Qwen1.5 MoE A2.7B — Hardware Requirements & GPU Compatibility
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Qwen1.5 MoE A2.7B is a Mixture of Experts (MoE) model from Alibaba Cloud's Qwen 1.5 generation, with 14.3 billion total parameters but only 2.7 billion active parameters per forward pass. The MoE architecture allows it to deliver performance closer to dense 7B models while requiring less compute during inference, as only a subset of expert layers are activated for each token. The model supports a 32K token context window and requires VRAM proportional to its total parameter count for loading, despite lower compute cost per token. It is an interesting architectural variant for users exploring efficient inference and MoE models locally. Released under a custom Qwen license.
181.8K downloads
225 likesSpecifications
- Publisher
- Alibaba
- Family
- Qwen
- Parameters
- 14.3B
- Architecture
- Qwen2MoeForCausalLM
- Context Length
- 8,192 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2024-02-29
- License
- Other
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HuggingFace
How Much VRAM Does Qwen1.5 MoE A2.7B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 6.8 GB | 8.0 GB | 6.08 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 7.7 GB | 8.9 GB | 6.98 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 9.3 GB | 10.5 GB | 8.59 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 10.9 GB | 12.1 GB | 10.20 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 12.5 GB | 13.7 GB | 11.81 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 15.0 GB | 16.2 GB | 14.32 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 29.3 GB | 30.5 GB | 28.63 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 Qwen1.5 MoE A2.7B?
Q4_K_M · 9.3 GBQwen1.5 MoE A2.7B (Q4_K_M) requires 9.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 13+ GB is recommended. Using the full 8K context window can add up to 1.2 GB, bringing total usage to 10.5 GB. 39 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3080 Ti.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Qwen1.5 MoE A2.7B?
Q4_K_M · 9.3 GB49 devices with unified memory can run Qwen1.5 MoE A2.7B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, iPad Pro M5 13" (16 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does Qwen1.5 MoE A2.7B need?
Qwen1.5 MoE A2.7B requires 9.3 GB of VRAM at Q4_K_M, or 29.3 GB at BF16. Full 8K context adds up to 1.2 GB (10.5 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 14.3B × 4.8 bits ÷ 8 = 8.6 GB
KV Cache + Overhead ≈ 0.7 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 1.9 GB (at full 8K context)
VRAM usage by quantization
Q4_K_M9.3 GBQ4_K_M + full context10.5 GB- Can NVIDIA GeForce RTX 4090 run Qwen1.5 MoE A2.7B?
Yes, at Q8_0 (15.0 GB) or lower. Higher quantizations like BF16 (29.3 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Qwen1.5 MoE A2.7B?
For Qwen1.5 MoE A2.7B, Q4_K_M (9.3 GB) offers the best balance of quality and VRAM usage. Q5_K_M (10.9 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 6.8 GB.
VRAM requirement by quantization
Q2_K6.8 GBQ4_K_M ★9.3 GBQ5_K_M10.9 GBQ6_K12.5 GBQ8_015.0 GBBF1629.3 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen1.5 MoE A2.7B on a Mac?
Qwen1.5 MoE A2.7B requires at least 6.8 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 Qwen1.5 MoE A2.7B locally?
Yes — Qwen1.5 MoE A2.7B can run locally on consumer hardware. At Q4_K_M quantization it needs 9.3 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen1.5 MoE A2.7B?
At Q4_K_M, Qwen1.5 MoE A2.7B can reach ~474 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~71 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 ÷ 9.3 × 0.65 = ~560 tok/s
Estimated speed at Q4_K_M (9.3 GB)
~560 tok/s~71 tok/s~560 tok/s~474 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Qwen1.5 MoE A2.7B?
At Q4_K_M, the download is about 8.59 GB. The full-precision BF16 version is 28.63 GB. The smallest option (Q2_K) is 6.08 GB.
- Which GPUs can run Qwen1.5 MoE A2.7B?
39 consumer GPUs can run Qwen1.5 MoE A2.7B at Q4_K_M (9.3 GB). Top options include AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 6900 XT, AMD Radeon RX 6700 XT. 26 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Qwen1.5 MoE A2.7B?
52 devices with unified memory can run Qwen1.5 MoE A2.7B at Q4_K_M (9.3 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, 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.