| Model & Quantization | VRAM Demand | NVIDIA GeForce RTX 3090 | AMD Radeon RX 7900 XTX |
|---|---|---|---|
| Google Gemma 4 31B (Q4_K_M) | 21.44 GB | Fits Clean (+2.56 GB, CUDA) | Fits Clean (+2.56 GB, ROCm) |
| Alibaba Qwen3-Coder 30B-A3B (Q4_K_M) | 20.66 GB | Resident (+3.34 GB, FlashAttn) | Resident (+3.34 GB, HIP/Vulkan) |
| Alibaba Wan 2.1 14B 720p (Q4 / FP8) | 18.15 GB | Native SageAttention (+5.85 GB) | Linux ROCm Only (+5.85 GB, Slow) |
| FLUX.1 [dev] NF4 / Q4 | 15.46 GB | Native bitsandbytes (+8.54 GB) | GGUF Q4 via ROCm (+8.54 GB) |
| Meta LLaMA 3.3 70B (Q4_K_M) | 46.10 GB | Needs 2nd 3090 + NVLink | Exceeds VRAM (-22.10 GB OOM) |
Underneath their matched 384-bit controllers, Ampere GA102 and RDNA 3 Navi 31 solve high-bandwidth inference differently. AMD splits Navi 31 (`gfx1100`) into a 5nm Graphics Compute Die flanked by six 6nm Memory Cache Dies housing 96 MB of 2nd-generation Infinity Cache and 20 Gbps GDDR6 (960 GB/s). In memory-bound GGUF decoding via llama.cpp Vulkan or Linux ROCm HIP, the 7900 XTX slightly edges out the RTX 3090's 936 GB/s GDDR6X. Yet RDNA 3 lacks dedicated Tensor Core hardware equivalent to Ampere's 2:4 sparsity and TF32/BF16 matrix engines, relying on dual-issue Wave32 WMMA instructions. Consequently, PyTorch video diffusion stacks (Wan 2.1, HunyuanVideo, SageAttention) and vLLM quantized AWQ/GPTQ kernels often require custom ROCm containers or Windows ZLUDA workarounds, whereas GA102 runs upstream wheels immediately.
Already running an AMD RX 7900 XTX? Boot Ubuntu Linux with official ROCm 6.4+ drivers, set `export HSA_OVERRIDE_GFX_VERSION=11.0.0` only when legacy PyTorch wheels fail to recognize `gfx1100`, and launch `llama-server -m model.gguf -ngl 99 -fa on -ctk q8_0 -ctv q8_0` (or `--device Vulkan0` on Windows) for reliable throughput.
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On Ubuntu Linux with official ROCm PyTorch wheels, ComfyUI image workflows run acceptably, though slower than NVIDIA CUDA during long sampling passes. Video pipelines relying on SageAttention, xFormers, or bitsandbytes 4-bit often break or demand tedious manual kernel compilation.
AMD provides no consumer interconnect bridge on RDNA 3, forcing dual RX 7900 XTX builds to split tensors across PCIe 4.0 x8 motherboard lanes via RCCL or llama.cpp, whereas two RTX 3090 GPUs share a dedicated 112.5 GB/s NVLink bridge.
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