Qwen2.5 Math 1.5B Bnb 4bit — Hardware Requirements & GPU Compatibility
ChatMath
2.5K downloads
3 likesSpecifications
- Publisher
- Unsloth
- Family
- Qwen 2.5
- Parameters
- 1.6B
- Architecture
- Qwen2ForCausalLM
- Context Length
- 4,096 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2024-11-12
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Qwen2.5 Math 1.5B Bnb 4bit Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 3.5 GB | 3.6 GB | 3.17 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run Qwen2.5 Math 1.5B Bnb 4bit?
BF16 · 3.5 GBQwen2.5 Math 1.5B Bnb 4bit (BF16) requires 3.5 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 5+ GB is recommended. Using the full 4K context window can add up to 0.1 GB, bringing total usage to 3.6 GB. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Qwen2.5 Math 1.5B Bnb 4bit?
BF16 · 3.5 GB33 devices with unified memory can run Qwen2.5 Math 1.5B Bnb 4bit, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Qwen2.5 Math 1.5B Bnb 4bit need?
Qwen2.5 Math 1.5B Bnb 4bit requires 3.5 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 1.6B × 16 bits ÷ 8 = 3.2 GB
KV Cache + Overhead ≈ 0.3 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 0.4 GB (at full 4K context)
VRAM usage by quantization
BF163.5 GBBF16 + full context3.6 GB- Can I run Qwen2.5 Math 1.5B Bnb 4bit on a Mac?
Qwen2.5 Math 1.5B Bnb 4bit requires at least 3.5 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 Qwen2.5 Math 1.5B Bnb 4bit locally?
Yes — Qwen2.5 Math 1.5B Bnb 4bit can run locally on consumer hardware. At BF16 quantization it needs 3.5 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen2.5 Math 1.5B Bnb 4bit?
At BF16, Qwen2.5 Math 1.5B Bnb 4bit can reach ~826 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~186 tok/s. 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 ÷ 3.5 × 0.55 = ~826 tok/s
Estimated speed at BF16 (3.5 GB)
AMD Instinct MI300X~826 tok/sNVIDIA GeForce RTX 4090~186 tok/sNVIDIA H100 SXM~617 tok/sAMD Instinct MI250X~511 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Qwen2.5 Math 1.5B Bnb 4bit?
At BF16, the download is about 3.17 GB.
- Which GPUs can run Qwen2.5 Math 1.5B Bnb 4bit?
35 consumer GPUs can run Qwen2.5 Math 1.5B Bnb 4bit at BF16 (3.5 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 35 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Qwen2.5 Math 1.5B Bnb 4bit?
33 devices with unified memory can run Qwen2.5 Math 1.5B Bnb 4bit at BF16 (3.5 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.