Qwen 3.6 27B vs Gemma 3 27B Instruct

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 19.0 GB at its Q4_K_M build vs 20.1 GB for Gemma 3 27B Instruct's Q4_K_M, fitting on 84 GPUs natively.

VRAM at each quantization (8k context)

FP32
Qwen 3.6 27B
121.6 GB
Gemma 3 27B Instruct
122.7 GB
BF16
Qwen 3.6 27B
61.1 GB
Gemma 3 27B Instruct
62.2 GB
FP16
Qwen 3.6 27B
61.1 GB
Gemma 3 27B Instruct
62.2 GB
Q8_0
Qwen 3.6 27B
32.8 GB
Gemma 3 27B Instruct
33.9 GB
Q6_K
Qwen 3.6 27B
25.4 GB
Gemma 3 27B Instruct
26.6 GB
Q5_K_M
Qwen 3.6 27B
22.1 GB
Gemma 3 27B Instruct
23.3 GB
Q4_K_M
Qwen 3.6 27B
19.0 GB
Gemma 3 27B Instruct
20.1 GB
Q3_K_M
Qwen 3.6 27B
15.2 GB
Gemma 3 27B Instruct
16.3 GB
Q2_K
Qwen 3.6 27B
12.1 GB
Gemma 3 27B Instruct
13.3 GB
NVFP4
Qwen 3.6 27B
15.7 GB
Gemma 3 27B Instruct
16.9 GB
QuantQwen 3.6 27BGemma 3 27B InstructDiff
FP32121.6 GB122.7 GB-1%
BF1661.1 GB62.2 GB-2%
FP1661.1 GB62.2 GB-2%
Q8_032.8 GB33.9 GB-3%
Q6_K25.4 GB26.6 GB-4%
Q5_K_M22.1 GB23.3 GB-5%
Q4_K_M19.0 GB20.1 GB-6%
Q3_K_M15.2 GB16.3 GB-7%
Q2_K12.1 GB13.3 GB-9%
NVFP415.7 GB16.9 GB-7%

Diff is Qwen 3.6 27B relative to Gemma 3 27B Instruct. Green = lower VRAM (fits more GPUs).

Model specifications

SpecQwen 3.6 27BGemma 3 27B Instruct
OrgAlibabaGoogle
Parameters27B27B
ArchitectureDenseDense
Context256k tokens128k tokens
Modalitiestext, vision, videotext, vision
LicenseApache 2.0Gemma
CommercialYesYes
Released2026-04-222025-03-12
GPUs (native)84 / 11984 / 119

Benchmark scores

BenchmarkQwen 3.6 27BGemma 3 27B Instruct
MMLU-Pro86.267.5
GPQA Diamond87.8N/A
LiveCodeBench83.9N/A
SWE-bench Verified77.2N/A
SWE-bench Pro53.5N/A

Green = higher score (better). N/A = not yet available. ~ = inherited from a base model, not independently reported for that release itself.

GPUs that run only Qwen 3.6 27B(0)

Every GPU that runs Qwen 3.6 27B also runs Gemma 3 27B Instruct.

GPUs that run only Gemma 3 27B Instruct(0)

Every GPU that runs Gemma 3 27B Instruct also runs Qwen 3.6 27B.

GPUs that run both natively(84)

Which should you use?

Choose Qwen 3.6 27B if:
  • Long context matters: it supports 256k tokens vs 128k
  • Benchmark quality matters: scores 86.2 vs 67.5 on MMLU-Pro
  • You're running coding tasks
  • You need chain-of-thought reasoning
  • It's the newer release (2026-04-22 vs 2025-03-12); check the benchmark table above for what actually improved
Choose Gemma 3 27B Instruct if:
  • No clear spec advantage over Qwen 3.6 27B, see the benchmark and VRAM tables above.

Frequently asked questions

Which is better, Qwen 3.6 27B or Gemma 3 27B Instruct?
Qwen 3.6 27B is more hardware-efficient, needing 19.0 GB at its Q4_K_M build vs 20.1 GB for Gemma 3 27B Instruct's Q4_K_M. On MMLU-Pro, Qwen 3.6 27B scores higher (86.2 vs 67.5).
How much VRAM does Qwen 3.6 27B need vs Gemma 3 27B Instruct?
At 8k context, Qwen 3.6 27B needs approximately 19.0 GB of VRAM at its Q4_K_M build, while Gemma 3 27B Instruct needs 20.1 GB at its Q4_K_M build. At the largest build each ships, Qwen 3.6 27B requires 61.1 GB (FP16) vs 62.2 GB (FP16) for Gemma 3 27B Instruct.
Can you run Qwen 3.6 27B on the same GPUs as Gemma 3 27B Instruct?
Yes, 84 GPUs can run both natively in VRAM, including NVIDIA RTX 5090, NVIDIA RTX 5080, NVIDIA RTX 5070 Ti. However, no GPU can run Qwen 3.6 27B without also fitting Gemma 3 27B Instruct, and no GPU can run Gemma 3 27B Instruct without also fitting Qwen 3.6 27B.
What is the difference between Qwen 3.6 27B and Gemma 3 27B Instruct?
Qwen 3.6 27B has 27B parameters (dense) with a 256k context window. Gemma 3 27B Instruct has 27B parameters (dense) with a 128k context window. Licensing differs: Qwen 3.6 27B is Apache 2.0 while Gemma 3 27B Instruct is Gemma.
Which model fits in 24 GB of VRAM, Qwen 3.6 27B or Gemma 3 27B Instruct?
Both fit in 24 GB of VRAM at their respective recommended builds: Qwen 3.6 27B (Q4_K_M) needs 19.0 GB and Gemma 3 27B Instruct (Q4_K_M) needs 20.1 GB.
Full Qwen 3.6 27B page →Full Gemma 3 27B Instruct page →Check your hardware →