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The RTX 5070 (£139/mo) is faster and uses the full CUDA ecosystem. The Arc Pro B60 (£129/mo) has double the VRAM (24 GB ECC vs 12 GB) and ECC error correction. Pick the 5070 for CUDA-dependent frameworks (vLLM, TensorRT), fast SDXL, or 7B–13B Q4 inference. Pick the B60 for large model capacity (Flux.1 FP16, 7B FP16, 13B+ Q4 with KV headroom) or production ECC reliability at the lowest price.
Spec Comparison
| Spec | RTX 5070 | Arc Pro B60 |
|---|---|---|
| Architecture | NVIDIA Blackwell GB205 | Intel Battlemage Xe2 |
| VRAM | 12 GB GDDR7 | 24 GB ECC GDDR6 |
| Memory bandwidth | ~672 GB/s | 456 GB/s |
| Compute cores | 6,144 CUDA | 2,560 Xe2 shaders + 160 XMX |
| AI accelerators | 5th Gen Tensor Cores | 160 XMX Engines (INT8/BF16) |
| FP32 | ~36 TFLOPS | ~24 TFLOPS |
| ECC memory | No | Yes |
| CUDA | Yes | No (SYCL/oneAPI) |
| vLLM | Full | CPU path only |
| TDP | ~150 W | ~120 W |
| PCIe | 5.0 x16 | 5.0 x16 |
| Price | £139/mo | £129/mo |
Software: CUDA vs SYCL
| Framework | RTX 5070 (CUDA) | Arc Pro B60 (SYCL) |
|---|---|---|
| pip install torch | Works immediately | Needs Intel XPU wheel |
| Ollama | Automatic | Needs env var |
| ComfyUI | Native (--use-xformers) | IPEX-XPU or OpenVINO node |
| vLLM GPU | Full support | Not supported |
| TensorRT-LLM | Yes | No |
| bitsandbytes GPU | Yes | No |
| FlashAttention 3 | Yes | No |
| OpenVINO | Limited | Native (best) |
| IPEX-LLM | No | Native (best) |
The software gap is the defining factor. If your workflow depends on CUDA-native libraries (vLLM, TensorRT, bitsandbytes GPU, xformers), the RTX 5070 is the only option of the two. If your workflow targets the Intel stack (ComfyUI + OpenVINO, LlamaCPP SYCL, IPEX-LLM) or you’re model-size-constrained and need 24 GB, the B60 delivers more value.
Image Generation
| Workload | RTX 5070 | Arc Pro B60 | Winner |
|---|---|---|---|
| SDXL (1024×1024) | ~4–5 s (CUDA) | ~8 s (IPEX) | 5070 (~60% faster) |
| Flux.1 Dev FP16 | No (12 GB) | Yes (24 GB via OpenVINO) | B60 |
| Flux.1 Dev Q4 GGUF | Tight (~12 GB) | Comfortable (24 GB) | B60 (headroom) |
| Flux.1 Schnell Q4 | ~3 s | ~4 s | 5070 (speed) |
| SDXL + 2× ControlNet | No (OOM) | Yes (24 GB) | B60 |
| xformers attention | Yes | No | 5070 |
LLM Inference
| Model | RTX 5070 tok/s | Arc Pro B60 tok/s | Winner |
|---|---|---|---|
| Llama 3.1 8B Q4_K_M | ~80 | ~50 | 5070 (~60% faster) |
| Qwen 2.5 14B Q4_K_M | ~50 | ~32 | 5070 |
| Llama 3.1 8B FP16 | No (12 GB) | Yes (16 GB on 24) | B60 |
| Gemma 2 27B Q4 | No (>12 GB) | Yes (~16 GB on 24) | B60 |
| vLLM continuous batching | Yes | No | 5070 |
| 7B + Whisper simultaneous | Yes (~8 GB total) | Yes (~19 GB total) | Tie (both fit) |
When RTX 5070 Wins
- CUDA-native frameworks are required (vLLM, TensorRT, bitsandbytes)
- Fast token generation matters more than model size (70–80 tok/s vs 50)
- SDXL generation speed is the priority
- You want plug-and-play Python AI library compatibility
- Running models that comfortably fit in 12 GB at Q4 or FP8
When Arc Pro B60 Wins
- You need 24 GB VRAM — 7B FP16, Flux.1 Dev, 13B+ with context headroom
- ECC memory for production 24/7 inference reliability
- Your image generation workflow is OpenVINO or IPEX-native
- Budget is the hard constraint — £129 vs £139
- Running multiple models simultaneously (B60’s 24 GB holds far more)
Verdict
These cards solve different problems. The RTX 5070 is the fastest CUDA card under £150 and the right pick if your workflow fits in 12 GB. The Arc Pro B60 is the cheapest 24 GB ECC card on GigaGPU and the right pick if VRAM capacity defines your workload ceiling. Neither is universally better — it depends entirely on model size requirements and framework dependencies.
Choose your card
RTX 5070 — £139/mo, 12 GB CUDA · Arc Pro B60 — £129/mo, 24 GB ECC · Compare all GPUs.
