Which PC should you use for local AI in 2026?
You cannot evaluate an AI computer solely on whether a model fits in memory. Memory determines whether the model can load; bandwidth and computing power determine how fast it answers.
16 GB VRAM is the practical baseline for 8B models with full context and 14B models at 4-bit (Q4) with moderate context. Running 14B with long documents (RAG), 32B models entirely in VRAM, or higher precision typically requires 24 GB VRAM or 32–64+ GB unified memory.
Memory requirements depend strictly on model parameter size, quantization bit-depth, and context length (KV cache). NVIDIA retains the broadest software ecosystem, while Apple Silicon and AMD provide compelling alternatives for specific workloads.
1. The most important rule: "can load" is not the same as "fast enough"
When researching local AI online, the hardware choice is often reduced to a simple question: Can the model fit in my VRAM?
- Memory capacity (VRAM/RAM in GB)
- Determines whether the model can load without crashing or failing.
- Memory bandwidth (GB/s)
- Determines how many words (tokens) the model can generate per second.
- Compute power (TFLOPS / Tensor Cores / Compute Units)
- Determines how fast the machine processes your prompt (Time to First Token – TTFT) before the first word is written.
If you force a 14B model onto a machine by offloading half of it to system RAM (CPU offload), the model will technically run. But because standard DDR5 RAM has a bandwidth of 50–80 GB/s compared to a graphics card's 500–1,000 GB/s, generation speed often drops from reading speed (15–30 tok/s) to crawl speed (1–3 tok/s).
2. How much memory do you actually need?
A model's memory footprint consists of weights, KV cache (context), and runtime working buffers. Here are the practical boundaries for 4-bit (Q4_K_M) models with 8,192 tokens of context:
| Memory / VRAM | Recommended model (4-bit) | Context boundary | Usability & status | Typical hardware | Illustrative price (DK) | Market |
|---|---|---|---|---|---|---|
| 8 GB VRAM Short context | 3B–4B smooth; 8B with short context (<4K tokens) | Up to 4K tokens | Planning estimate: Limited headroom for long context. 8B models quickly exceed 8 GB VRAM when context or KV cache expands. | RTX 4060 8GB | From ~2,300 DKK (illustrative) | See in Market |
| 12 GB VRAM Good for 8B | 8B (full context); 14B only with aggressive compression (IQ3) | 8K–16K tokens | Planning estimate: Comfortable for 8B models with extended context. 14B Q4 leaves very narrow headroom for KV cache. | RTX 4070 12GB | From ~4,200 DKK (illustrative) | See in Market |
| 16 GB VRAM ★ Sweet spot (8K measured) | 8B full context; 14B (Q4_K_M) smooth | 8K tokens measured (Labs) · 16K–32K guidance | Practical sweet spot in 2026. 14B (Q4_K_M) fits in VRAM at moderate context (8K measured in Labs); 32B Q4 requires system RAM offloading or aggressive quantization. | Radeon RX 7800 XT / RTX 4070 Ti Super | From ~3,900 DKK (illustrative) | See in Market |
| 24 GB VRAM High-end (Calculated) | 14B (long context); 32B (Q4) in VRAM | 32K+ tokens | Planning estimate: Provides headroom for 14B with extended context and 32B Q4 at standard context entirely in VRAM. | RTX 3090 (used) / RTX 4090 | See latest retailer prices in Market | See in Market |
| 32–64+ GB Unified Max memory | 32B (on 32 GB); 70B requires 64+ GB | Up to 16K–32K for 32B; 70B requires 64+ GB (modest context) | Planning estimate: 32 GB unified memory accommodates 32B Q4 at standard context. 70B Q4 requires at least 64 GB unified memory, with significant prompt processing wait times (TTFT) on large contexts. | Mac Studio / MacBook Pro | From ~16,000 DKK (illustrative) | See in Market |
Note: Labs empirical measurements for 16 GB cover RX 7800 XT at 8K context (Vulkan). Context extension up to 16K–32K for 8B models is guidance based on calculated memory sizing. Listed hardware prices are illustrative estimates; retailer pricing and availability reflect the latest synchronized retailer feed data in RigRoute Market.
3. Context length: why long documents change the equation
If you use AI to analyze long PDFs, large code repositories, or long conversations (RAG), the KV cache grows significantly. In RigRoute Labs we tested Qwen2.5 models from 3B to 32B on a MacBook Pro with an M1 Pro (32 GB unified memory):
Labs benchmark: How long do you wait before the first token (TTFT)?
Measured on Apple M1 Pro (32 GB unified memory) with Qwen 2.5 14B (Q4_K_M, q8_0 KV). Both runs completed with zero swap.
⚡ Key observation: Even when the model and full context fit 100% in RAM without swap, prompt ingestion latency increases 260× from 1.81s to 471.6s.
Measurement limitation: Tested on one machine with Metal backend (Stage 1A protocol). It demonstrates that even when a model and context fit 100% in memory without swap, compute latency on large prompts can make interactive use impractical without substantial compute power.
4. Mac, NVIDIA Desktop or AMD: which platform should you choose?
Desktop PC with NVIDIA GeForce (CUDA)
Industry standardThe safest and most versatile route. Broadest software support (Ollama, LM Studio, vLLM, ComfyUI, PyTorch) via CUDA out of the box. Highest memory bandwidth and rapid prompt processing via Tensor Cores. Limitation: Consumer cards stop at 16 GB VRAM or cost over 14,000 DKK for 24 GB.
Apple Silicon (Mac Mini / MacBook Pro / Mac Studio)
Unified memoryMost memory for the money for large models. Unified Memory allows the GPU to use up to 75–80% of total system RAM. A Mac with 36 GB accommodates 32B Q4 models at moderate context, while 64 GB or 128 GB can load 70B Q4 models that would otherwise require multiple expensive GPUs. Limitation: Memory cannot be upgraded after purchase, and long prompt ingestion takes longer.
AMD Radeon on Linux or Windows (ROCm / Vulkan)
Labs tested (Vulkan)More VRAM per krone. For example, Radeon RX 7800 XT offers 16 GB VRAM for under 4,000 DKK. RigRoute Labs measured strong generation speeds on RX 7800 XT under Vulkan. Limitation: ROCm support is primarily mature on Linux, and some specialized fine-tuning libraries still target NVIDIA CUDA exclusively.
5. How to find the right hardware on RigRoute
Retailer links on RigRoute may be affiliate links; our algorithmic recommendations and benchmark measurements are never influenced by commission. Read more in our independence disclosure.
Updated 22 October 2026. Independent hardware analysis based on measurements from RigRoute Labs.