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.
153.9K downloads
221 likesSpecifications
- Publisher
- Alibaba
- Family
- Qwen
- Parameters
- 14.3B
- Architecture
- Qwen2MoeForCausalLM
- Context Length
- 8,192 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2024-04-18
- License
- Other
Get Started
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 |
|---|---|---|---|---|---|
| BF16 | 16.00 | 29.3 GB | 30.5 GB | 28.63 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run Qwen1.5 MoE A2.7B?
BF16 · 29.3 GBQwen1.5 MoE A2.7B (BF16) requires 29.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 39+ GB is recommended. Using the full 8K context window can add up to 1.2 GB, bringing total usage to 30.5 GB. 1 GPU can run it, including NVIDIA GeForce RTX 5090.
All compatible consumer-level GPUs are running near their VRAM limit. You may also want to consider professional GPUs (e.g., NVIDIA A100, H100) which offer significantly more VRAM. For more headroom and better throughput, consider a multi-GPU configuration with tensor parallelism (supported by tools like vLLM, llama.cpp, or text-generation-inference).
Decent
— Enough VRAM, may be tightWhich Devices Can Run Qwen1.5 MoE A2.7B?
BF16 · 29.3 GB15 devices with unified memory can run Qwen1.5 MoE A2.7B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio M4 Max (36 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 29.3 GB of VRAM at BF16. Full 8K context adds up to 1.2 GB (30.5 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 14.3B × 16 bits ÷ 8 = 28.6 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
BF1629.3 GBBF16 + full context30.5 GB- Can I run Qwen1.5 MoE A2.7B on a Mac?
Qwen1.5 MoE A2.7B requires at least 29.3 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 Qwen1.5 MoE A2.7B locally?
Yes — Qwen1.5 MoE A2.7B can run locally on consumer hardware. At BF16 quantization it needs 29.3 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen1.5 MoE A2.7B?
At BF16, Qwen1.5 MoE A2.7B can reach ~99 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 ÷ 29.3 × 0.55 = ~99 tok/s
Estimated speed at BF16 (29.3 GB)
AMD Instinct MI300X~99 tok/sNVIDIA H100 SXM~74 tok/sAMD Instinct MI250X~61 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 BF16, the download is about 28.63 GB.