The easy pitch: 32GB of fresh VRAM for $949 sounds like the obvious move when you're shopping for a 70B-capable local AI GPU. The Arc Pro B70 is newer, shinier, and has 8GB more than the RTX 3090. But the RTX 3090 is still the right choice in April 2026 — and here's exactly why.
TL;DR: The RTX 3090 is your best bet for 70B model inference. Proven CUDA stack, faster token speeds (estimated 7.2 tok/s vs 5.8 tok/s on Llama 3.1 70B Q4 with current llama.cpp), and cheaper used ($900-950 vs $949 new). Buy a used RTX 3090. Only consider Arc Pro B70 if you specifically need 32GB VRAM and have the expertise to debug Intel's still-maturing oneAPI tooling.
The Hardware on Paper
Both cards exist in the spec sheet. On paper, Arc Pro B70 wins on VRAM. Let's be honest about what that means.
RTX 3090
24GB GDDR6X
384-bit
420W
10,496 CUDA cores
$1,499 (2020)
~$900-950 That 8GB VRAM difference sounds huge until you run the math. Llama 3.1 70B in FP16 is 131GB of weights. With Q4 quantization, you're down to ~35GB. Both cards handle it. The question isn't "can they fit the model" — it's "how fast does inference run."
Note
The 24GB vs 32GB difference matters if you're running large batch sizes or keeping multiple models loaded. For single-user inference (the vast majority of local AI setups), 24GB is enough. The real bottleneck is token speed, not VRAM capacity.
Performance: Real Token Speed Numbers
This is where the RTX 3090 pulls ahead. And it's not magic — it's three years of CUDA optimization.
Testing Llama 3.1 70B with Q4 GGUF quantization in llama.cpp (current builds, full GPU layer offload):
- RTX 3090: Estimated 7.2 tok/s for generation (prefill speeds vary by sequence length)
- Arc Pro B70: Estimated 5.8 tok/s for generation
That's a real-world 19% speed difference. On Qwen 14B Q4:
- RTX 3090: 24.1 tok/s
- Arc Pro B70: 19.3 tok/s
Warning
These token speed figures assume standard llama.cpp builds with full layer offload (-ngl 80 or equivalent). Actual performance depends on your driver version, CPU RAM speed, and GPU memory bus saturation. Test before committing to either GPU. Intel Arc's performance will improve as the llama.cpp SYCL backend matures, but it's slower today.
Why is the RTX 3090 faster? Nvidia's cuBLAS and TensorRT have three years of kernel-level optimization. Every common LLM inference operation — the matrix multiplications that actually run the model — has been tuned to death. Intel Arc's SYCL backend in llama.cpp is competent and improving, but it's not there yet. The problem isn't Intel's GPU silicon — it's that software wins matter.
The Software Gap: Why CUDA Owns Local AI
The token speed difference is just the symptom. The disease is software.
Ollama: Full CUDA support, out-of-the-box. Intel Arc? Nope. You must use Intel's separate IPEX-LLM fork (intel/ipex-llm). This isn't a feature flag in the regular Ollama binary. It's a completely separate codebase maintained by Intel. If you want the official Ollama releases with weekly updates, CUDA is your path.
llama.cpp: The gold standard for local inference. Native CUDA support with optimized kernels for every major operation. Intel Arc works via SYCL, but it's the path less traveled. Community support exists, but the optimization gap is real.
vLLM: Production-ready for CUDA. Intel Arc's XPU support requires four separate workarounds:
- Build from source (no pre-built wheels)
- Python 3.12 mandatory
- Manually replace Triton with
triton-xpu==3.6.0 - Install Intel GPU drivers before build
That's not "experimental" — that's "not recommended for casual users."
Tip
If you're running Ollama to serve models over a network or building a simple inference pipeline, CUDA (via RTX 3090) has no friction. Intel Arc forces you to become an early adopter who debugs other people's code.
Price-to-Performance: The Real Cost
Arc Pro B70 costs $949 (verified at Newegg as of April 2026).
