Intel Arc Pro B70 Hosting — The 24 GB Power-Sipper
Intel’s professional Battlemage card brings 24 GB of GDDR6 to a 200 W envelope — the lowest TDP-per-VRAM ratio in our catalogue. The right pick when you want to run 13B–14B class models without the power bill (or the CUDA lock-in) of a 3090 or 4090.
Arc Pro B70 Server Specs
The hardware you actually rent.
| GPU model | Intel Arc Pro B70 (Battlemage, Xe2) |
|---|---|
| Architecture | Xe2 — 2nd gen XMX matrix engines |
| VRAM | 24 GB GDDR6 @ 456 GB/s |
| Xe-cores | 20 (160 XMX AI engines) |
| FP16 compute | ~ 24 TFLOPS |
| INT8 (XMX dense) | ~ 256 TOPS |
| TDP | 200 W |
| Host CPU | AMD Ryzen 7 / 9 |
| Host RAM | Up to 64 GB DDR5 |
| Storage | 1 TB NVMe + 4 TB SATA SSD |
| Network | 1 Gbps unmetered |
| Location | London, United Kingdom |
What Fits on a Single Arc Pro B70
24 GB of VRAM puts the B70 in the same envelope as an RTX 3090. Comfortable for 7B–9B at FP16 with long context, 13B–14B at INT8, and Whisper + LLM co-hosting on a single card.
| Model | Params | FP16 | INT8 | Notes |
|---|---|---|---|---|
| Mistral 7B Instruct | 7B | 14 GB FP16 | 7 GB INT8 | FP16 with plenty of headroom |
| Llama 3.1 8B | 8B | 16 GB FP16 | 8 GB INT8 | Comfortable FP16 at 32K context |
| Gemma 2 9B | 9B | 18 GB FP16 | 9 GB INT8 | FP16 fits with KV cache headroom |
| Llama 3.1 13B | 13B | 26 GB FP16 | 13 GB INT8 | INT8 only — fits with KV budget |
| Qwen 2.5 14B | 14B | 28 GB FP16 | 14 GB INT8 | INT8 comfortable, FP16 won’t fit |
| DeepSeek-Coder V2 Lite | 16B MoE | 32 GB FP16 | 16 GB INT8 | INT8 fits — strong code model |
| Whisper Large-v3 + 7B LLM | ~9B combined | ~22 GB FP16 | ~12 GB INT8 | Comfortable single-card co-hosting |
| SDXL 1.0 | 3.5B | 8 GB FP16 | n/a | Runs via OpenVINO or PyTorch IPEX |
| FLUX.1 schnell | 12B | 24 GB FP16 | 12 GB INT8 | FP16 tight — INT8 is the sweet spot |
When the Arc Pro B70 Is the Right Card
Real customer workloads where Intel’s 24 GB Battlemage card earns its place in the rack.
OpenVINO production deployments
If you’ve already invested in Intel’s OpenVINO toolkit — for edge devices, NUC fleets, or CPU-side inference — the B70 is the only card in our catalogue that runs your existing graph natively without re-tooling for CUDA.
Power-constrained colocation
200 W TDP is roughly 60% the draw of a 3090 and 55% of a 4090 for the same 24 GB envelope. If your rack is metered on amps or you’re paying UK industrial electricity rates, the B70 is the cheapest 24 GB card to actually run.
13B / 14B class LLM inference
Llama 3.1 13B and Qwen 2.5 14B at INT8 are the workloads where 16 GB cards fall over and 24 GB cards shine. The B70 hits this tier at a lower entry price than any other 24 GB card we host.
Long-context 8B serving
Llama 3.1 8B at FP16 with a 64K–128K context window leaves ~12 GB of KV cache room on a 24 GB card. Document QA, codebase analysis, and long-form summarisation all benefit.
Embeddings + reranker on Intel silicon
BGE-large + a cross-encoder reranker fit in under 8 GB, leaving headroom for a small generation model on the same card. Native IPEX kernels make this a strong throughput-per-watt setup.
Vendor-neutrality / non-NVIDIA strategy
If your procurement, security, or compliance team has decided not to bet the entire stack on CUDA, the B70 is a credible second-source 24 GB card. Pair it with an AMD R9700 or MAX+ 395 for a fully NVIDIA-free fleet.
