| Model & Quantization | VRAM Demand | AMD Radeon RX 7900 XTX | NVIDIA GeForce RTX 4090 |
|---|---|---|---|
| Qwen 2.5 Coder 32B (Q4_K_M, 8k) | 23.1 GB | Runs Comfortably (~36 tok/s) | Runs Comfortably (~42 tok/s) |
| Google Gemma 4 31B (Q4_K_M, 32k FP8 KV) | 22.7 GB | Tight Margin (+1.3GB) | Tight Margin (+1.3GB, Native FP8) |
| Alibaba Qwen3-Coder 30B-A3B (Q4_K_M) | 20.7 GB | Runs Comfortably (~88 tok/s) | Runs Comfortably (~95 tok/s) |
| Alibaba Wan 2.1 14B 720p (Q4) | 18.2 GB | Linux ROCm / Slower DiT | Runs Comfortably (Turnkey CUDA) |
| FLUX.1 [dev] (FP8) | 24.1 GB | Tight / CPU Offload (No FP8 HW) | Tight / Native FP8 Tiled |
AMD's Navi 31 chiplet package pairs a 5nm Graphics Compute Die (6,144 stream processors, dual-issue FP16/WMMA pipelines, 355W TDP) with six 6nm Memory Cache Dies housing 96MB of second-generation Infinity Cache over a 384-bit 20 Gbps GDDR6 bus (960 GB/s). NVIDIA's monolithic AD102 packs 16,384 CUDA cores, 512 fourth-generation Tensor Cores with dedicated E4M3 FP8 matrix hardware, and 72MB of L2 cache over 21 Gbps GDDR6X (1,008 GB/s) at 450W TDP. Because single-batch 32B Q4 decoding is capped primarily by memory bus speed, Navi 31 trails AD102 by just 14% on raw token output. Yet the instant workloads turn compute-heavy—32k RAG prompt prefill, LoRA backpropagation, or MMDiT denoising—AD102's massive Tensor Core FLOPS, FlashAttention-2, and Triton CUDA wheels finish jobs 2x–3.5x faster without ROCm dependency workarounds.
Maximizing an RX 7900 XTX on Ubuntu 24.04? Install ROCm 6.4+ (`gfx1100` native), export `OLLAMA_FLASH_ATTENTION=1` and `OLLAMA_KV_CACHE_TYPE=q8_0`, or pass `-fa on -ctk q8_0 -ctv q8_0` to `llama-server` so Gemma 4 31B (`22.67 GB`) sustains 32k context cleanly while disabling PCIe ASPM power throttling in sysfs.
Matched Upgrade: Driving a 450W RTX 4090 or 355W RX 7900 XTX through sustained inference requires an ATX 3.1 1000W PSU; its native 600W 12V-2x6 cable and 200% excursion tolerance stop microsecond power spikes from tripping system protection.
Corsair RM1000x ATX 3.1 on Amazon Prime ↗Zero monthly subscriptions, 100% private inference, and unmetered tokens for your homelab or workstation.
Skip the upfront hardware expense. Spin up secure cloud instances from $0.35/hr on Vast.ai or $0.79/hr on RunPod.
Interactive LLM decoding streams active weight tensors from VRAM on every generated token, making 384-bit memory bus speed decisive. At 960 GB/s versus 1,008 GB/s, the RX 7900 XTX sits within 5% of the RTX 4090's raw memory bandwidth.
Long-context RAG prompt ingestion, Unsloth QLoRA fine-tuning, native E4M3 FP8 ComfyUI video workflows (Wan 2.1, HunyuanVideo), and plug-and-play Windows Python wheels with FlashAttention-2 all execute noticeably faster and without manual compiler tuning on Ada Lovelace CUDA.
Inspect exact KV-cache formulas, test 32k/128k context allocations, or compare cloud rental break-even costs in the interactive matchmaker.
Launch Interactive Showdown Calculator →