b70-optimization-lab

GPU Comparison For Local AI

This page gives a practical buying and deployment frame for B70-based local inference. Prices move quickly; treat MSRP and street price ranges as dated notes, not procurement quotes.

Last updated: 2026-05-23.

Summary

The B70 is interesting because it offers 32 GB of VRAM per card at a much lower price than traditional workstation GPUs. The tradeoff is software maturity: NVIDIA remains easier for most LLM tooling, while B70/XPU can require exact driver, compiler, PyTorch, and kernel combinations.

For local LLMs, the first question is usually not raw TFLOPS. It is:

  1. Does the model fit in VRAM?
  2. Does the backend support the GPU well?
  3. Does multi-GPU splitting preserve quality?
  4. Can the result be reproduced by someone else?

Rough Comparison

GPU VRAM Bandwidth Board Power Price Anchor Local AI Take
Intel Arc Pro B70 32 GB GDDR6 ECC 608 GB/s 160-290 W $949 MSRP Strong VRAM/$; promising for local inference; software path still young.
NVIDIA RTX 3090 24 GB GDDR6X 936 GB/s 350 W $1,499 launch MSRP; used market varies Mature CUDA ecosystem; less VRAM per card than B70; used cards can be good value but condition varies.
NVIDIA RTX 4090 24 GB GDDR6X 1008 GB/s 450 W $1,599 launch MSRP; street price varies Very fast single-card inference; 24 GB VRAM is the limiting factor for larger local models.
NVIDIA RTX 6000 Ada 48 GB GDDR6 ECC 960 GB/s 300 W high workstation pricing Much easier pro CUDA path and 48 GB VRAM, but cost is in another class.

Interpreting Price

Use three different price concepts:

As of public reporting in 2026:

Performance Caveats

Do not compare “tok/s” without workload context.

A useful benchmark line includes:

For example, the current fresh MiniMax deployment reports:

That is not directly comparable to single-GPU 7B tests, chat UI subjective speed, MLPerf Client numbers, or synthetic prefill-only numbers.

The current 4x B70 host appears limited by PCIe4 fabric versus an earlier PCIe5 host. In lay terms, PCIe5 x16 can move about twice as much data per second as PCIe4 x16. The measured 256 MiB allreduce bandwidth was also almost exactly half: 13.79 GB/s current versus 27.88 GB/s older reference. For multi-GPU tensor parallel inference, that can matter because cards must exchange small pieces of the calculation repeatedly during decode.

B70 Strengths

B70 Weak Spots Today

3090 Strengths

3090 Weak Spots

Practical Recommendation

Choose B70 when:

Choose NVIDIA when:

Two B70s Versus Four B70s

Two B70s are the practical community build:

Four B70s are the lab build:

Do not assume four cards beat two cards for every model. Measure it.

Sources