Table of Contents
The B60 is £30/mo cheaper than the RTX 3090 with the same 24 GB VRAM — and adds ECC error-correcting memory. The 3090 wins on software compatibility (CUDA, vLLM, TensorRT) and raw throughput (~25–30% faster per generation on SDXL and LLM decode). Pick the B60 if your workload runs on SYCL/OpenVINO (image gen, LlamaCPP, IPEX) and you want the most VRAM per pound. Pick the 3090 if you need CUDA-native frameworks or the fastest possible per-token speed.
Spec Comparison
| Spec | Intel Arc Pro B60 | NVIDIA RTX 3090 |
|---|---|---|
| Architecture | Battlemage Xe2 | Ampere |
| VRAM | 24 GB ECC GDDR6 | 24 GB GDDR6X |
| Memory bandwidth | 456 GB/s | 936 GB/s |
| Memory bus | 192-bit | 384-bit |
| FP32 compute | ~24 TFLOPS | ~35 TFLOPS |
| AI engines | 160 XMX (INT8/BF16) | 328 Tensor Cores (FP16/INT8) |
| PCIe interface | PCIe 5.0 x16 | PCIe 4.0 x16 |
| ECC memory | Yes | No |
| Software stack | oneAPI / SYCL, OpenVINO | CUDA (mature ecosystem) |
| TDP | ~120 W | ~350 W |
| GigaGPU price | £129/mo | £159/mo |
The most striking difference isn’t VRAM — it’s bandwidth. The RTX 3090’s 936 GB/s (on a 384-bit bus) is more than double the B60’s 456 GB/s. That gap directly affects every memory-bandwidth-bound operation: KV cache reads during LLM generation, VAE decode in image generation, and large tensor loads during model inference. On the other hand, the B60 draws ~120 W vs the 3090’s ~350 W — meaningfully lower operating cost and heat over 24/7 workloads.
The Software Stack Gap
This is the most important factor in the decision:
| Framework / Tool | Arc Pro B60 | RTX 3090 |
|---|---|---|
| PyTorch (standard) | Via IPEX-XPU (pip install intel-extension-for-pytorch) | Native CUDA, pip install torch |
| ComfyUI | Yes — IPEX-XPU or OpenVINO node | Yes — native CUDA |
| Automatic1111 | Yes — --use-ipex flag | Yes — native CUDA |
| Ollama | Yes — SYCL backend | Yes — CUDA backend |
| LlamaCPP | Yes — build with -DGGML_SYCL=ON | Yes — build with -DGGML_CUDA=ON |
| IPEX-LLM | Yes — native Intel stack | No (NVIDIA only) |
| vLLM | Limited — CPU path only | Yes — first-class support |
| TensorRT-LLM | No | Yes |
| FlashAttention | No (CUDA-specific) | Yes |
| xformers | No | Yes |
| bitsandbytes (GPU INT4/INT8) | No | Yes |
| OpenVINO | Yes — native Intel | Limited |
The practical implication: if you’re starting a new project and picking your stack, the B60 imposes a setup cost (IPEX wheel instead of standard torch, SYCL flag in llama.cpp). If you’re migrating an existing CUDA project, assess which libraries it depends on — vLLM and TensorRT are hard blockers; ComfyUI and LlamaCPP are easy ports.
Image Generation Comparison
| Workload | Arc Pro B60 (£129/mo) | RTX 3090 (£159/mo) | Winner |
|---|---|---|---|
| SD 1.5 @ 512×512, 20 steps | ~1.5 s | ~1.1 s | 3090 (bandwidth) |
| SDXL @ 1024×1024, 30 steps | ~8 s | ~6 s | 3090 (bandwidth) |
| Flux.1 Schnell, FP16 | ~4 s | ~3 s | 3090 (slight) |
| Flux.1 Dev, FP16 (full model resident) | ~20 s (OpenVINO) | ~22 s (CUDA, T5 offload) | B60 (no T5 offload) |
| Flux.1 Dev, Q4 GGUF | ~14 s | ~12 s | 3090 (slight) |
| SDXL + 3× ControlNet | Comfortable | Tight (OOM possible) | B60 (same VRAM budget, but 3090 may OOM) |
The RTX 3090 is consistently faster on standard SDXL and SD 1.5 workflows because its 936 GB/s bandwidth feeds the VAE decoder and attention cache at double the rate. The B60’s advantage appears specifically with Flux.1 Dev at FP16: the 3090 at 24 GB has to offload the T5-XXL text encoder (~5 GB) to CPU before the denoising step, adding ~2 seconds per generation. The B60 on OpenVINO squeezes everything into 24 GB without offloading.
