#benchmarks

A year ago Intel Arc was still a punchline for AI workloads. The Battlemage refresh (B580/B60) is the thing that changed that — and a year of driver, backend, and ecosystem work has made it genuinely good, with caveats. Here's where Arc actually stands for local AI in mid-2026.

In 2024, Q4_K_M meant 'noticeably broken.' In 2026 it's the practical default. We ran the Q4 vs Q6 vs Q8 sweep on Qwen3-Coder-30B — here's the measured quality delta, the VRAM math, and when you actually should step up.

Every model worth running locally in 2026 — Qwen3-30B-A3B, LFM2.5-8B-A1B, the GLM/Kimi frontier — is MoE. Here's what 'active parameters' actually means, why it's the reason these models fit on your card, and the measured 2.5× speed data behind it.

We took Qwen3.6-27B at Q4_K_M — byte-identical weights, verified by Ollama digest — and ran it on all four lab machines: dual 12GB NVIDIA, single 20GB AMD, dual 32GB AMD, and a 24GB Intel Arc. The three CUDA/ROCm machines landed within 10% of each other. The honest answer to 'do I need two GPUs?' is: only once the model stops fitting in one.

We ran the same two 8B models on both cards with byte-identical weights and identical settings. The RX 7900 XT is ~3.9x faster — consistently across both architectures. Here's the full 2x2, what it actually tells you, and where we got the framing wrong the first time.

We benchmarked OpenVINO Model Server 2026.2.1 against llama.cpp Vulkan on the same Intel Arc Pro B60. OpenVINO is ~1.76x faster on a 30B-A3B model — and it works cleanly, contradicting our own earlier note that Battlemage was Vulkan-only. Here's the data and the correction.

Yes, really. A Q4_K_M 30B coder model fits in 12GB of VRAM and runs at usable speed on a single Intel Arc B580. Here's the recipe — with the numbers to back it.