Intel's B70 GPU: Where Hardware Promise Meets

Here's what nobody tells you about GPU benchmarks: the hardware specifications are the easy part. It's the software layer that determines whether your $900 GPU performs like a $1,700 one—or like a $300 disappointment.

Alex [Ziskind just put Intel's new Arc Pro B70 through exhaustive testing against Nvidia's RTX Pro 4000 and AMD's Radeon AI R9700. The results illuminate something more interesting than simple winner/loser declarations: the messy, uneven terrain where hardware capabilities collide with software maturity.

The Numbers That Don't Add Up

On paper, this shouldn't be close. The B70 costs under $1,000 and offers 32GB of VRAM. The Nvidia RTX Pro 4000 costs $1,699 for 24GB of VRAM with GDDR7 memory and 672 GB/s bandwidth—nearly ten times the B70's 68 GB/s. The AMD R9700 matches the B70's 32GB at $1,300, with 640 GB/s bandwidth.

Bandwidth matters for token generation in AI workloads. More bandwidth means faster memory access, which directly translates to faster output. So the Nvidia card should demolish Intel's offering, right?

Ziskind's benchmarks tell a different story. Running the Qwen 34B model in full BF-16 precision through vLLM, the B70 delivered 56 tokens per second for generation compared to the RTX 4000's 51. Prompt processing showed similar results: 12,910 tokens per second on the B70 versus 11,745 on the Nvidia card.

"I swear I'm not trying to make the B70 look better," Ziskind notes in the video, "but compared to this GPU, the 4000, it's slightly better in performance, which is something I did not expect."

The AMD card? It underperformed both, despite having comparable specs to the B70 and superior bandwidth.

The Quantization Variable

Before we crown Intel the budget champion, context matters. Switch to AWQ (Activation-Aware Weight Quantization)—a 4-bit quantization method that preserves important model weights—and the results flip. The Nvidia card suddenly pulls ahead: 89 tokens per second versus the B70's 72. At higher concurrency (simulating multiple users or agentic workflows), that gap widens: 275 tokens per second on Nvidia versus 236 on Intel.

The AMD card's performance with AWQ quantization collapsed entirely: 25 tokens per second for generation. That's not a typo.

What's happening here isn't mysterious—it's the software stack doing exactly what it was designed to do. Nvidia's CUDA ecosystem has been refined over years with specific optimizations for different model architectures and quantization methods. Intel's software tools work well for certain configurations. AMD's ROCm stack, while improving, still struggles with edge cases.

Software as the Real Bottleneck

Ziskind's image generation tests reinforced this pattern. Using Comfy UI to generate a 1328x1328 image, the AMD R9700 completed the task in 133 seconds versus the B70's 147 seconds—but here's the catch: the Intel card was running Comfy UI version 0.8.2 while AMD ran version 0.18.1.

"The B70 is limited to what's available in the LLM scaler vLLM Omni package," Ziskind explains. That package includes Intel-specific patches and custom nodes, but it means you're not running the latest Comfy UI features. AMD's ROCm, for all its issues, works through PyTorch's existing HIP/CUDA compatibility layer, making it easier to use current software versions.

This creates an uncomfortable trade-off: better raw performance in specific configurations versus broader software compatibility. Neither Intel nor AMD can match Nvidia's "it just works" status across the ecosystem.

The Multi-GPU Reality Check

Ziskind's test of four B70 GPUs together (128GB total VRAM for under $4,000—the cost of a single RTX 5090) revealed another software limitation. Prompt processing scaled well, jumping from 9,281 tokens per second on one GPU to 18,170 on four. But token generation actually dropped from 72 to 52 tokens per second.

The culprit: PCIe bandwidth limitations between GPUs. While each B70 has 68 GB/s of memory bandwidth, GPU-to-GPU communication maxes out at 63 GB/s over PCIe Gen 5. For smaller models that fit on a single GPU, adding more cards introduces overhead without benefit.

"That's why on smaller models, we're not going to see an increase. We might even see a decrease like we're seeing right now," Ziskind notes.

This matters for anyone planning a multi-GPU setup. More cards only help if your workload actually needs distributed memory—running massive models or high-concurrency scenarios. For single-user local AI work, you're often better with one powerful GPU than multiple weaker ones.

What This Means for Buyers

If you're evaluating these cards, the question isn't "which hardware is better?" It's "which software stack matches my actual workload?"

For production environments running optimized inference servers with tested models, the B70 delivers remarkable value. You get 32GB of VRAM for $900, and if you're using vLLM with models it handles well, performance matches or exceeds cards costing twice as much.

For experimentation, bleeding-edge models, or workflows requiring the latest software features, Nvidia's premium pricing buys you something real: compatibility. When a new framework drops, it supports CUDA first. When a model gets optimized, CUDA gets the optimization first.

AMD occupies an uncomfortable middle ground: not quite as cheap as Intel, not quite as broadly supported as Nvidia. The R9700's poor AWQ performance and inconsistent results across workloads make it hard to recommend unless you have specific ROCm requirements.

The Database That Doesn't Exist

Midway through testing, Ziskind raises a question I find more interesting than any benchmark: "I do wonder if we need some kind of global database that'll have all this information available."

He's right to wonder. The AI hardware landscape has become impossibly complex. Performance varies not just by GPU, but by software stack, model architecture, quantization method, concurrency level, and a dozen other variables. No single benchmark captures this complexity.

What we need—and don't have—is a comprehensive, community-maintained database mapping GPUs to workloads to actual measured performance. Not synthetic benchmarks, but real tasks: "Run Qwen 34B at Q4 on this hardware with this software, here's what you get." Until that exists, every purchase is partially a gamble.

The B70 performs better than its price suggests, worse than its specifications promise, and differently depending on how you use it. Which is to say: it's exactly like every other GPU on the market, just with a different set of trade-offs.

Rachel 'Rach' Kovacs covers cybersecurity, privacy, and digital safety for Buzzrag.