Qwen1.5 MoE A2.7B Chat — Hardware Requirements & GPU Compatibility
Chat
Qwen1.5 MoE A2.7B Chat is a 14.3B-parameter open language model from Alibaba in the Qwen family. It supports a context window of up to 32,768 tokens. At Q4_K_M it needs about 9.29 GB of VRAM — see which GPUs and Macs can run it below.
Runs great on a 24 GB GPU
Runs from 12 GB (Q4_K_M · 8K)Best on 24 GB (Q4_K_M · ~251 tok/s · 32K)Macs from 16 GB (Q4_K_M · ~58 tok/s)
Other
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Qwen1.5 MoE A2.7B Chat
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve Qwen/Qwen1.5-MoE-A2.7B-Chat --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Other ways
each opens step-by-stepRun in cloud
Fits on RTX 5060 Ti 16GB (3 GB headroom) · Q4_K_M
- Generation speed
- ~137 tok/s
- generation speed
- Cost per 1M output tokens
- $0.33
- per 1M output tokens
How Much VRAM Does Qwen1.5 MoE A2.7B Chat Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 6.8 GB | 12.8 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 32K context · ~306 tok/s | |
| 7.7 GB | 13.7 GB | 3-bit medium — 3-bit medium quantization | Fits: 32K context · ~284 tok/s | |
| 9.3 GB | 15.3 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 32K context · ~251 tok/s | |
| 10.9 GB | 16.9 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 32K context · ~225 tok/s | |
| 12.5 GB | 18.6 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 32K context · ~204 tok/s | |
| 15.0 GB | 21.1 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 32K context · ~178 tok/s | |
| 29.3 GB | 35.4 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Does not fit: needs 32.2 GB |
Selected Q4_K_M
4-bit medium quantization12.1GBat 16K context
- Weights
- 8.6 GB
- KV cache
- 3.2 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 11.9 GB free · ~251 tok/s
≥50 tok/s needs ≥144 GB/s · RTX 5090 ~355, RTX 3090 Ti ~251
Which Hardware Can Run Qwen1.5 MoE A2.7B Chat?
Q4_K_M · 9.3 GBQwen1.5 MoE A2.7B Chat (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 33K context window can add up to 6.0 GB, bringing total usage to 15.3 GB. 40 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3080 Ti. 150 devices with unified memory can run Qwen1.5 MoE A2.7B Chat, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Mini M2 Pro (16 GB).
16–18 GB
16–18 GB tier →fits · 3.9 GB free · up to 32K ctx
18 GPUs · 2 Mac classes · 2 device families
RTX 5080~243Simulate output on RTX 5080RTX 5070 Ti~232Simulate output on RTX 5070 TiRTX 4080 SUPER~201Simulate output on RTX 4080 SUPERM516 GB~48Simulate output on MacBook Air 13" M5 (16 GB)M3 Pro*18 GB~47Simulate output on MacBook Pro 14" M3 Pro (18 GB)iPad~47Simulate output on iPad Pro M5 13" (16 GB)Orin NX~36Simulate output on NVIDIA Jetson Orin NX 16GB20 GB
fits · 7.9 GB free · up to 32K ctx
1 GPU
RX 7900 XT~119Simulate output on RX 7900 XT32–40 GB
fits · 19.9 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~355Simulate output on RTX 5090M5 Max36 GB~98Simulate output on Mac Studio M5 Max (36 GB)M532 GB~48Simulate output on MacBook Air 13" M5 (32 GB)Orin~69Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~45Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Qwen1.5 MoE A2.7B Chat
Qwen family · 28 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Apriel 1.6 15B ThinkerServiceNow-AI | — | 13.2 GB | ~67 | same size |
| Phi 4 ReasoningMicrosoft | — | 12.5 GB | ~69 | same size |
| Phi 3 Medium 128k InstructMicrosoft | — | 12 GB | ~72 | same size |
| JiRackUltra 14BCMSManhattan | — | 12.4 GB | ~69 | same size |
| Nemotron Terminal 14BNVIDIA | — | 11.9 GB | ~69 | same size |
| Ministral 3 14B Reasoning 2512Mistral AI | — | 12 GB | ~72 | same size |
Specifications
- Publisher
- Alibaba
- Family
- Qwen
- Parameters
- 14.3B
- Active parameters
- 2.7B per token
- Architecture
- Qwen2MoeForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2024-03-14
- License
- Other
Frequently Asked Questions
How much VRAM does Qwen1.5 MoE A2.7B Chat need?
Qwen1.5 MoE A2.7B Chat requires 9.3 GB of VRAM at Q4_K_M, or 29.3 GB at BF16. Full 33K context adds up to 6.0 GB (15.3 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)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 6.7 GB (at full 33K context)
VRAM usage by quantization
Can NVIDIA GeForce RTX 4090 run Qwen1.5 MoE A2.7B Chat?
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 Chat?
For Qwen1.5 MoE A2.7B Chat, 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
★ Recommended — best balance of quality and VRAM usage.
Can I run Qwen1.5 MoE A2.7B Chat on a Mac?
Yes — Mac Mini M2 Pro (16 GB) and 133 other Macs can run Qwen1.5 MoE A2.7B Chat. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 9.3 GB of usable unified memory (RAM minus macOS overhead).
Can I run Qwen1.5 MoE A2.7B Chat locally?
Yes — Qwen1.5 MoE A2.7B Chat can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Qwen1.5 MoE A2.7B Chat?
At Q4_K_M, Qwen1.5 MoE A2.7B Chat can reach ~194 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~251 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = 1000 ÷ (active GB ÷ (bandwidth GB/s × efficiency) × 1000 + layers × routing ms)
Mixture-of-Experts: just the active experts get read for each token, along with a fixed routing cost per layer.
Example: NVIDIA B200 → 1.7 GB active ÷ (8000 × 0.65) = 0.336 ms, plus 24 layers × 0.055 ms = 1.320 ms, so 1000 ÷ 1.656 ms = ~604 tok/s
Estimated speed at Q4_K_M (9.3 GB)
Real-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 Chat?
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 Chat?
40 consumer GPUs can run Qwen1.5 MoE A2.7B Chat at Q4_K_M. 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 Chat?
150 devices with unified memory can run Qwen1.5 MoE A2.7B Chat at Q4_K_M (9.3 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (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.