# Best Intel GPU for local LLMs: Arc B580, Pro B70, A770 (2026) > Intel Arc GPUs for running LLMs locally: 5 cards by VRAM, bandwidth, list price and est. tok/s, which software works (llama.cpp Vulkan/SYCL, OpenVINO), and the cheapest NVIDIA and AMD card with the same VRAM. Source: https://whatcanirunai.vercel.app/best-intel-gpu-for-local-llm/ # Best Intel GPU for local LLMs Intel Arc cards are some of the cheapest ways to get 12 to 32 GB of VRAM. Here are the 5 Arc GPUs we track, with list prices, estimated tokens per second, which software runs them, and the cheapest NVIDIA and AMD card with the same VRAM. Intel GPUs tracked 5 Still sold new 3 VRAM range 8–32 GB Short answer: the **Arc Pro B70** (32 GB, $949 list) is the best Intel card for local LLMs and fits 27B–32B models at Q4\. The **Arc B580** (12 GB, $249) is the budget pick for 8B–14B models. A used **A770 16GB** is the cheapest way into 16 GB on Intel. **About software:** llama.cpp supports Arc through Vulkan and SYCL ([SYCL backend docs](https://github.com/ggml-org/llama.cpp/blob/master/docs/backend/SYCL.md)); LM Studio and Ollama use Vulkan. Intel's old IPEX-LLM project was [archived on 2026-01-28](https://github.com/intel/ipex-llm) (checked 2026-11-10), so skip guides that rely on it. Prices are list prices (MSRP) or launch MSRPs for discontinued cards, never street prices. ## Our picks Most VRAM: 32 GB for 27B–32B models ### [Intel Arc Pro B70 32GB](https://whatcanirunai.vercel.app/hardware/arc-pro-b70/) VRAM 32 GB Bandwidth 608 GB/s Run at Q4 504 / 656 Battlemage (Xe2). Qwen3-32B at Q4: est. 18–26 tok/s. Biggest at Q4: [IQuest-Coder-V1-40B-Loop-Instruct](https://whatcanirunai.vercel.app/models/iquest-coder-v1-40b-loop-instruct/). $949 (list price (MSRP), [source](https://www.igorslab.de/en/intel-launches-the-arc-pro-b70-with-32-gb-of-vram-for-949-big-battlemage-is-coming-first-for-ai-and-workstations/)) Cheapest GeForce with 32+ GB: GeForce RTX 5090, $1,999 list. [Amazon](https://www.amazon.com/s?k=Intel%20Arc%20Pro%20B70%2032GB&tag=whatcanirunai-20)[eBay](https://www.ebay.com/sch/i.html?%5Fnkw=Intel%20Arc%20Pro%20B70%2032GB) Best budget card: 12 GB ### [Intel Arc B580](https://whatcanirunai.vercel.app/hardware/arc-b580/) VRAM 12 GB Bandwidth 456 GB/s Run at Q4 350 / 656 Battlemage (Xe2). Qwen3-8B at Q4: est. 55–78 tok/s. Biggest at Q4: [OLMo-2-1124-13B-Instruct](https://whatcanirunai.vercel.app/models/olmo-2-1124-13b-instruct/). $249 (list price (MSRP), [source](https://en.wikipedia.org/wiki/Intel%5FArc#Battlemage)) Cheapest GeForce with 12+ GB: GeForce RTX 3060 12GB, $329 list. [Amazon](https://www.amazon.com/s?k=Intel%20Arc%20B580&tag=whatcanirunai-20)[eBay](https://www.ebay.com/sch/i.html?%5Fnkw=Intel%20Arc%20B580) 16 GB on the used market ### [Intel Arc A770 16GB](https://whatcanirunai.vercel.app/hardware/arc-a770-16gb/) VRAM 16 GB Bandwidth 560 GB/s Run at Q4 388 / 656 Alchemist (Xe). Qwen3-14B at Q4: est. 38–53 tok/s. Biggest at Q4: [Ling-Coder-lite](https://whatcanirunai.vercel.app/models/ling-coder-lite/). $349 (launch MSRP, discontinued, [source](https://en.wikipedia.org/wiki/Intel%5FArc)) [Amazon](https://www.amazon.com/s?k=Intel%20Arc%20A770%2016GB&tag=whatcanirunai-20)[eBay](https://www.ebay.com/sch/i.html?%5Fnkw=Intel%20Arc%20A770%2016GB) Cheapest current Arc: 10 GB ### [Intel Arc B570](https://whatcanirunai.vercel.app/hardware/arc-b570/) VRAM 10 GB Bandwidth 380 GB/s Run at Q4 340 / 656 Battlemage (Xe2). Qwen3-8B at Q4: est. 46–65 tok/s. Biggest at Q4: [HyperCLOVAX-SEED-Omni-8B](https://whatcanirunai.vercel.app/models/hyperclovax-seed-omni-8b/). $219 (list price (MSRP), [source](https://en.wikipedia.org/wiki/Intel%5FArc)) Cheapest GeForce with 10+ GB: GeForce RTX 3060 12GB, $329 list. [Amazon](https://www.amazon.com/s?k=Intel%20Arc%20B570&tag=whatcanirunai-20)[eBay](https://www.ebay.com/sch/i.html?