Qwen 3.6 27B vs Gemma 4 31B
Side-by-side VRAM requirements, benchmark scores, and GPU compatibility for local AI inference.
Quick verdict
Qwen 3.6 27B is more hardware-efficient — it needs 16.9 GB at Q4_K_M vs 21.0 GB for Gemma 4 31B, fitting on 61 GPUs natively.
VRAM at each quantization (8k context)
| Quant | Qwen 3.6 27B | Gemma 4 31B | Diff |
|---|---|---|---|
| FP16 | 62.3 GB | 73.0 GB | -15% |
| Q8 | 32.0 GB | 38.3 GB | -16% |
| Q6_K | 24.5 GB | 29.6 GB | -17% |
| Q5_K_M | 20.7 GB | 25.3 GB | -18% |
| Q4_K_M | 16.9 GB | 21.0 GB | -19% |
| Q3_K_M | 13.9 GB | 17.5 GB | -21% |
| Q2_K | 10.9 GB | 14.0 GB | -22% |
Diff is Qwen 3.6 27B relative to Gemma 4 31B. Green = lower VRAM (fits more GPUs).
Model specifications
| Spec | Qwen 3.6 27B | Gemma 4 31B |
|---|---|---|
| Org | Alibaba | |
| Parameters | 27B | 31B |
| Architecture | Dense | Dense |
| Context | 256k tokens | 250k tokens |
| Modalities | text, vision | text, vision |
| License | Apache 2.0 | Apache 2.0 |
| Commercial | Yes | Yes |
| Released | 2026-04-01 | 2026-04-02 |
| GPUs (native) | 61 / 67 | 50 / 67 |
GPUs that run only Qwen 3.6 27B(11)
- NVIDIA RTX 4070 Ti12 GB
- NVIDIA RTX 407012 GB
- NVIDIA RTX 3060 12GB12 GB
- Apple M5 (16GB)16 GB
- Apple M4 (16GB)16 GB
- Apple M3 Pro (18GB)18 GB
- Apple M3 (16GB)16 GB
- Apple M2 Pro (16GB)16 GB
- Apple M2 (16GB)16 GB
- Apple M1 Pro (16GB)16 GB
- +1 more
GPUs that run only Gemma 4 31B(0)
Every GPU that runs Gemma 4 31B also runs Qwen 3.6 27B.
GPUs that run both natively(50)
- NVIDIA RTX 509032 GB
- NVIDIA RTX 409024 GB
- NVIDIA RTX 408016 GB
- NVIDIA RTX 4060 Ti 16GB16 GB
- NVIDIA RTX 309024 GB
- NVIDIA RTX 3090 Ti24 GB
- NVIDIA H100 80GB80 GB
- NVIDIA A100 80GB80 GB
- NVIDIA A100 40GB40 GB
- NVIDIA L40S48 GB
- NVIDIA RTX A600048 GB
- NVIDIA RTX 6000 Ada48 GB
- +38 more GPUs run both
Which should you use?
Choose Qwen 3.6 27B if:
- • You have limited VRAM — it's a smaller model needing 16.9 GB vs 21.0 GB
- • Long context matters — it supports 256k tokens vs 250k
- • You need chain-of-thought reasoning
Choose Gemma 4 31B if:
- • You want maximum capability and have a 21 GB+ GPU
Frequently asked questions
- Which is better, Qwen 3.6 27B or Gemma 4 31B?
- Qwen 3.6 27B has 27B parameters vs 31B for Gemma 4 31B, so Gemma 4 31B is the larger model. Qwen 3.6 27B is more hardware-efficient, needing 16.9 GB at Q4_K_M vs 21.0 GB. Qwen 3.6 27B runs on more GPUs natively (61 vs 50).
- How much VRAM does Qwen 3.6 27B need vs Gemma 4 31B?
- At Q4_K_M quantization with 8k context, Qwen 3.6 27B needs approximately 16.9 GB of VRAM, while Gemma 4 31B needs 21.0 GB. At FP16, Qwen 3.6 27B requires 62.3 GB vs 73.0 GB for Gemma 4 31B.
- Can you run Qwen 3.6 27B on the same GPUs as Gemma 4 31B?
- Yes, 50 GPUs can run both natively in VRAM, including NVIDIA RTX 5090, NVIDIA RTX 4090, NVIDIA RTX 4080. However, 11 GPUs can run Qwen 3.6 27B but not Gemma 4 31B, and no GPU can run Gemma 4 31B without also fitting Qwen 3.6 27B.
- What is the difference between Qwen 3.6 27B and Gemma 4 31B?
- Qwen 3.6 27B has 27B parameters (dense) with a 256k context window. Gemma 4 31B has 31B parameters (dense) with a 250k context window.
- Which model fits in 24 GB of VRAM, Qwen 3.6 27B or Gemma 4 31B?
- Both fit in 24 GB of VRAM at Q4_K_M — Qwen 3.6 27B needs 16.9 GB and Gemma 4 31B needs 21.0 GB.