Amadeus Verbo FI Qwen2.5 32B PT BR Instruct — Hardware Requirements & GPU Compatibility
Chat
1 downloads
1 likesSpecifications
- Publisher
- amadeusai
- Family
- Qwen 2.5
- Parameters
- 32.8B
- Architecture
- Qwen2ForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 152,064
- Release Date
- 2025-06-03
- License
- Apache 2.0
Get Started
How Much VRAM Does Amadeus Verbo FI Qwen2.5 32B PT BR Instruct Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 66.4 GB | 74.4 GB | 65.53 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run Amadeus Verbo FI Qwen2.5 32B PT BR Instruct?
BF16 · 66.4 GBAmadeus Verbo FI Qwen2.5 32B PT BR Instruct (BF16) requires 66.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 87+ GB is recommended. Using the full 33K context window can add up to 8.1 GB, bringing total usage to 74.4 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run Amadeus Verbo FI Qwen2.5 32B PT BR Instruct?
BF16 · 66.4 GB5 devices with unified memory can run Amadeus Verbo FI Qwen2.5 32B PT BR Instruct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Related Models
Frequently Asked Questions
- How much VRAM does Amadeus Verbo FI Qwen2.5 32B PT BR Instruct need?
Amadeus Verbo FI Qwen2.5 32B PT BR Instruct requires 66.4 GB of VRAM at BF16. Full 33K context adds up to 8.1 GB (74.4 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 32.8B × 16 bits ÷ 8 = 65.5 GB
KV Cache + Overhead ≈ 0.9 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 8.9 GB (at full 33K context)
VRAM usage by quantization
BF1666.4 GBBF16 + full context74.4 GB- Can NVIDIA GeForce RTX 5090 run Amadeus Verbo FI Qwen2.5 32B PT BR Instruct?
No — Amadeus Verbo FI Qwen2.5 32B PT BR Instruct requires at least 66.4 GB at BF16, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- Can I run Amadeus Verbo FI Qwen2.5 32B PT BR Instruct on a Mac?
Amadeus Verbo FI Qwen2.5 32B PT BR Instruct requires at least 66.4 GB at BF16, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run Amadeus Verbo FI Qwen2.5 32B PT BR Instruct locally?
Yes — Amadeus Verbo FI Qwen2.5 32B PT BR Instruct can run locally on consumer hardware. At BF16 quantization it needs 66.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Amadeus Verbo FI Qwen2.5 32B PT BR Instruct?
At BF16, Amadeus Verbo FI Qwen2.5 32B PT BR Instruct can reach ~44 tok/s on AMD Instinct MI300X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 66.4 × 0.55 = ~44 tok/s
Estimated speed at BF16 (66.4 GB)
AMD Instinct MI300X~44 tok/sNVIDIA H100 SXM~33 tok/sAMD Instinct MI250X~27 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Amadeus Verbo FI Qwen2.5 32B PT BR Instruct?
At BF16, the download is about 65.53 GB.
- Which GPUs can run Amadeus Verbo FI Qwen2.5 32B PT BR Instruct?
No single consumer GPU has enough VRAM to run Amadeus Verbo FI Qwen2.5 32B PT BR Instruct at BF16 (66.4 GB). Multi-GPU or professional hardware is required.
- Which devices can run Amadeus Verbo FI Qwen2.5 32B PT BR Instruct?
5 devices with unified memory can run Amadeus Verbo FI Qwen2.5 32B PT BR Instruct at BF16 (66.4 GB), including Mac Pro M2 Ultra (192 GB), Mac Studio M2 Ultra (192 GB), Mac Studio M4 Max (128 GB), NVIDIA DGX A100 640GB. Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.