Amadeus Verbo FI Qwen2.5 32B PT BR Instruct — Hardware Requirements & GPU Compatibility
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Amadeus Verbo FI Qwen2.5 32B PT BR Instruct is a 32.8B-parameter open language model from amadeusai in the Qwen 2.5 family. It supports a context window of up to 32,768 tokens. At Q4_K_M it needs about 20.50 GB of VRAM — see which GPUs and Macs can run it below.
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
- 2025-03-29
- 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 |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 14.8 GB | 22.8 GB | 13.92 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 16.8 GB | 24.9 GB | 15.97 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 20.5 GB | 28.6 GB | 19.66 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 24.2 GB | 32.2 GB | 23.34 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 27.9 GB | 35.9 GB | 27.03 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 33.6 GB | 41.6 GB | 32.76 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 66.4 GB | 74.4 GB | 65.53 GB | Brain floating point 16 — preferred for training |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run Amadeus Verbo FI Qwen2.5 32B PT BR Instruct?
Q4_K_M · 20.5 GBAmadeus Verbo FI Qwen2.5 32B PT BR Instruct (Q4_K_M) requires 20.5 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 27+ GB is recommended. Using the full 33K context window can add up to 8.1 GB, bringing total usage to 28.6 GB. 7 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroom
Which Devices Can Run Amadeus Verbo FI Qwen2.5 32B PT BR Instruct?
Q4_K_M · 20.5 GB41 devices with unified memory can run Amadeus Verbo FI Qwen2.5 32B PT BR Instruct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightRelated 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 20.5 GB of VRAM at Q4_K_M, or 66.4 GB at BF16. Full 33K context adds up to 8.1 GB (28.6 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 32.8B × 4.8 bits ÷ 8 = 19.7 GB
KV Cache + Overhead ≈ 0.8 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 8.9 GB (at full 33K context)
VRAM usage by quantization
Q4_K_M20.5 GBQ4_K_M + full context28.6 GB- Can NVIDIA GeForce RTX 4090 run Amadeus Verbo FI Qwen2.5 32B PT BR Instruct?
Yes, at Q4_K_M (20.5 GB) or lower. Higher quantizations like Q5_K_M (24.2 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Amadeus Verbo FI Qwen2.5 32B PT BR Instruct?
For Amadeus Verbo FI Qwen2.5 32B PT BR Instruct, Q4_K_M (20.5 GB) offers the best balance of quality and VRAM usage. Q5_K_M (24.2 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 14.8 GB.
VRAM requirement by quantization
Q2_K14.8 GBQ4_K_M ★20.5 GBQ5_K_M24.2 GBQ6_K27.9 GBQ8_033.6 GBBF1666.4 GB★ Recommended — best balance of quality and VRAM usage.
- 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 14.8 GB at Q2_K, 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 Q4_K_M quantization it needs 20.5 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 Q4_K_M, Amadeus Verbo FI Qwen2.5 32B PT BR Instruct can reach ~215 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~32 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 20.5 × 0.65 = ~254 tok/s
Estimated speed at Q4_K_M (20.5 GB)
~254 tok/s~32 tok/s~254 tok/s~215 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 Q4_K_M, the download is about 19.66 GB. The full-precision BF16 version is 65.53 GB. The smallest option (Q2_K) is 13.92 GB.
- Which GPUs can run Amadeus Verbo FI Qwen2.5 32B PT BR Instruct?
7 consumer GPUs can run Amadeus Verbo FI Qwen2.5 32B PT BR Instruct at Q4_K_M (20.5 GB). Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090. 1 GPU have plenty of headroom for comfortable inference.
- Which devices can run Amadeus Verbo FI Qwen2.5 32B PT BR Instruct?
41 devices with unified memory can run Amadeus Verbo FI Qwen2.5 32B PT BR Instruct at Q4_K_M (20.5 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.