%5Fnkw=Intel%20Arc%20B570) We may earn a commission when you buy through these links, at no extra cost to you. ## Every Intel GPU we track Sorted by memory, then bandwidth. Board power is the reference spec ([how we calculate fit](https://whatcanirunai.vercel.app/method/)). __Intel Arc GPUs ranked by VRAM with bandwidth, board power, price, price per GB and the biggest model each runs at Q4__ | GPU | VRAM | Bandwidth | Board power | Price | $ per GB | Biggest model at Q4 | Models that fit | | -------------------------------------------------------------------------------------- | ----- | --------- | ----------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------- | ---------------------------------------------------------------------------------------------- | --------------- | | [Intel Arc Pro B70 32GB](https://whatcanirunai.vercel.app/hardware/arc-pro-b70/)Battlemage (Xe2) | 32 GB | 608 GB/s | 230 W | $949list price (MSRP), [source](https://www.igorslab.de/en/intel-launches-the-arc-pro-b70-with-32-gb-of-vram-for-949-big-battlemage-is-coming-first-for-ai-and-workstations/), 2026-10-29 | $30 | [IQuest-Coder-V1-40B-Loop-Instruct](https://whatcanirunai.vercel.app/models/iquest-coder-v1-40b-loop-instruct/)\~28.7 GB at Q4 | 504 / 656 | | [Intel Arc A770 16GB](https://whatcanirunai.vercel.app/hardware/arc-a770-16gb/)Alchemist (Xe) · discontinued, buy used | 16 GB | 560 GB/s | 225 W | $349launch MSRP, discontinued, [source](https://en.wikipedia.org/wiki/Intel%5FArc), 2026-10-28 | $22 | [Ling-Coder-lite](https://whatcanirunai.vercel.app/models/ling-coder-lite/)\~12.7 GB at Q4 | 388 / 656 | | [Intel Arc B580](https://whatcanirunai.vercel.app/hardware/arc-b580/)Battlemage (Xe2) | 12 GB | 456 GB/s | — | $249list price (MSRP), [source](https://en.wikipedia.org/wiki/Intel%5FArc#Battlemage), 2026-10-22 | $21 | [OLMo-2-1124-13B-Instruct](https://whatcanirunai.vercel.app/models/olmo-2-1124-13b-instruct/)\~10.6 GB at Q4 | 350 / 656 | | [Intel Arc B570](https://whatcanirunai.vercel.app/hardware/arc-b570/)Battlemage (Xe2) | 10 GB | 380 GB/s | 150 W | $219list price (MSRP), [source](https://en.wikipedia.org/wiki/Intel%5FArc), 2026-10-28 | $22 | [HyperCLOVAX-SEED-Omni-8B](https://whatcanirunai.vercel.app/models/hyperclovax-seed-omni-8b/)\~8.5 GB at Q4 | 340 / 656 | | [Intel Arc A750](https://whatcanirunai.vercel.app/hardware/arc-a750/)Alchemist (Xe) · discontinued, buy used | 8 GB | 512 GB/s | 225 W | $289launch MSRP, discontinued, [source](https://en.wikipedia.org/wiki/Intel%5FArc), 2026-10-28 | $36 | [NVIDIA-Nemotron-Nano-9B-v2](https://whatcanirunai.vercel.app/models/nvidia-nemotron-nano-9b-v2/)\~7.2 GB at Q4 | 315 / 656 | ## Which popular models run, and how fast Estimated decode speed at Q4 in tokens per second ([how we estimate speed](https://whatcanirunai.vercel.app/method/#speed)); MoE models show an upper bound only. Real Arc speeds depend on the backend (Vulkan or SYCL) and driver version. __Estimated fit and decode speed at Q4 for popular models on each Intel Arc GPU__ | Intel GPU | [Llama-3.1-8B-Instruct](https://whatcanirunai.vercel.app/models/llama-3.1-8b-instruct/)\~6.6 GB | [Qwen3-8B](https://whatcanirunai.vercel.app/models/qwen3-8b/)\~6.7 GB | [Qwen3-14B](https://whatcanirunai.vercel.app/models/qwen3-14b/)\~11.3 GB | [gpt-oss-20b](https://whatcanirunai.vercel.app/models/gpt-oss-20b/)\~15.6 GB | [Qwen3-30B-A3B](https://whatcanirunai.vercel.app/models/qwen3-30b-a3b/)\~22.3 GB | [Qwen3.8-27B](https://whatcanirunai.vercel.app/models/qwen3.8-27b/)\~20.4 GB | [Qwen3-32B](https://whatcanirunai.vercel.app/models/qwen3-32b/)\~23.8 GB | | ---------------------------------------------------------------- | --------------------------------------------------------------- | ------------------------------------- | ---------------------------------------- | -------------------------------------------- | ------------------------------------------------ | -------------------------------------------- | ---------------------------------------- | | [Intel Arc Pro B70 32GB](https://whatcanirunai.vercel.app/hardware/arc-pro-b70/)32 GB · 608 GB/s | Fits75–106 tok/s | Fits73–104 tok/s | Fits41–58 tok/s | Fitsup to 237 tok/s | Fitsup to 284 tok/s | Fits22–31 tok/s | Fits18–26 tok/s | | [Intel Arc A770 16GB](https://whatcanirunai.vercel.app/hardware/arc-a770-16gb/)16 GB · 560 GB/s | Fits69–98 tok/s | Fits68–96 tok/s | Fits38–53 tok/s | Tightup to 218 tok/s | Won’t fit | Won’t fit | Won’t fit | | [Intel Arc B580](https://whatcanirunai.vercel.app/hardware/arc-b580/)12 GB · 456 GB/s | Fits56–80 tok/s | Fits55–78 tok/s | Tight31–43 tok/s | Won’t fit | Won’t fit | Won’t fit | Won’t fit | | [Intel Arc B570](https://whatcanirunai.vercel.app/hardware/arc-b570/)10 GB · 380 GB/s | Fits47–66 tok/s | Fits46–65 tok/s | Won’t fit | Won’t fit | Won’t fit | Won’t fit | Won’t fit | | [Intel Arc A750](https://whatcanirunai.vercel.app/hardware/arc-a750/)8 GB · 512 GB/s | Fits63–89 tok/s | Fits62–88 tok/s | Won’t fit | Won’t fit | Won’t fit | Won’t fit | Won’t fit | ## Intel vs NVIDIA vs AMD at the same VRAM For each memory size Intel still sells: the cheapest current Arc, GeForce and Radeon card we track with at least that much VRAM, all at list price (MSRP). __Cheapest current Intel, NVIDIA and AMD GPU at each VRAM size, at list price__ | VRAM | Cheapest Intel | Cheapest NVIDIA | Cheapest AMD | | ------ | ---------------------------------------------------------------------------- | ----------------------------------------------------------------------------- | ---------------------------------------------------------------------- | | 10+ GB | [Intel Arc B570](https://whatcanirunai.vercel.app/hardware/arc-b570/)10 GB · 380 GB/s · $219 list | [GeForce RTX 3060 12GB](https://whatcanirunai.vercel.app/hardware/rtx-3060-12gb/)12 GB · 360 GB/s · $329 list | [Radeon RX 7600 XT](https://whatcanirunai.vercel.app/hardware/rx-7600-xt/)16 GB · 288 GB/s · $329 list | | 12+ GB | [Intel Arc B580](https://whatcanirunai.vercel.app/hardware/arc-b580/)12 GB · 456 GB/s · $249 list | [GeForce RTX 3060 12GB](https://whatcanirunai.vercel.app/hardware/rtx-3060-12gb/)12 GB · 360 GB/s · $329 list | [Radeon RX 7600 XT](https://whatcanirunai.vercel.app/hardware/rx-7600-xt/)16 GB · 288 GB/s · $329 list | | 32+ GB | [Intel Arc Pro B70 32GB](https://whatcanirunai.vercel.app/hardware/arc-pro-b70/)32 GB · 608 GB/s · $949 list | [GeForce RTX 5090](https://whatcanirunai.vercel.app/hardware/rtx-5090/)32 GB · 1792 GB/s · $1,999 list | None we track | ## Before you buy Intel * **Start with Vulkan.** LM Studio and Ollama work with just the GPU driver. Try llama.cpp's SYCL build (oneAPI) if you need faster prompt processing on long inputs. * **Keep drivers current.** Arc performance in llama.cpp has improved a lot with newer drivers and builds; old benchmarks undersell the cards. * **Enable Resizable BAR.