Best AI Models for Intel Core Ultra 9 288V (Lunar Lake) Laptop
32 GB total — ~24 GB usable as VRAM
The 48-TOPS NPU is NOT used by llama.cpp — inference runs on the Arc 140V iGPU via IPEX-LLM and is bound by ~137 GB/s bandwidth; soldered 32 GB caps model size.
32 GB positions this hardware in the professional tier for local AI. Most popular open-source models run comfortably, and even large 70B parameter models are accessible at lower quantization levels.
This memory amount is a sweet spot for enthusiasts and professionals. You can run 13B–30B models like DeepSeek R1 Distill at Q5 or Q6 quality with smooth token generation, and 7B models at near-lossless precision. The 70B class of models (Llama 3 70B, Qwen 72B) becomes possible at Q2–Q3 quantization, though with some quality trade-off. For day-to-day use with coding assistants, chat models, and reasoning tasks, this tier delivers an excellent experience.
Runs Well
- 7B–13B models at Q6–Q8 quality
- 14B–30B models at Q4–Q5 quality
- Small models (3B–7B) at FP16 precision
- Vision-language models at good quality
Challenging
- 70B models only at Q2–Q3 (noticeable quality loss)
- Large context windows with 30B+ models
What LLMs Can Intel Core Ultra 9 288V (Lunar Lake) Laptop Run?
105 models · 7 excellent · 15 good
Showing compatibility for Intel Core Ultra 9 288V (Lunar Lake) Laptop
| Model | Quant | VRAM | Speed | Context | Status | Grade |
|---|---|---|---|---|---|---|
Q4_K_M·13.7 t/s tok/s·33K ctx·EASY RUN | Q4_K_M | 5.0 GB | 13.7 t/s | 33K | EASY RUN | C36 |
Q4_K_M·12.8 t/s tok/s·131K ctx·EASY RUN | Q4_K_M | 5.3 GB | 12.8 t/s | 131K | EASY RUN | C37 |
Q4_K_M·9.7 t/s tok/s·EASY RUN | Q4_K_M | 7.0 GB | 9.7 t/s | — | EASY RUN | C44 |
Q4_K_M·30.6 t/s tok/s·33K ctx·EASY RUN | Q4_K_M | 2.2 GB | 30.6 t/s | 33K | EASY RUN | C30 |
Q4_K_M·23.5 t/s tok/s·262K ctx·EASY RUN | Q4_K_M | 2.9 GB | 23.5 t/s | 262K | EASY RUN | C31 |
Q4_K_M·8.0 t/s tok/s·FAIR FIT | Q4_K_M | 8.6 GB | 8.0 t/s | — | FAIR FIT | B51 |
Q4_K_M·56.4 t/s tok/s·8K ctx·EASY RUN | Q4_K_M | 1.2 GB | 56.4 t/s | 8K | EASY RUN | D28 |
Q4_K_M·8.0 t/s tok/s·2K ctx·FAIR FIT | Q4_K_M | 8.6 GB | 8.0 t/s | 2K | FAIR FIT | B51 |
Q4_K_M·13.9 t/s tok/s·33K ctx·EASY RUN | Q4_K_M | 4.9 GB | 13.9 t/s | 33K | EASY RUN | C36 |
Q4_K_M·29.7 t/s tok/s·66K ctx·EASY RUN | Q4_K_M | 2.3 GB | 29.7 t/s | 66K | EASY RUN | C30 |
Q4_K_M·8.0 t/s tok/s·FAIR FIT | Q4_K_M | 8.6 GB | 8.0 t/s | — | FAIR FIT | B51 |
Q4_K_M·11.2 t/s tok/s·8K ctx·EASY RUN | Q4_K_M | 6.1 GB | 11.2 t/s | 8K | EASY RUN | C40 |
Q4_K_M·24.0 t/s tok/s·EASY RUN | Q4_K_M | 2.8 GB | 24.0 t/s | — | EASY RUN | C31 |
Q4_K_M·19.9 t/s tok/s·131K ctx·EASY RUN | Q4_K_M | 3.4 GB | 19.9 t/s | 131K | EASY RUN | C32 |
Q4_K_M·23.8 t/s tok/s·131K ctx·EASY RUN | Q4_K_M | 2.9 GB | 23.8 t/s | 131K | EASY RUN | C31 |
Q4_K_M·31.3 t/s tok/s·16K ctx·EASY RUN | Q4_K_M | 2.2 GB | 31.3 t/s | 16K | EASY RUN | C30 |
Intel Core Ultra 9 288V (Lunar Lake) Laptop Specifications
- Brand
- Intel
- Chip
- Core Ultra 9 288V
- Type
- Laptop
- Unified Memory
- 32 GB
- Memory Bandwidth
- 136.5 GB/s
- GPU Cores
- 8
- CPU Cores
- 8
- Neural Engine
- 48.0 TOPS
- Memory Type
- LPDDR5X-8533 (on-package)
- TDP
- 30 W
- NPU
- Intel AI Boost NPU 4 (48 TOPS)
- Release Date
- 2024-09-24
Get Started
Devices to Consider
Similar devices and upgrades with more memory or higher bandwidth
Frequently Asked Questions
- Can Intel Core Ultra 9 288V (Lunar Lake) Laptop run Gemma 3 27B IT?
