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Qwen3 VL 235B A22B Instruct vs Llama Nemotron Rerank VL 1B v2
Side-by-side technical showdown between Qwen3 VL 235B A22B Instruct and Llama Nemotron Rerank VL 1B v2 on TheModelverse. Compare verified LLM benchmark scores, quantization compression, local GPU hardware sizing, and API inference pricing.
Executive Showdown & Winner Breakdown
Who Wins Where: Qwen3 VL 235B A22B Instruct vs Llama Nemotron Rerank VL 1B v2
Verified across benchmarks, pricing & local VRAM footprintVRAM Efficiency (INT4)
150 GB lighterLeader:Llama Nemotron Rerank VL 1B v2(150.3 GB vs 0.6 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
Qwen3 VL 235B A22B Instruct
150.3GB
Weights: ~129.3 GBKV Cache: ~1.4 GB+15% CUDA Buffer
Total Params:
235B
Active Compute:
Dense
Attention Scheme:
GQA (Grouped-Query)
Max Context:
131k tokens
Target Hardware: 4x–8x H100 Cluster with Tensor Parallelism (TP=4)
Model 2Open 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)
GPU & Hardware Tier Compatibility Matrix
| Hardware Configuration | Available VRAM | Qwen3 VL 235B A22B Instruct | Llama Nemotron Rerank VL 1B v2 |
|---|---|---|---|
16 GB VRAM RTX 4070 / 4080 (16GB), Mac M-Series (16GB) | 16 GB | ✕ OOM | Optimal |
24 GB VRAM 1x RTX 3090 / 4090 (24GB), Mac M-Series (32GB) | 24 GB | ✕ OOM | Optimal |
48 GB VRAM 2x RTX 4090 (TP=2), 1x L40S, Mac M-Series (64GB) | 48 GB | ✕ OOM | Optimal |
80 GB VRAM 1x NVIDIA A100 / H100 (80GB), Mac Studio (128GB) | 80 GB | ✕ OOM | Optimal |
160 GB Node 2x H100 (TP=2), 4x L40S, Mac Studio (192GB) | 160 GB | Tight (Low Ctx) | 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.
10k500k1M
| Pricing Metric | Qwen3 VL 235B A22B Instruct | Llama Nemotron Rerank VL 1B v2 |
|---|---|---|
| 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 | Qwen3 VL 235B A22B Instruct | Llama Nemotron Rerank VL 1B v2 |
|---|---|---|
| Release Date | 9/23/2025 | 3/31/2026 |
| Routing / MoE | Dense | Dense |
| Attention Mechanism | Standard / GQA | Standard / GQA |
| Source Type | Open Weights (Open) | Open Weights (Open) |
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