Intel Arc 140V (32GB)
The Intel Arc 140V (32GB) has 32 GB VRAM and 137 GB/s memory bandwidth. It can run 0 of our 71 tracked models natively in VRAM at 8k context.
With 32 GB LPDDR5X, the Intel Arc 140V (32GB) is a integrated-tier GPU that can run 0 models natively. It handles 70B-class models at Q4 quantization.
The Intel Arc 140V is the flagship Lunar Lake integrated GPU, built on the Xe2 (Battlemage) architecture and sharing 32GB of on-package LPDDR5X with the Core Ultra 200V CPU. At 137 GB/s it's bandwidth-constrained compared to discrete cards, but the 32GB unified pool lets it load 13B models at Q8 or 30B models at Q4 — making it one of the most capable laptop iGPUs for local LLM inference.
Intel Arc 140V (32GB): 2024 Xe2-LPG Battlemage iGPU with 32GB unified LPDDR5X at 137 GB/s — top Lunar Lake AI laptop iGPU.
13B at Q8 or 30B at Q4 in unified memory. ~3-6 t/s for 7B via Vulkan; bandwidth-constrained.
Vulkan via llama.cpp works; shares on-package memory with CPU. SYCL backend available with oneAPI. Ollama support limited.
| Vendor | Intel |
| Architecture | Xe2-LPG (Battlemage) |
| VRAM | 32 GB (unified) |
| Memory type | LPDDR5X |
| Memory bandwidth | 137 GB/s |
| Compute backend | VULKAN |
| Tier | Integrated |
| Released | 2024 |
| Models (native) | 0 / 71 |
| Models (offload) | 0 / 71 |
Models this GPU runs natively in VRAM (0)
None.
Too large for this GPU (71)
- Llama 3.3 70B Instruct
- Llama 3.1 8B Instruct
- Llama 3.2 3B Instruct
- Llama 3.2 1B Instruct
- Qwen 2.5 72B Instruct
- Qwen 2.5 32B Instruct
- Qwen 2.5 14B Instruct
- Qwen 2.5 7B Instruct
- Qwen 2.5 3B Instruct
- Qwen 2.5 Coder 32B Instruct
- Mistral Small 22B
- Mistral Nemo 12B Instruct
- Mistral 7B Instruct v0.3
- Mixtral 8x7B Instruct v0.1
- Gemma 2 27B Instruct
- Gemma 2 9B Instruct
- Gemma 2 2B Instruct
- Phi-3.5 Mini Instruct
- DeepSeek R1 Distill Llama 70B
- DeepSeek R1 Distill Qwen 32B
- DeepSeek R1 Distill Llama 8B
- Command-R 35B
- Yi 1.5 34B Chat
- SmolLM2 1.7B Instruct
- SmolLM2 360M Instruct
- Llama 3.1 70B Instruct
- Qwen 2.5 1.5B Instruct
- Qwen 2.5 0.5B Instruct
- Mixtral 8x22B Instruct v0.1
- Llama 3.1 405B Instruct
- Phi-4 14B Instruct
- DeepSeek V3 671B
- DeepSeek R1 671B
- Mistral Small 3.1 24B Instruct
- Gemma 3 27B Instruct
- Gemma 3 12B Instruct
- Gemma 3 4B Instruct
- Gemma 3 1B Instruct
- Llama 4 Scout 109B
- Llama 4 Maverick 400B
- Qwen3 235B-A22B (MoE)
- Qwen3 30B-A3B (MoE)
- Qwen3 32B
- Qwen3 14B
- Qwen3 8B
- MiniMax M1 456B
- GPT-OSS 120B
- GPT-OSS 20B
- GLM-4.5 355B
- GLM-4.5 Air 106B
- GLM-4.6 355B
- GLM-4.6V 106B
- Nemotron 3 Nano 30B
- GLM-4.7 358B
- Gemma 4 31B
- Gemma 4 26B (MoE)
- Gemma 4 E4B
- Gemma 4 E2B
- Qwen 3.5 35B-A3B (MoE)
- Qwen 3.5 122B-A10B (MoE)
- MiniMax M2.5 229B
- GLM-5 744B
- MiniMax M2.7 229B
- Nemotron 3 Super 120B
- Qwen 3.6 27B
- Qwen 3.6 35B
- Kimi K2.6
- GLM-5.1 754B
- DeepSeek V4 Pro 1.6T
- DeepSeek V4 Flash 284B
- GLM-5.2 753B
Frequently asked questions
- How much VRAM does the Intel Arc 140V (32GB) have?
- The Intel Arc 140V (32GB) has 32 GB of LPDDR5X with 137 GB/s memory bandwidth (unified system memory, shared between CPU and GPU).
- What is the Intel Arc 140V (32GB) best for?
- With 32 GB of VRAM, the Intel Arc 140V (32GB) is well-suited for running 7B–32B models at Q4 with room for context, making it a great all-rounder for local LLM inference.
- What LLMs can the Intel Arc 140V (32GB) run locally?
- The Intel Arc 140V (32GB) cannot run any of the 71 tracked models fully in VRAM at 8k context. It may handle smaller models with CPU offload.
- Can the Intel Arc 140V (32GB) run Llama 3.3 70B Instruct?
- The Intel Arc 140V (32GB) does not have enough VRAM to run Llama 3.3 70B Instruct. You would need more VRAM or a lower quantization level.
- Can the Intel Arc 140V (32GB) run Qwen 3.6 27B?
- The Intel Arc 140V (32GB) does not have enough VRAM to run Qwen 3.6 27B. You would need more VRAM or a lower quantization level.
- Can the Intel Arc 140V (32GB) run Llama 3.1 8B Instruct?
- The Intel Arc 140V (32GB) does not have enough VRAM to run Llama 3.1 8B Instruct. You would need more VRAM or a lower quantization level.