Qwen2.5 72B Instruct Abliterated 2x TIES v1.0 GGUF — Hardware Requirements & GPU Compatibility
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56 downloadsSpecifications
- Publisher
- mradermacher
- Family
- Qwen 2.5
- Parameters
- 72B
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How Much VRAM Does Qwen2.5 72B Instruct Abliterated 2x TIES v1.0 GGUF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 33.7 GB | — | 30.60 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 34.6 GB | — | 31.50 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 38.6 GB | — | 35.10 GB | 3-bit medium quantization |
| Q3_K_L | 4.10 | 40.6 GB | — | 36.90 GB | 3-bit large quantization |
| IQ4_XS | 4.30 | 42.6 GB | — | 38.70 GB | Importance-weighted 4-bit, compact |
| Q4_K_S | 4.50 | 44.5 GB | — | 40.50 GB | 4-bit small quantization |
| Q4_K_M | 4.80 | 47.5 GB | — | 43.20 GB | 4-bit medium quantization — most popular sweet spot |
Which GPUs Can Run Qwen2.5 72B Instruct Abliterated 2x TIES v1.0 GGUF?
Q4_K_M · 47.5 GBQwen2.5 72B Instruct Abliterated 2x TIES v1.0 GGUF (Q4_K_M) requires 47.5 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 62+ GB is recommended. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run Qwen2.5 72B Instruct Abliterated 2x TIES v1.0 GGUF?
Q4_K_M · 47.5 GB11 devices with unified memory can run Qwen2.5 72B Instruct Abliterated 2x TIES v1.0 GGUF, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio M4 Max (64 GB).
Runs great
— Plenty of headroom
Related Models
Frequently Asked Questions
- How much VRAM does Qwen2.5 72B Instruct Abliterated 2x TIES v1.0 GGUF need?
Qwen2.5 72B Instruct Abliterated 2x TIES v1.0 GGUF requires 47.5 GB of VRAM at Q4_K_M.
VRAM = Weights + KV Cache + Overhead
Weights = 72B × 4.8 bits ÷ 8 = 43.2 GB
KV Cache + Overhead ≈ 4.3 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M47.5 GB- Can NVIDIA GeForce RTX 5090 run Qwen2.5 72B Instruct Abliterated 2x TIES v1.0 GGUF?
No — Qwen2.5 72B Instruct Abliterated 2x TIES v1.0 GGUF requires at least 33.7 GB at Q2_K, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- What's the best quantization for Qwen2.5 72B Instruct Abliterated 2x TIES v1.0 GGUF?
For Qwen2.5 72B Instruct Abliterated 2x TIES v1.0 GGUF, Q4_K_M (47.5 GB) offers the best balance of quality and VRAM usage. The smallest option is Q2_K at 33.7 GB.
VRAM requirement by quantization
Q2_K33.7 GB~75%Q3_K_S34.6 GB~77%Q3_K_M38.6 GB~83%IQ4_XS42.6 GB~87%Q4_K_S44.5 GB~88%Q4_K_M ★47.5 GB~89%★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen2.5 72B Instruct Abliterated 2x TIES v1.0 GGUF on a Mac?
Qwen2.5 72B Instruct Abliterated 2x TIES v1.0 GGUF requires at least 33.7 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 Qwen2.5 72B Instruct Abliterated 2x TIES v1.0 GGUF locally?
Yes — Qwen2.5 72B Instruct Abliterated 2x TIES v1.0 GGUF can run locally on consumer hardware. At Q4_K_M quantization it needs 47.5 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen2.5 72B Instruct Abliterated 2x TIES v1.0 GGUF?
At Q4_K_M, Qwen2.5 72B Instruct Abliterated 2x TIES v1.0 GGUF can reach ~61 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 ÷ 47.5 × 0.55 = ~61 tok/s
Estimated speed at Q4_K_M (47.5 GB)
AMD Instinct MI300X~61 tok/sNVIDIA H100 SXM~46 tok/sAMD Instinct MI250X~38 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 72B Instruct Abliterated 2x TIES v1.0 GGUF?
At Q4_K_M, the download is about 43.20 GB. The smallest option (Q2_K) is 30.60 GB.
- Which GPUs can run Qwen2.5 72B Instruct Abliterated 2x TIES v1.0 GGUF?
No single consumer GPU has enough VRAM to run Qwen2.5 72B Instruct Abliterated 2x TIES v1.0 GGUF at Q4_K_M (47.5 GB). Multi-GPU or professional hardware is required.
- Which devices can run Qwen2.5 72B Instruct Abliterated 2x TIES v1.0 GGUF?
11 devices with unified memory can run Qwen2.5 72B Instruct Abliterated 2x TIES v1.0 GGUF at Q4_K_M (47.5 GB), including Mac Mini M4 Pro (48 GB), Mac Pro M2 Ultra (192 GB), Mac Studio M2 Ultra (192 GB), Mac Studio M4 Max (128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.