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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteFor running AI models locally, check memory first: a laptop’s discrete GPU VRAM or, on Apple Silicon, its shared unified memory determines which models can load. Then check the model’s quantization and context length, runtime support, and performance. There is no single memory minimum for every local model or app.
Which laptop spec matters most?
Start with the memory available to the model. On a laptop with a discrete GPU, that usually means the GPU’s dedicated VRAM. On Apple Silicon, the CPU and GPU draw from unified memory, which is also used by the operating system and other apps. In either case, the amount printed on a spec sheet is not necessarily all available for model weights and runtime work.
Memory capacity is a fit question: can the intended model load with its chosen settings? Speed is separate: a model may fit but generate too slowly for comfortable use. Once a configuration has enough usable memory, compare runtime support and workload-specific performance.
How much memory does a local model need?
Model parameter count is only part of the estimate. Precision or quantization changes how many bytes each parameter uses; longer context can add memory demand, and the runtime needs room for other data and buffers. Lenovo’s sizing guide gives this simplified inference estimate:
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Estimated memory = parameter count in billions × bytes per parameter × 1.2
The guide assigns 0.5 bytes per parameter to INT4, 1 byte to FP8/INT8, 2 bytes to FP16, and 4 bytes to FP32. Its 1.2 multiplier represents a 20% allowance for additional data. Lenovo illustrates the formula with a 70-billion-parameter model at FP16: 70 × 2 × 1.2 = 168 GB. That is a planning estimate, not a laptop recommendation or a guarantee for every model and runtime. Lenovo’s LLM sizing guide does not state a publication date in the opened document.
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As a separate rule of thumb, SitePoint estimates that a 7B model at 4-bit precision has about 3.5–4 GB of weights and roughly 5 GB total in its example after overhead. It estimates 35 GB of weights and 40–45 GB including overhead for a 70B 4-bit model. These are approximate examples, not universal requirements: actual model files, context settings, and runtime use vary. SitePoint’s hardware guide provides the examples.
How to compare GPU VRAM with Apple unified memory
Discrete GPU laptops
Check the exact GPU configuration’s dedicated VRAM, not just its GPU family or model name. Two configurations may differ in available graphics memory, and a runtime must also support the GPU and intended model. For a specific laptop, sustained power and cooling can affect performance, but the available evidence does not establish a current laptop ranking or comparable battery and thermal results.
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Apple Silicon laptops
Apple Silicon uses unified memory shared across CPU and GPU work, so the total capacity is not reserved entirely for the model. Ollama’s Apple Silicon preview uses MLX. For the preview’s highlighted Qwen3.5 35B-A3B coding model, Ollama said on March 30, 2026: “Please make sure you have a Mac with more than 32GB of unified memory.” This is specific to that preview and model; it is not a minimum for all local AI workloads. Ollama’s MLX preview announcement gives the recommendation.
Runtime-specific requirements
Requirements vary by runtime and model. OpenJet documents 24 GB or more of unified memory, or 14 GB or more of GPU VRAM, for its managed coding-agent runtime. Those figures apply to that product’s documented use case, not to local inference in general. OpenJet’s hardware guidance describes its requirements.
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What else affects speed?
Memory bandwidth and runtime implementation influence inference speed, so capacity alone cannot predict how responsive a laptop will feel. Compare performance figures only when the model, quantization, context, hardware, and runtime are sufficiently alike.
A 2025 study by Varun Rajesh and coauthors compared MLX, MLC-LLM, Ollama, llama.cpp, and PyTorch MPS on a Mac Studio with an M2 Ultra and 192 GB of unified memory. It used Qwen 2.5 models and prompts up to 100,000 tokens. In that test setup, the authors report that MLX had the highest sustained generation throughput, while MLC-LLM had lower time-to-first-token for moderate prompts. They also report that the Apple Silicon frameworks in their comparison trailed NVIDIA GPU systems such as vLLM in absolute performance. These results describe that workstation, software, models, and settings—not a general laptop benchmark. Read the comparative study.
What to check before choosing a configuration
- Choose the workload. Identify the model or model size, quantization, and context length you intend to use. A laptop that suits one model may not suit another.
- Check usable memory. For a discrete GPU, verify the exact configuration’s dedicated VRAM. For Apple Silicon, consider unified memory shared with the system and other work.
- Confirm runtime support. Check that the runtime supports the laptop’s GPU or Apple Silicon path and the intended model. Treat vendor thresholds as specific to the named runtime and workload.
- Compare speed on like terms. Look for results using a comparable model, quantization, context, hardware, and runtime; otherwise, a throughput comparison may mislead.
- Check the whole laptop configuration. For product-specific evidence, verify cooling and power behavior, battery life, portability, storage for model files, and whether memory or storage can be upgraded. The sources here do not establish comparable figures or a current SKU shortlist for those factors.
Does fine-tuning need different specs?
Yes. Running a pretrained model to generate outputs is inference; training or fine-tuning is a different, more resource-intensive workload. Lenovo says fine-tuning and training require considerably more resources than inference. Its examples list 5 GB for 7B QLoRA at 4-bit and 46 GB for 70B QLoRA at 4-bit, and explain that LoRA/QLoRA can substantially reduce requirements compared with full fine-tuning. These are guide estimates, not guaranteed end-to-end laptop requirements. Lenovo’s guide covers the estimates.
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