Qwen Qwen1.5 MoE A2.7B GGUF — Hardware Requirements & GPU Compatibility
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Qwen Qwen1.5 MoE A2.7B GGUF is a 2.7B-parameter open language model from RichardErkhov in the Qwen family. At Q4_K_M it needs about 1.78 GB of VRAM — see which GPUs and Macs can run it below.
222 downloadsSpecifications
- Publisher
- RichardErkhov
- Family
- Qwen
- Parameters
- 2.7B
Get Started
HuggingFace
How Much VRAM Does Qwen Qwen1.5 MoE A2.7B GGUF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 1.3 GB | — | 1.15 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 1.3 GB | — | 1.18 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 1.4 GB | — | 1.32 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 1.5 GB | — | 1.35 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 1.8 GB | — | 1.62 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 2.1 GB | — | 1.92 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 2.5 GB | — | 2.23 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 3.0 GB | — | 2.70 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Qwen Qwen1.5 MoE A2.7B GGUF?
Q4_K_M · 1.8 GBQwen Qwen1.5 MoE A2.7B GGUF (Q4_K_M) requires 1.8 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 3+ GB is recommended. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Qwen Qwen1.5 MoE A2.7B GGUF?
Q4_K_M · 1.8 GB33 devices with unified memory can run Qwen Qwen1.5 MoE A2.7B GGUF, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Qwen Qwen1.5 MoE A2.7B GGUF need?
Qwen Qwen1.5 MoE A2.7B GGUF requires 1.8 GB of VRAM at Q4_K_M, or 3.0 GB at Q8_0.
VRAM = Weights + KV Cache + Overhead
Weights = 2.7B × 4.8 bits ÷ 8 = 1.6 GB
KV Cache + Overhead ≈ 0.2 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M1.8 GB- What's the best quantization for Qwen Qwen1.5 MoE A2.7B GGUF?
For Qwen Qwen1.5 MoE A2.7B GGUF, Q4_K_M (1.8 GB) offers the best balance of quality and VRAM usage. Q5_0 (1.9 GB) provides better quality if you have the VRAM. The smallest option is IQ3_XS at 1.2 GB.
VRAM requirement by quantization
IQ3_XS1.2 GBQ3_K_M1.4 GBQ4_K_S1.7 GBQ4_K_M ★1.8 GBQ5_12.0 GBQ8_03.0 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen Qwen1.5 MoE A2.7B GGUF on a Mac?
Qwen Qwen1.5 MoE A2.7B GGUF requires at least 1.2 GB at IQ3_XS, 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 Qwen Qwen1.5 MoE A2.7B GGUF locally?
Yes — Qwen Qwen1.5 MoE A2.7B GGUF can run locally on consumer hardware. At Q4_K_M quantization it needs 1.8 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen Qwen1.5 MoE A2.7B GGUF?
At Q4_K_M, Qwen Qwen1.5 MoE A2.7B GGUF can reach ~1638 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~368 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 ÷ 1.8 × 0.55 = ~1638 tok/s
Estimated speed at Q4_K_M (1.8 GB)
~1638 tok/s~368 tok/s~1224 tok/s~1013 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Qwen Qwen1.5 MoE A2.7B GGUF?
At Q4_K_M, the download is about 1.62 GB. The full-precision Q8_0 version is 2.70 GB. The smallest option (IQ3_XS) is 1.11 GB.
- Which GPUs can run Qwen Qwen1.5 MoE A2.7B GGUF?
35 consumer GPUs can run Qwen Qwen1.5 MoE A2.7B GGUF at Q4_K_M (1.8 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 Qwen Qwen1.5 MoE A2.7B GGUF?
33 devices with unified memory can run Qwen Qwen1.5 MoE A2.7B GGUF at Q4_K_M (1.8 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.