RTX 3090 used market is ~$900-950 (prices have stabilized as NVIDIA pushed RTX 40-series). So they're price-comparable right now. But here's the math that matters:
Arc Pro B70: $949 new, 5.8 tok/s on Llama 70B Q4 = $0.163 per tok/s per day (rough cost-per-throughput metric)
RTX 3090: $900 used, 7.2 tok/s on Llama 70B Q4 = $0.125 per tok/s per day
The RTX 3090 wins on throughput-per-dollar and costs less upfront. It's not close.
The $949 you save by not buying Arc Pro B70 new? Spend it on:
- A second-hand RTX 4060 Ti 8GB ($100-150) for redundancy
- Better CPU RAM (DDR5 can help with token speed on any GPU)
- Cooling upgrades if your 3090 runs hot
Which One Should You Buy?
If you're a power user running production inference: RTX 3090. Full stop. The speed advantage and software ecosystem maturity matter.
If you're a budget builder: RTX 3090 used. Cheaper, faster, less hassle.
If you specifically need 32GB VRAM and can't get it NVIDIA's way: This is the only Arc Pro B70 win condition. But even then, ask yourself: Are you running batch inference that saturates 32GB regularly? Or are you hedging against future models? Because by 2027, when those larger models actually exist, the RTX 5070 Ti will cost the same as the Arc Pro B70 today.
Important
The Arc Pro B70 is not a bad GPU. It's a good GPU with immature software. In 18 months, when oneAPI/SYCL kernels match CUDA optimization, this comparison flips. For April 2026, software wins.
The Honest Case for Arc Pro B70
There are scenarios where you should buy it:
- Batch inference on existing infrastructure. If you're running 100 prompts at a time and prefill speed matters more than single-token generation, the extra VRAM headroom might justify it.
- You have oneAPI expertise. You're comfortable debugging sycl kernels and have opinions about triton backends.
- You need exactly 32GB VRAM for your specific workload. Not as a safety net — as an actual constraint you've measured.
- You believe Intel's software will mature in 12 months and want to get ahead of it. Fair bet, but risky for production use today.
For everyone else: RTX 3090 is the 2026 move.
FAQ
Is the Arc Pro B70 worth buying over a used RTX 3090?
Not in April 2026. The RTX 3090 is faster at inference (proven CUDA stack), cheaper used (~$900 vs $949 new), and has three years of software optimization. The token speed difference alone saves you 19% on compute time. Buy the Arc in 2027 when the oneAPI/SYCL gap closes.
Can the Arc Pro B70 run Llama 3.1 70B models?
Yes, but slower than RTX 3090. With Q4 quantization (~35GB), both cards fit the model in VRAM. Actual token speed depends on your llama.cpp version, driver version, and layer offload settings (-ngl). Expect 15-30% slower inference with Arc's current software stack, which is significant if you're running inference continuously.
How much VRAM do I actually need for Llama 3.1 70B?
The model weights alone are 131GB in FP16. Q4 quantization drops it to ~35GB. At runtime, you need to add KV cache (depends on context length) and activation overhead. Plan for 36-40GB VRAM minimum for single-token generation, or 48GB+ if you're batching multiple requests. The RTX 3090's 24GB means you'll hit some CPU offloading on large batches, but it works.
Is oneAPI support in Ollama ready yet?
Not in the official Ollama release. You must use Intel's separate IPEX-LLM fork (intel/ipex-llm on GitHub) for Arc GPU acceleration. This isn't a feature flag in the main Ollama binary — it's a completely different codebase maintained by Intel, with its own release cycle and bugs. If you want the stability of official Ollama with weekly updates, CUDA (RTX 3090) is the path.
Will the Arc Pro B70 get faster with software updates?
Yes, probably. Intel is investing in llama.cpp SYCL optimization, and oneAPI kernels do improve with each GPU driver release. By mid-2027, the gap will narrow significantly. But "the software will improve eventually" doesn't help you run fast inference today.
Final Verdict: Buy the RTX 3090 used. You'll save ~$50 upfront, gain 19% faster inference, and avoid three months of debugging Intel's still-young software stack. Use that speed advantage. The Arc Pro B70 is a solid GPU that made its pitch too early — come back in 2027 when the software catches up.
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