1B v2	Trendyol Asure 12B
16 GB VRAM RTX 4070 / 4080 (16GB), Mac M-Series (16GB)	16 GB	Optimal	Optimal
24 GB VRAM 1x RTX 3090 / 4090 (24GB), Mac M-Series (32GB)	24 GB	Optimal	Optimal
48 GB VRAM 2x RTX 4090 (TP=2), 1x L40S, Mac M-Series (64GB)	48 GB	Optimal	Optimal
80 GB VRAM 1x NVIDIA A100 / H100 (80GB), Mac Studio (128GB)	80 GB	Optimal	Optimal
160 GB Node 2x H100 (TP=2), 4x L40S, Mac Studio (192GB)	160 GB	Optimal	Optimal
Multi-Node Cluster 4x–8x H100 Datacenter Cluster	320 GB	Optimal	Optimal

Inference Economics & Cost Simulator

Token Pricing & Scale Economics

Calculate estimated monthly cloud API spend and evaluate the breakeven point vs self-hosted GPU hardware.

Monthly Queries: 100,000

10k500k1M

Avg Prompt Tokens: 1000

Avg Output Tokens: 300

Pricing Metric	Llama Nemotron Rerank VL 1B v2	Trendyol Asure 12B
Input Cost (/1M tokens)	Free / Open	Free / Open
Cached Input (/1M tokens)	—	—
Output Cost (/1M tokens)	Free / Open	Free / Open
Simulated Monthly Bill (100,000 calls)	$0 API Cost Open Weights	$0 API Cost Open Weights
Self-Hosted Breakeven vs $864/mo GPU	Self-Hostable Day 1	Self-Hostable Day 1

Architecture Matrix

Feature	Llama Nemotron Rerank VL 1B v2	Trendyol Asure 12B
Release Date	3/31/2026	2/19/2026
Routing / MoE	Dense	Dense
Attention Mechanism	Standard / GQA	Standard / GQA
Source Type	Open Weights (Open)	Open Weights (Gemma)

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