LLM Inference Comparison
| Model + Quant | B60 tok/s (est.) | RTX 3090 tok/s (est.) | B60 vs 3090 |
|---|---|---|---|
| Llama 3.1 8B Q4_K_M | ~50 | ~70 | 3090 ~40% faster |
| Mistral 7B Q4_K_M | ~55 | ~75 | 3090 ~35% faster |
| Qwen 2.5 14B Q4_K_M | ~32 | ~45 | 3090 ~40% faster |
| Llama 3.1 8B FP16 | ~22 | ~35 | 3090 ~60% faster |
The bandwidth gap hurts the B60 on LLM inference: each generated token requires reading the full KV cache plus model weights from VRAM, and 936 GB/s lets the 3090 do that ~2× faster. For latency-sensitive single-user serving, the 3090 is the better pick. For batch inference where throughput at scale matters more than first-token latency, the difference narrows. For workflows that don’t require CUDA-specific frameworks, the B60 at £30/mo less delivers acceptable performance.
ECC Memory: Why It Matters
The Arc Pro B60’s “Pro” designation comes with ECC (Error-Correcting Code) GDDR6 — the RTX 3090 (a consumer card) has no ECC. In practice:
- Without ECC: a cosmic ray or electrical glitch flips a single bit in a weight matrix during a 24/7 inference job. The output is subtly wrong — and you won’t know unless you validate every response.
- With ECC: single-bit errors are detected and corrected transparently, with no change to outputs.
For interactive chat, bit-flip probability is low enough that ECC is cosmetic. For production inference APIs that run continuously, or for batch scientific computing where output correctness is audited, ECC provides a meaningful reliability guarantee. It’s the same reason data centre GPUs (A100, H100) all include ECC — the Pro tier of workstation and server GPUs has offered it for years.
Cost Analysis: 12-Month Perspective
| Period | Arc Pro B60 (£129/mo) | RTX 3090 (£159/mo) | B60 saving |
|---|---|---|---|
| 1 month | £129 | £159 | £30 |
| 3 months | £387 | £477 | £90 |
| 6 months | £774 | £954 | £180 |
| 12 months | £1,548 | £1,908 | £360 |
Over a year, the B60 saves £360 vs the RTX 3090 for the same 24 GB VRAM. That’s roughly the equivalent of 2.7 additional months of compute. For workloads where the ~25–35% speed difference doesn’t materially affect your pipeline (e.g. batch image generation where you don’t need interactive latency), the B60 compounds that saving meaningfully over time.
When to Pick the B60
- Primary workload is image generation: ComfyUI + OpenVINO or IPEX is production-ready, and the B60 handles Flux.1, SDXL, and SD 1.5 without issue.
- Running LlamaCPP or IPEX-LLM: both have mature SYCL backends; setup is a one-time step.
- Running Ollama: SYCL backend works; slightly slower than CUDA at same quant but functional for most use cases.
- ECC matters for your workload: 24/7 batch inference, scientific pipelines, production APIs where silent data corruption is unacceptable.
- Budget is the constraint: £129/mo is the cheapest 24 GB card on GigaGPU.
- You’re on Intel’s stack already: OpenVINO workloads, oneAPI pipelines, Intel-native computer vision.
When to Pick the RTX 3090
- You need vLLM with GPU kernels: PagedAttention, continuous batching — these require CUDA and have no SYCL equivalent at the same maturity.
- Your existing code uses CUDA extensions: bitsandbytes INT4/INT8 GPU, custom CUDA C++ ops, TensorRT.
- You need maximum LLM generation speed: 936 GB/s bandwidth delivers ~35–40% more tokens/sec on the same model at the same quantisation.
- xformers is in your A1111/ComfyUI stack: xformers is NVIDIA-only; the 3090’s CUDA path is faster with it enabled.
- You want broadest Python library compatibility:
pip install torchand every library works out of the box; no IPEX step.
Verdict
The Arc Pro B60 is the right choice if your workload maps onto the Intel software stack (image generation, LlamaCPP/IPEX-LLM, Ollama) and you want 24 GB for as little as possible. The £30/mo saving compounds — at 12 months that’s £360 back, and the ECC is a genuine production reliability benefit that the 3090 can’t offer.
The RTX 3090 is the right choice if you need CUDA-native frameworks, depend on vLLM or TensorRT, or want the fastest possible per-token LLM generation speed. The mature ecosystem and 2× memory bandwidth justify the premium for CUDA-dependent workloads.
See the full GPU catalogue to compare all options. For Flux.1 specifically see best GPU for Flux.1; for LLMs see best GPU for LLaMA.
Compare and deploy
Arc Pro B60 at £129/mo · RTX 3090 at £159/mo · Both dedicated bare metal, UK data centre · Order the B60 · Compare all GPUs.
