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Qwen3.8 2.4T A95B vs Ornith 1.0 397B
Side-by-side technical showdown between Qwen3.8 2.4T A95B and Ornith 1.0 397B on TheModelverse. Compare verified LLM benchmark scores, quantization compression, local GPU hardware sizing, and API inference pricing.
Executive Showdown & Winner Breakdown
Who Wins Where: Qwen3.8 2.4T A95B vs Ornith 1.0 397B
Verified across benchmarks, pricing & local VRAM footprintVRAM Efficiency (INT4)
193 GB lighterLeader:Qwen3.8 2.4T A95B(60.7 GB vs 253.8 GB)
Qwen3.8 2.4T A95B 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
Qwen3.8 2.4T A95B
60.7GB
Weights: ~52.3 GBKV Cache: ~0.6 GB+15% CUDA Buffer
Total Params:
95B
Active Compute:
Dense
Attention Scheme:
GQA (Grouped-Query)
Max Context:
262k tokens
Target Hardware: 1x NVIDIA A100 / H100 (80GB)
Model 2Open Weights
Ornith 1.0 397B
253.8GB
Weights: ~218.4 GBKV Cache: ~2.4 GB+15% CUDA Buffer
Total Params:
397B
Active Compute:
Dense
Attention Scheme:
GQA (Grouped-Query)
Max Context:
262k tokens
Target Hardware: 4x–8x H100 Cluster with Tensor Parallelism (TP=4)
GPU & Hardware Tier Compatibility Matrix
| Hardware Configuration | Available VRAM | Qwen3.8 2.4T A95B | Ornith 1.0 397B |
|---|---|---|---|
16 GB VRAM RTX 4070 / 4080 (16GB), Mac M-Series (16GB) | 16 GB | ✕ OOM | ✕ OOM |
24 GB VRAM 1x RTX 3090 / 4090 (24GB), Mac M-Series (32GB) | 24 GB | ✕ OOM | ✕ OOM |
48 GB VRAM 2x RTX 4090 (TP=2), 1x L40S, Mac M-Series (64GB) | 48 GB | ✕ OOM | ✕ OOM |
80 GB VRAM 1x NVIDIA A100 / H100 (80GB), Mac Studio (128GB) | 80 GB | Optimal | ✕ OOM |
160 GB Node 2x H100 (TP=2), 4x L40S, Mac Studio (192GB) | 160 GB | Optimal | ✕ OOM |
Multi-Node Cluster 4x–8x H100 Datacenter Cluster | 320 GB | Optimal | Optimal |
LLM Benchmark Database
LLM Benchmark Showdown & Head-to-Head Deltas
Normalized evaluation scores across code generation, advanced reasoning, mathematics, and multidisciplinary exams.
SWE-bench (Coding)Score % / Points
Qwen3.8 2.4T A95B
—
Ornith 1.0 397B
82.4
HumanEval (Python Code)Score % / Points
Qwen3.8 2.4T A95B
—
Ornith 1.0 397B
77.1
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 | Qwen3.8 2.4T A95B | Ornith 1.0 397B |
|---|---|---|
| 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 | Qwen3.8 2.4T A95B | Ornith 1.0 397B |
|---|---|---|
| Release Date | 8/12/2026 | 6/25/2026 |
| Routing / MoE | Dense | Dense |
| Attention Mechanism | Standard / GQA | Standard / GQA |
| Source Type | Open Weights (qwen3.8-max) | Open Weights (MIT) |
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