What is the difference between Qwen3.8 2.4T A95B and Ornith 1.5 35B A3B?

Qwen3.8 2.4T A95B (Alibaba Cloud) features 95B with a 262k context window, while Ornith 1.5 35B A3B (DeepReinforce) features 35B with a 262k context window.

Which is cheaper for production inference, Qwen3.8 2.4T A95B or Ornith 1.5 35B A3B?

Qwen3.8 2.4T A95B input pricing is Open / Free per 1M tokens vs Ornith 1.5 35B A3B at Open / Free per 1M tokens.

Can I run Qwen3.8 2.4T A95B or Ornith 1.5 35B A3B locally on private GPU hardware?

Qwen3.8 2.4T A95B is an open-weights model and can be hosted locally using Ollama or vLLM. Ornith 1.5 35B A3B is an open-weights model and can be hosted locally.

Back to LLM Benchmark & Hardware Sizing Engine

Alibaba CloudvsDeepReinforce

Qwen3.8 2.4T A95B vs Ornith 1.5 35B A3B

Side-by-side technical showdown between Qwen3.8 2.4T A95B and Ornith 1.5 35B A3B 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 1.5 35B A3B

Verified across benchmarks, pricing & local VRAM footprint

VRAM Efficiency (INT4)

38 GB lighter

Leader:Ornith 1.5 35B A3B(60.7 GB vs 22.4 GB)

Ornith 1.5 35B A3B 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 1.5 35B A3B

22.4GB

Weights: ~19.3 GBKV Cache: ~0.2 GB+15% CUDA Buffer

Total Params:

35B

Active Compute:

Dense

Attention Scheme:

GQA (Grouped-Query)

Max Context:

262k tokens

Target Hardware: 2x RTX 4090 (TP=2) or 1x L40S (48GB) (TP=2)

GPU & Hardware Tier Compatibility Matrix

Hardware Configuration	Available VRAM	Qwen3.8 2.4T A95B	Ornith 1.5 35B A3B
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	Tight (Low Ctx)
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	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	Qwen3.8 2.4T A95B	Ornith 1.5 35B A3B
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 1.5 35B A3B
Release Date	8/12/2026	8/18/2026
Routing / MoE	Dense	Dense
Attention Mechanism	Standard / GQA	Standard / GQA
Source Type	Open Weights (qwen3.8-max)	Open Weights (MIT)

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