4× NVIDIA RTX 3090 24 GB

Hardware

GPU

NVIDIA RTX 3090 24 GB

GPU count

4

VRAM per GPU

24.0 GB advertised

Aggregate VRAM

96.0 GB advertised

System RAM

252 GB

CPU

AMD EPYC 7502 32-Core Processor

Storage

1280 GB SSD

Network

353 down · 299.1 up

AI memory capability

Default catalog below is Q4 at 8K. Statuses reuse Phase 2 (1 GPU) or Phase 8 (2–4 identical GPUs). GPU counts above 4 are not modeled. Public wording is memory-capable, not serving throughput.

Memory-capable at Q4/8K: 54 published models

• Qwen2.5 0.5B Instruct
• Llama 3.2 1B Instruct
• Qwen2.5 1.5B Instruct
• Qwen2.5-Coder 1.5B Instruct
• SmolLM2 1.7B Instruct
• Gemma 2 2B Instruct
• Qwen2.5 3B Instruct
• Llama 3.2 3B Instruct
• Phi-3.5 Mini Instruct
• Phi-3 Mini 128K Instruct
• Phi-4 Mini Instruct
• Code Llama 7B Instruct
• StarCoder2 7B
• Falcon 7B Instruct
• OpenChat 3.5 7B
• Mistral 7B Instruct v0.3
• Falcon 3 7B Instruct
• DeepSeek R1 Distill Qwen 7B
• Qwen2.5 7B Instruct
• Qwen2.5-Coder 7B Instruct
• Qwen2 7B Instruct
• DeepSeek R1 Distill Llama 8B
• Hermes 3 Llama 3.1 8B
• Llama 3.1 8B Instruct

Serving performance

Measured vLLM serving results for this GPU identity (4× NVIDIA RTX 3090 24 GB), not a specific rental host. Tensor parallel size is stored explicitly and is not assumed equal to GPU count. Request rate is separate from concurrency; this corpus is closed-loop / max-concurrency.

No measured vLLM serving rows map to this GPU count yet.

Current rental offers