** Intel recommends it for Arc cards; check your motherboard BIOS. * **Fine-tuning and CUDA-only tools** mostly target NVIDIA. If you train models or use bleeding-edge inference kernels, see the [best GPU](https://whatcanirunai.vercel.app/best-gpu-for-local-llm/) guide. * **Power and fit:** check board power and case size in the [planner](https://whatcanirunai.vercel.app/planner/). Head-to-head: [Arc B580 vs RTX 3060 12GB](https://whatcanirunai.vercel.app/compare/arc-b580-vs-rtx-3060-12gb/) · [Arc A770 vs RTX 4060 Ti 16GB](https://whatcanirunai.vercel.app/compare/arc-a770-16gb-vs-rtx-4060-ti-16gb/) · [Arc Pro B70 vs RTX 3090](https://whatcanirunai.vercel.app/compare/arc-pro-b70-vs-rtx-3090/) · [Arc Pro B70 vs RX 7900 XTX](https://whatcanirunai.vercel.app/compare/arc-pro-b70-vs-rx-7900-xtx/) · [Arc Pro B70 vs RTX 5090](https://whatcanirunai.vercel.app/compare/arc-pro-b70-vs-rtx-5090/). ## Quick answers What is the best Intel GPU for local LLMs? For the most models, the Intel Arc Pro B70 32GB: 32 GB of VRAM and 608 GB/s for $949 list, enough for 27B–32B-class models at Q4 (Qwen3.8-27B at an estimated 22–31 tok/s). The cheapest current GeForce with 32 GB is the GeForce RTX 5090 at $1,999 list. On a budget, the Intel Arc B580 (12 GB, $249 list) runs 8B–14B models. Do Intel Arc GPUs work for running LLMs? Yes. llama.cpp runs on Arc through its Vulkan backend (what LM Studio and Ollama use) and its SYCL backend (Intel's oneAPI path, which lists the Arc A770, A750 and B580 as verified devices). Intel's OpenVINO GenAI and vLLM's Intel GPU (XPU) backend are other options. Expect fewer tools and tutorials than for NVIDIA, and more sensitivity to driver versions. Should I use IPEX-LLM? Not for new setups. Intel archived the [IPEX-LLM repository](https://github.com/intel/ipex-llm) on January 28, 2026, so it no longer gets updates. Many 2024–2025 guides still point to it. Use llama.cpp (Vulkan or SYCL), LM Studio, Ollama or OpenVINO instead. Is the Arc B580 good for local LLMs? For the price, yes: 12 GB and 456 GB/s for $249 list, enough for 8B models with room for context (Qwen3-8B at an estimated 55–78 tok/s at Q4). 14B models fit at Q4 too, but only with a short context. The cheapest 12 GB GeForce we track is the GeForce RTX 3060 12GB at $329 list. Is a used Arc A770 16GB worth it? It has 16 GB and 560 GB/s, more than the B580, so it fits 14B models with more context. It's discontinued (launch MSRP was $349), so you'll be buying used. The older Alchemist chip idles at higher power and gets less tuning in llama.cpp than Battlemage. New, the cheapest 16 GB GeForce we track is the GeForce RTX 5060 Ti 16GB at $429 list. Intel, AMD or NVIDIA for local AI? Intel and AMD give more VRAM per dollar; NVIDIA has the widest software support (CUDA, FlashAttention, most fine-tuning tools). For chat and coding in llama.cpp-based apps, all three work. Intel's 32 GB Arc Pro B70 is one of the cheapest ways to get 32 GB on a single new card. See [NVIDIA vs AMD vs Mac](https://whatcanirunai.vercel.app/nvidia-vs-amd-vs-mac/) and the [best AMD GPU](https://whatcanirunai.vercel.app/best-amd-gpu-for-local-llm/) guide. [Buying guides](https://whatcanirunai.vercel.app/best-gpu-for/) · [Best GPU (all brands)](https://whatcanirunai.vercel.app/best-gpu-for-local-llm/) · [Best AMD GPU](https://whatcanirunai.vercel.app/best-amd-gpu-for-local-llm/) · [Best used GPU](https://whatcanirunai.vercel.app/best-used-gpu-for-local-llm/) · [Best GPU by budget](https://whatcanirunai.vercel.app/best-gpu-under/) · [Tokens per second](https://whatcanirunai.vercel.app/tokens-per-second/) --- Agent guide: https://whatcanirunai.vercel.app/llms.txt · JSON API: https://whatcanirunai.vercel.app/developers/