| Model & Quantization | VRAM Demand | NVIDIA GeForce RTX 5060 Ti | AMD Radeon RX 9070 XT |
|---|---|---|---|
| Qwen 2.5 14B Instruct (Q4_K_M) | 12.08 GB | Comfort Margin (~37 tok/s) | Comfort Margin (~53 tok/s) |
| OpenAI gpt-oss 20B MoE (Q4_K_M) | 14.43 GB | Tight Fit (+1.57 GB, NVFP4) | Tight Fit (+1.57 GB, ROCm) |
| Google Gemma 4 12B Multimodal (Q6_K) | 12.70 GB | Comfort Margin (~32 tok/s) | Comfort Margin (~45 tok/s) |
| FLUX.1 [schnell] 4-bit | 14.46 GB | Tight Fit (+1.54 GB, CUDA) | Tight Fit (+1.54 GB, Vulkan/ROCm) |
| Tencent HunyuanVideo 13B (Q4 / NF4) | 16.41 GB | Swap 1–2 Blocks Cleanly (CUDA) | Overflow (-0.41 GB ROCm Friction) |
This matchup pits Blackwell's narrow high-speed GB206 die against AMD's monolithic RDNA 4 Navi 48 (`gfx1201`) silicon. NVIDIA trims cost and board power to 180W by driving 28 Gbps PAM3 GDDR7 across a 128-bit PCIe 5.0 x8 package (448 GB/s), pairing it with 5th-gen Tensor Cores capable of native NVFP4/MXFP4 execution. AMD takes the opposite approach on Navi 48: a wider 256-bit GDDR6 bus at 20 Gbps delivers 640 GB/s backed by 64 MB of 3rd-generation Infinity Cache and revamped 2nd-gen AI Accelerators supporting native hardware FP8 WMMA under ROCm 6.4+. For pure text inference in Linux llama.cpp or Vulkan, Navi 48 streams tokens ~43% faster. Yet whenever workloads touch Python diffusion ecosystems—like HunyuanVideo 13B block-swapping or bitsandbytes 4-bit LoRA training—NVIDIA's CUDA stack avoids compilation friction.
On an RX 9070 XT under Ubuntu Linux, compile `llama-server` with `-DGGML_HIP=ON -DAMDGPU_TARGETS=gfx1201` (or export `HSA_OVERRIDE_GFX_VERSION=12.0.1` when legacy ROCm wheels omit RDNA 4) alongside `-fa on -ctk q8_0 -ctv q8_0`; on the RTX 5060 Ti 16GB, run Blackwell-native MXFP4 weights for instant prompt prefill speedups.
Matched Upgrade: Frequent swapping between 12GB–15GB GGUF quants and FLUX checkpoints bottlenecks slow SSDs; a Samsung 990 PRO 2TB NVMe sustains 7,450 MB/s warm loads inside 16GB builds.
Samsung 990 PRO 2TB NVMe 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.
If you interact with dense 14B Q4 coding or chat models for hours daily, jumping from ~37 tok/s to ~53 tok/s feels noticeably brisker for $150 extra, provided your power supply handles 304W sustained load and you run Linux ROCm or Vulkan.
Diffusion image and video synthesis is heavily compute-bound rather than purely memory-bandwidth limited. Custom ComfyUI nodes optimize around PyTorch CUDA FlashAttention-2, SageAttention, and Blackwell hardware NVFP4 kernels, whereas RDNA 4 `gfx1201` still lags in community PyTorch operator coverage.
Inspect exact KV-cache formulas, test 32k/128k context allocations, or compare cloud rental break-even costs in the interactive matchmaker.
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