Arc Pro B70 vs Other 24 GB Class Cards
How Intel’s pro Battlemage card stacks up against the rest of the GigaGPU catalogue at the 24 GB tier and around it.
| GPU | VRAM | TDP / Notes | 13B INT8 fits? | Price |
|---|---|---|---|---|
| Arc Pro B70 | 24 GB GDDR6 | 200 W — oneAPI / IPEX / OpenVINO stack | Yes (13B / 14B INT8) | from £179 |
| RTX 3090 | 24 GB GDDR6X | 350 W — CUDA mature, faster on FP16 | Yes (13B FP16 fits) | from £159 |
| RTX 4090 | 24 GB GDDR6X | 450 W — fastest 24 GB card we host | Yes, with massive headroom | from £289 |
| Radeon AI Pro R9700 | 32 GB GDDR6 | ~300 W — ROCm stack | Yes, plus 32B INT4 | from £199 |
| RTX 5080 | 16 GB GDDR7 | 360 W — fastest small card on FP4/FP8 | 14B INT4 only | from £189 |
Deep Dive
The honest software-stack story
The B70 is genuinely good silicon — 24 GB at 200 W, second-generation XMX matrix engines, a real Intel professional product line. The caveat is the software ecosystem. PyTorch on Intel GPUs goes through IPEX (Intel Extension for PyTorch) or the SYCL backend; OpenVINO is the most polished path; vLLM and llama.cpp have Intel back-ends but they trail the CUDA paths in feature velocity.
If your team already runs Intel infrastructure — OpenVINO in production, oneAPI on dev workstations, or CPU-side inference at the edge — the B70 is the natural extension. If you’re starting from a blank slate and your reference implementations all assume CUDA, an RTX 3090 at £159 will get you running faster.
Why we still see B70 demand growing
Three reasons. First, power: at 200 W the B70 is half the draw of a 4090 for the same VRAM, and UK electricity costs are no joke. Second, vendor-neutrality: more procurement teams are explicitly asking for a non-NVIDIA option after the last two years of CUDA scarcity and pricing. Third, OpenVINO production: shops that already serve models through OVMS to edge devices want a server card that runs the same IR graphs without re-tooling.
None of those is a fit for everyone. But each is a real, growing segment — and for those teams the B70 is the right card, not a compromise.
INT8 is the precision tier that matters here
The B70’s XMX engines hit ~256 TOPS dense at INT8 — that’s the precision where this card is most competitive on raw throughput, and it’s also the precision where 13B–14B models fit comfortably in 24 GB:
- Llama 3.1 13B at FP16 → 26 GB. Doesn’t fit.
- Llama 3.1 13B at INT8 → ~13 GB. Comfortable, with 8–10 GB for KV cache.
- Qwen 2.5 14B at INT8 → ~14 GB. Same story.
If you’re picking the B70, plan to serve at INT8. The OpenVINO POT (post-training optimisation) toolkit and IPEX both have mature INT8 paths — this is the well-trodden route, not a workaround.
Frequently Asked Questions
The questions buyers actually ask before committing to an Intel GPU server.
Will my CUDA code just run on the B70?
No. CUDA is NVIDIA-only. You’ll use IPEX (Intel Extension for PyTorch) for PyTorch workloads, OpenVINO for production deployment, or the SYCL backend for portable code. Most modern Hugging Face transformers code runs with a one-line device change to xpu.
Does vLLM work on the B70?
Yes — there’s an Intel back-end for vLLM, but it lags behind the CUDA path on feature velocity. For production INT8 serving on Intel GPUs, OpenVINO Model Server (OVMS) is usually the more polished route.
Why pick this over a 3090?
Power and software preference. The 3090 is faster on raw FP16 and has the deeper CUDA ecosystem. The B70 draws 200 W instead of 350 W (huge for power-billed colocation), is newer silicon, and is the right call if you’re already running Intel-stack production. See RTX 3090 hosting.
Can I fine-tune on a B70?
QLoRA on 7B–13B models works through IPEX — the 24 GB envelope helps here. Full SFT on 13B is borderline. For serious fine-tuning workloads we’d point you at a 4090 or 6000 Pro.
How does it compare to the consumer Arc B580?
Same Battlemage architecture, but the B580 is a 12 GB card and the B70 is 24 GB with twice the Xe-cores. Only the B70 fits 13B+ models. The B580 is a desktop card; the B70 is the professional/datacenter SKU we host.
Is the B70 a good 4090 alternative?
Cheaper (£179 vs £289) and lower-power, but slower on raw throughput. If you need raw speed and don’t care about power or vendor-neutrality, the 4090 wins. If you want 24 GB at the lowest sustained running cost, the B70 wins.
Power draw at 100% load?
200 W. The lowest TDP-per-VRAM ratio in our entire catalogue — easily cooled in a 2U chassis, ideal for dense racks.
Same-day deployment?
Yes for in-stock SKUs. Out-of-stock B70 lead time is 3–5 working days — Intel pro cards are less common in the channel than NVIDIA equivalents.
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Want 24 GB without the power bill or the CUDA lock-in? The B70 is your card.
24 GB GDDR6, 200 W TDP, oneAPI / OpenVINO / IPEX out of the box. From £179/mo with same-day deployment on in-stock SKUs.
