AI system category
PC builds for private AI chat, coding assistants, and local models at home, with larger workloads checked against memory estimates.
AI fit is a rough estimate; model/runtime/quantization/context affects results.
Local AI PC
Configuration: RTX 5070 Ti Local Inference PC
A private local inference PC for chat, document questions, coding help, and everyday CUDA workloads.
GPU: PNY GeForce RTX 5070 Ti 16GB Triple Fan
CPU: AMD Ryzen 7 9700X
RAM: 64 GB | Storage: 4000 GB
Recommended model class: A practical first local-AI computer without overbuying.
13B/14B Q4 at practical context
Everyday local LLM fit assumes quantization and moderate context; 30B is not a normal target without more VRAM.
€4,938
RTX 5070 Ti Local Chat Build
Lower-cost 16GB CUDA alternative for private chat, coding help, and document workflows. It remains quote-only, and its exact stock, pricing, BIOS, memory profile, cable routing, and clearances are rechecked before quote.
GPU: PNY GeForce RTX 5070 Ti 16GB Triple Fan
CPU: AMD Ryzen 5 9600X
RAM: 64 GB | Storage: 2000 GB
Recommended model class: 7B-14B q4; selected larger models with CPU offload
13B/14B Q4 at practical context
Everyday local LLM fit assumes quantization and moderate context; 30B is not a normal target without more VRAM.
€4,515
RTX 5060 Ti Local Chat Alternative
The 16GB RTX 5060 Ti keeps CUDA and useful local-model capacity while trading GPU throughput for a lower component cost than the main RTX 5070 Ti build. The 304mm, 2.5-slot card is comfortably inside the case's 405mm GPU limit.
GPU: ASUS Prime GeForce RTX 5060 Ti OC Edition 16GB GDDR7
CPU: AMD Ryzen 5 9600X
RAM: 64 GB | Storage: 2000 GB
Recommended model class: 7B-14B q4; larger models need CPU offload
13B/14B Q4 at practical context
Everyday local LLM fit assumes quantization and moderate context; 30B is not a normal target without more VRAM.
€3,419
Recommended model
A current multimodal everyday model that makes good use of a 16GB GPU.
Fast
Likely good memory headroom for this quantized model at normal context sizes.
Private chat, coding help, document summaries, and image questions
A strong everyday model for 12GB-class GPUs and a practical coding pick when speed matters.
Expected experience: Fast
Likely good memory headroom for this quantized model at normal context sizes.
Serious coding help, complex reasoning, and longer document analysis
A strong dense model for 24GB to 32GB GPUs and higher-memory Apple systems.
Expected experience: Very slow
Offload-heavy experiment only: expect slower responses and keep context short. This is not a normal recommended target.
Assumptions
Family: Google Gemma 4
Parameters: 11.95B
Structure: 11.95B dense
License: Apache License 2.0; allowed. For guidance only; review the model licence before commercial use.
Native context: 262,144 tokens
Quantization: Q4_0 QAT
Approx. Q4 weights: 7.2 GB
Default estimate: 11.5 GB @ 8,192 tokens
Weights / KV / runtime / margin: 7.2 GB / 1.5 GB / 1.2 GB / 1.5 GB
CPU/RAM fallback: Not recommended
VRAM: 12 GB minimum / 16 GB recommended
RAM: 24 GB minimum / 32 GB recommended
Current run mode: Comfortable GPU fit
Expected experience: Fast
Full GPU offload: Only when the memory estimate and context fit
Context warning: Its large context window still requires substantial KV-cache headroom.
| Context | Weights | KV | Runtime | Margin | Estimated GPU memory |
|---|---|---|---|---|---|
| 4K | 7.2 GB | 1 GB | 1.2 GB | 1.5 GB | 11 GB |
| 8K | 7.2 GB | 1.5 GB | 1.2 GB | 1.5 GB | 11.5 GB |
| 16K | 7.2 GB | 2.5 GB | 1.2 GB | 1.5 GB | 12.5 GB |
| 32K | 7.2 GB | 5 GB | 1.2 GB | 2 GB | 15.5 GB |
Swipe the table sideways to inspect every memory estimate.
Research sources
Researched: 2026-07-30
Family: Qwen3.5
Parameters: 9B
Structure: 9B dense
License: Apache License 2.0; allowed. For guidance only; review the model licence before commercial use.
Native context: 262,144 tokens
Extended context: 1,010,000 via YaRN; not a default fit assumption
Quantization: Q4_K_M
Approx. Q4 weights: 6.6 GB
Default estimate: 11 GB @ 8,192 tokens
Weights / KV / runtime / margin: 6.6 GB / 1.5 GB / 1.2 GB / 1.5 GB
CPU/RAM fallback: Not recommended
VRAM: 8 GB minimum / 12 GB recommended
RAM: 16 GB minimum / 32 GB recommended
Current run mode: Comfortable GPU fit
Expected experience: Fast
Full GPU offload: Only when the memory estimate and context fit
Context warning: Start around 8K context even though the model supports much more.
| Context | Weights | KV | Runtime | Margin | Estimated GPU memory |
|---|---|---|---|---|---|
| 4K | 6.6 GB | 1 GB | 1.2 GB | 1.5 GB | 10.5 GB |
| 8K | 6.6 GB | 1.5 GB | 1.2 GB | 1.5 GB | 11 GB |
| 16K | 6.6 GB | 2.5 GB | 1.2 GB | 1.5 GB | 12 GB |
| 32K | 6.6 GB | 5 GB | 1.2 GB | 2 GB | 15 GB |
Swipe the table sideways to inspect every memory estimate.
Family: Qwen3.6
Parameters: 27B
Structure: 27B dense
License: Apache License 2.0; allowed. For guidance only; review the model licence before commercial use.
Native context: 262,144 tokens
Extended context: 1,010,000 via YaRN; not a default fit assumption
Quantization: Q4_K_M
Approx. Q4 weights: 17 GB
Default estimate: 23 GB @ 8,192 tokens
Weights / KV / runtime / margin: 17 GB / 2 GB / 1.5 GB / 2.5 GB
CPU/RAM fallback: Not recommended
VRAM: 24 GB minimum / 32 GB recommended
RAM: 48 GB minimum / 64 GB recommended
Current run mode: Partial GPU offload only
Expected experience: Very slow
Full GPU offload: Only when the memory estimate and context fit
Context warning: Repository-scale or very long document context can push a 24GB card beyond a comfortable fit.
| Context | Weights | KV | Runtime | Margin | Estimated GPU memory |
|---|---|---|---|---|---|
| 4K | 17 GB | 1 GB | 1.5 GB | 2.5 GB | 22 GB |
| 8K | 17 GB | 2 GB | 1.5 GB | 2.5 GB | 23 GB |
| 16K | 17 GB | 4 GB | 1.5 GB | 3 GB | 25.5 GB |
| 32K | 17 GB | 8 GB | 1.5 GB | 3.5 GB | 30 GB |
Swipe the table sideways to inspect every memory estimate.
Local AI performance is approximate. Results depend on quantization, context length, backend, drivers, and whether the model plus KV cache fits in VRAM or Apple unified memory.