Yes, the Intel Core Ultra 9 288V (Lunar Lake) Laptop with 32 GB unified memory can run Gemma 3 27B IT, Qwen3.6 27B, Gemma 4 26B A4B IT, and 1264 other models. 69 models achieve excellent performance, and 153 run at good quality. Apple Silicon's unified memory architecture lets the GPU access the full memory pool without copying data, making it efficient for AI workloads.
- How much memory is available for AI on Intel Core Ultra 9 288V (Lunar Lake) Laptop?
The Intel Core Ultra 9 288V (Lunar Lake) Laptop has 32 GB unified memory. After macOS reserves ~3.5 GB for the operating system, approximately 28.5 GB is available for AI models. Unlike discrete GPUs where VRAM is separate from system RAM, Apple Silicon shares one memory pool between the CPU and GPU — this means no data copying overhead, but you share memory with macOS and open apps.
- Is Intel Core Ultra 9 288V (Lunar Lake) Laptop good for AI?
With 32 GB unified memory and 136.5 GB/s bandwidth, the Intel Core Ultra 9 288V (Lunar Lake) Laptop is very good for running local AI models. It supports 222 models at good quality or better. This is a strong configuration for AI — 7B models run at maximum quality, and you can comfortably handle 14B models like DeepSeek R1 Distill 14B and larger.
- What's the best model for Intel Core Ultra 9 288V (Lunar Lake) Laptop?
The top-rated models for the Intel Core Ultra 9 288V (Lunar Lake) Laptop are Gemma 3 27B IT, Qwen3.6 27B, Gemma 4 26B A4B IT. For general chat, instruction-tuned 7B models give the best speed-to-quality ratio. For coding or reasoning, a 14B model at Q4_K_M is a sweet spot.
- How fast is Intel Core Ultra 9 288V (Lunar Lake) Laptop for AI inference?
With 136.5 GB/s memory bandwidth, the Intel Core Ultra 9 288V (Lunar Lake) Laptop achieves approximately 21 tok/s on a 7B model at Q4_K_M — that's functional for interactive use. A 14B model runs at ~11 tok/s. Apple Silicon achieves high efficiency (~70%) thanks to unified memory — there's no PCIe bottleneck between CPU and GPU.
tok/s = (136.5 GB/s ÷ model GB) × efficiency
Apple Silicon achieves ~70% bandwidth efficiency thanks to unified memory and Metal acceleration.
Estimated speed on Intel Core Ultra 9 288V (Lunar Lake) Laptop
~4 tok/s~4 tok/s~4 tok/s~4 tok/sReal-world results typically within ±20%.
- Can I run AI offline on Intel Core Ultra 9 288V (Lunar Lake) Laptop?
Yes — once you download a model, it runs entirely on the Intel Core Ultra 9 288V (Lunar Lake) Laptop without internet. Applications like Ollama and LM Studio make it straightforward to download, manage, and run models locally. All your conversations stay private on your device with zero data sent to external servers. This is one of the key advantages of local AI: complete privacy, no API costs, and no rate limits.
- Will Intel Core Ultra 9 288V (Lunar Lake) Laptop throttle or drain the battery running LLMs?
Yes, sustained LLM inference is one of the most thermally demanding workloads a laptop can face — more continuous than most gaming sessions. Plugged in, the Intel Core Ultra 9 288V (Lunar Lake) Laptop will run at full performance but may still throttle if the chassis thermal limit is hit under prolonged load; expect the fans to spin up noticeably. On battery, macOS and the firmware typically cap power draw to protect the cells, which can reduce inference speed by 20–40%. For long sessions (generating documents, batch processing), keep the laptop plugged in, on a hard flat surface with the vents unobstructed, and consider a 7B model at Q4_K_M rather than a larger model — smaller models generate less heat while still giving useful results. Real-world speed on battery is typically within ±20% of the on-charger figure for short bursts, but diverges more over 10+ minutes of continuous generation.
- Anything to watch out for with Intel Core Ultra 9 288V (Lunar Lake) Laptop?
The 48-TOPS NPU is NOT used by llama.cpp — inference runs on the Arc 140V iGPU via IPEX-LLM and is bound by ~137 GB/s bandwidth; soldered 32 GB caps model size.