UQ Research Computing Centre

Which Bunya GPU for My Open-Weight LLM?

Find the right GPU for inference, fine-tuning, or training open-weight language models on Bunya.
Covers Llama, Qwen, DeepSeek, Gemma, Mistral, GLM, Phi, and more.

Quick Reference — Model Sizes & Minimum GPU Memory

Approximate VRAM for inference (model weights + KV cache overhead). Fine-tuning requires significantly more — see the picker above. MoE models must load all parameters even though only a subset are active per token.

Model FamilyVariantParamsFP16INT8INT4Bunya GPU (inference, FP16)
Llama 3.1/3.38B8B~18 GB~10 GB~5 GBA100 MIG 20GB+, MI210
70B70B~168 GB~85 GB~44 GBMI300x (192GB) or 3× H100
405B405B~970 GB~485 GB~245 GBMulti-node (H100 SXM5 or MI300x)
Llama 4Scout (MoE)109B (17B active)~260 GB~130 GB~66 GB2× MI300x or 4× H100
Maverick (MoE)400B (17B active)~960 GB~480 GB~240 GBMulti-node (H100 SXM5 or MI300x)
Qwen 3 / 3.57-8B7-8B~18 GB~10 GB~5 GBA100 MIG 20GB+, MI210
27-32B27-32B~70 GB~36 GB~18 GBH100 (80GB) or MI210 (64GB, INT8)
72B72B~173 GB~87 GB~44 GBMI300x or 3× H100
235B (MoE, 22B active)235B~564 GB~282 GB~141 GB4× H100 SXM5 or 2× MI300x (INT8)
DeepSeekR1-distill 7/8B7-8B~18 GB~10 GB~5 GBA100 MIG 20GB+, MI210
R1-distill 14B14B~34 GB~17 GB~9 GBA100 MIG 40GB, MI210 (64GB)
R1-distill 70B70B~168 GB~85 GB~44 GBMI300x or 3× H100
V3.2 / R1 (MoE)671-685B (37B active)~1.6 TB~820 GB~410 GBMulti-node MI300x cluster
Gemma2/3 9B9B~22 GB~11 GB~6 GBA100 MIG 40GB, MI210
2/3 27B27B~65 GB~33 GB~17 GBH100 (80GB) or MI210 (64GB, INT8)
Gemma 427B (MoE)27B (14B active)~65 GB~33 GB~17 GBH100 (80GB) or MI210 (64GB, INT8)
Mistral7B / Nemo 12B7-12B~18-29 GB~9-15 GB~5-8 GBA100 MIG 20-40GB, MI210
Small 3 (24B)24B~58 GB~29 GB~15 GBMI210 (64GB) or H100 (80GB)
Large 2 (123B)123B~295 GB~148 GB~74 GB2× MI300x or 4× H100
Phi3/4-mini (3-4B)3-4B~10 GB~5 GB~3 GBA100 MIG 10GB
4 (14B)14B~34 GB~17 GB~9 GBA100 MIG 40GB, MI210 (64GB)
GLM4.7 (355B)355B~852 GB~426 GB~213 GBMulti-node MI300x or H100 SXM5
5 (744B)744B~1.8 TB~893 GB~447 GBMulti-node MI300x cluster
Need help? Contact the RCC support team at rcc-support@uq.edu.au. Full documentation: github.com/UQ-RCC/hpc-docs