Intel landed the Arc Pro B70 with 32 GB of VRAM at a price that undercuts the RTX 5080. On paper that is a serious proposition for LLM hosting because 32 GB lets you serve models the 5080 cannot touch. On our dedicated GPU servers we have run both through real inference stacks. The answer is nuanced.
What We Cover
- Raw specification comparison
- IPEX-LLM and oneAPI in 2026
- Which LLMs fit each card
- Tokens per second, both cards
- Who should pick which
Specs
| Spec | Arc Pro B70 | RTX 5080 |
|---|---|---|
| VRAM | 32 GB | 16 GB GDDR7 |
| Bandwidth | ~560 GB/s | ~960 GB/s |
| Software stack | IPEX-LLM, oneAPI, OpenVINO | CUDA, full vLLM support |
| FP8 | Yes | Yes |
| TDP | ~220 W | 360 W |
The Software Reality
Intel’s story matters more than spec sheets. IPEX-LLM has matured. You can run Llama 3, Qwen, Mistral, and most mainstream models through it with minor changes. vLLM has experimental Intel backend support via IPEX. What you lose is the library ecosystem – the niche fine-tuning scripts, the LoRA toolchains, the flash-attention ports. If your workload is “run a production LLM API,” the B70 works. If your workload is “experiment with the latest GitHub repo every week,” you will hit friction.
Model Fit
| Model | Arc Pro B70 32GB | RTX 5080 16GB |
|---|---|---|
| Llama 3 8B FP16 | Easy | Tight but fits |
| Qwen 2.5 14B FP16 | Fits | Does not fit FP16 |
| Qwen 2.5 32B INT4 | Fits comfortably | Does not fit |
| Gemma 2 27B INT8 | Fits | Does not fit |
| Mistral Small 3 24B INT4 | Comfortable with large context | INT4 only, short context |
The VRAM delta changes what you can host. A 5080 maxes out around 12B at FP16 or 30B at INT4 with tight KV cache. The B70 handles 30B at INT8 with room for batching. See our Qwen 32B VRAM page for specifics.
Host 24-32B Models on a Single Card
Dedicated Arc Pro B70 servers from our UK datacenter with fixed monthly pricing.
Browse GPU ServersTokens Per Second
Where both cards fit a model – say, Llama 3 8B at INT8 – the 5080 runs roughly 30-45% faster per token thanks to the raw bandwidth advantage and mature CUDA kernels. Where only the B70 fits – 30B class models – the comparison becomes moot. You are measuring a number against zero.
Which Card Wins
If your target model is 7-13B class and latency is everything, the 5080 wins. If your target is 20-32B and you want to avoid multi-GPU complexity, the B70 is compelling on price-per-VRAM. If your team already knows CUDA and relies on a long tail of Python libraries, the 5080 saves you days of debugging. For anyone purely serving a fixed production model through IPEX or OpenVINO, the B70 is a legitimate choice in 2026. Compare against the B70 vs 3090 matchup too – that is the other interesting 32GB-class decision.
