Table of Contents
RX 9070 XT has 4 GB more VRAM (16 vs 12) and costs £10/mo more (£149 vs £139). RTX 5070 has CUDA — full vLLM, TensorRT, xformers, and plug-and-play Python AI libraries. ROCm (AMD) has improved substantially but still lags CUDA on vLLM, TensorRT-LLM, and some custom CUDA extensions. Pick 5070 for CUDA-dependent stacks; 9070 XT if you need 16 GB and ROCm is sufficient for your use case.
Spec Comparison
| Spec | RTX 5070 | RX 9070 XT |
|---|---|---|
| Architecture | NVIDIA Blackwell GB205 | AMD RDNA 4 (Navi 48) |
| VRAM | 12 GB GDDR7 | 16 GB GDDR6 |
| Memory bandwidth | ~672 GB/s | ~576 GB/s |
| Compute cores | 6,144 CUDA | 4,096 CUs (AMD) |
| AI accelerators | 5th Gen Tensor (FP4/FP8) | AI Accelerators (FP8/INT8) |
| Software stack | CUDA (mature) | ROCm (improving) |
| vLLM GPU support | Full | Experimental ROCm path |
| TDP | ~150 W | ~220 W |
| Price | £139/mo | £149/mo |
CUDA vs ROCm
| Framework | RTX 5070 (CUDA) | RX 9070 XT (ROCm) |
|---|---|---|
| PyTorch | pip install — works | ROCm wheel — works |
| vLLM | Full support | Experimental, may work |
| llama.cpp | Full CUDA | Full ROCm/HIP path |
| Ollama | Automatic CUDA | ROCm support (may need config) |
| ComfyUI | Native CUDA | ROCm PyTorch |
| xformers | Yes | No (CUDA only) |
| TensorRT-LLM | Yes | No |
| bitsandbytes GPU | Yes | Partial (some ops CPU fallback) |
| FlashAttention 3 | Yes (Blackwell) | No |
ROCm has improved significantly through 2025–26. Basic PyTorch operations, ComfyUI, and llama.cpp work well on AMD cards. The gaps are in specialised high-performance inference libraries: vLLM’s ROCm path is experimental, TensorRT is NVIDIA-exclusive, and xformers is CUDA-only. For plug-and-play compatibility with any CUDA Python library, the RTX 5070 is categorically easier.
Image Generation
| Workload | RTX 5070 (£139) | RX 9070 XT (£149) | Winner |
|---|---|---|---|
| SDXL (1024×1024) | ~4–5 s (CUDA) | ~5–6 s (ROCm) | 5070 (speed) |
| Flux.1 Schnell Q4 | Tight (12 GB) | Comfortable (16 GB) | 9070 XT (VRAM) |
| Flux.1 Dev Q4 | Very tight | Comfortable | 9070 XT |
| SDXL + 2× ControlNet | No (OOM) | Yes (16 GB) | 9070 XT |
| xformers attention | Yes | No | 5070 |
LLM Inference
| Model | RTX 5070 tok/s | RX 9070 XT tok/s | Winner |
|---|---|---|---|
| Llama 3.1 8B Q4 | ~75–85 | ~55–65 | 5070 (bandwidth) |
| Qwen 2.5 14B Q4 | ~45–55 | ~35–45 | 5070 |
| Llama 3.1 8B FP16 | No (12 GB) | Yes (16 GB) | 9070 XT (VRAM) |
| vLLM batching | Full support | Experimental | 5070 (reliability) |
When RTX 5070 Wins
- CUDA-native frameworks required (vLLM, TensorRT, xformers, bitsandbytes)
- Faster LLM token generation on models that fit in 12 GB
- Lower TDP and power cost (~150W vs ~220W)
- £10/mo cheaper
- Widest Python AI library compatibility
When RX 9070 XT Wins
- 16 GB VRAM needed: Flux.1 Dev Q4 comfortably, SDXL + multi-ControlNet, 7B FP16
- ROCm is sufficient for your stack (ComfyUI, llama.cpp, basic Ollama)
- AMD open-source preference (fully open GPU driver stack)
Verdict
The £10/mo price difference is small. The software compatibility difference is large. Unless you specifically need 16 GB and ROCm covers your use case, the RTX 5070’s CUDA ecosystem makes it the safer and faster choice for general AI workloads. For the 16 GB use case on a CUDA budget, the RTX 5060 Ti at £119/mo is worth comparing. See the full GPU catalogue.
Compare mid-range cards
RTX 5070 — £139/mo, 12 GB CUDA · RX 9070 XT — £149/mo, 16 GB ROCm · All GPUs.
