Local AI workstation
The same 32GB GPU-memory class with a different trackable memory and TLC storage combination
AI strength score
AI strength score
What this score is based on
For serious local AI experiments and demanding workloads.
Value
Value
What this score is based on
Very strong local-AI price efficiency for the current estimate.
Comfortable
Possible with limits
Not recommended
Choose this build if you want a local AI PC matched to the workloads above.
The chart combines the current estimated baseline with verified market prices. New checks automatically replace estimated points for the matching period.
Latest estimated parts total
€3,876
Final price has service fees included.
Time range
The chart slider reports the component total by date.
| Date | Component total | Checked | Estimated | CPU | GPU | RAM | Storage | Motherboard | PSU | Case | Cooler |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 7 May | €3,876 | 0 | 8 | €311 | €1,733 | €812 | €605 | €150 | €132 | €90 | €44 |
| 10 May | €3,876 | 0 | 8 | €311 | €1,733 | €812 | €605 | €150 | €132 | €90 | €44 |
| 13 May | €3,876 | 0 | 8 | €311 | €1,733 | €812 | €605 | €150 | €132 | €90 | €44 |
| 16 May | €3,876 | 0 | 8 | €311 | €1,733 | €812 | €605 | €150 | €132 | €90 | €44 |
| 19 May | €3,876 | 0 | 8 | €311 | €1,733 | €812 | €605 | €150 | €132 | €90 | €44 |
| 22 May | €3,876 | 0 | 8 | €311 | €1,733 | €812 | €605 | €150 | €132 | €90 | €44 |
| 25 May | €3,876 | 0 | 8 | €311 | €1,733 | €812 | €605 | €150 | €132 | €90 | €44 |
| 28 May | €3,876 | 0 | 8 | €311 | €1,733 | €812 | €605 | €150 | €132 | €90 | €44 |
| 31 May | €3,876 | 0 | 8 | €311 | €1,733 | €812 | €605 | €150 | €132 | €90 | €44 |
| 3 Jun | €3,876 | 0 | 8 | €311 | €1,733 | €812 | €605 | €150 | €132 | €90 | €44 |
| 6 Jun | €3,876 | 0 | 8 | €311 | €1,733 | €812 | €605 | €150 | €132 | €90 | €44 |
| 9 Jun | €3,876 | 0 | 8 | €311 | €1,733 | €812 | €605 | €150 | €132 | €90 | €44 |
| 12 Jun | €3,876 | 0 | 8 | €311 | €1,733 | €812 | €605 | €150 | €132 | €90 | €44 |
| 15 Jun | €3,876 | 0 | 8 | €311 | €1,733 | €812 | €605 | €150 | €132 | €90 | €44 |
| 18 Jun | €3,876 | 0 | 8 | €311 | €1,733 | €812 | €605 | €150 | €132 | €90 | €44 |
| 21 Jun | €3,876 | 0 | 8 | €311 | €1,733 | €812 | €605 | €150 | €132 | €90 | €44 |
| 24 Jun | €3,876 | 0 | 8 | €311 | €1,733 | €812 | €605 | €150 | €132 | €90 | €44 |
| 27 Jun | €3,876 | 0 | 8 | €311 | €1,733 | €812 | €605 | €150 | €132 | €90 | €44 |
| 30 Jun | €3,876 | 0 | 8 | €311 | €1,733 | €812 | €605 | €150 | €132 | €90 | €44 |
| 3 Jul | €3,876 | 0 | 8 | €311 | €1,733 | €812 | €605 | €150 | €132 | €90 | €44 |
| 6 Jul | €3,876 | 0 | 8 | €311 | €1,733 | €812 | €605 | €150 | €132 | €90 | €44 |
| 9 Jul | €3,876 | 0 | 8 | €311 | €1,733 | €812 | €605 | €150 | €132 | €90 | €44 |
| 12 Jul | €3,876 | 0 | 8 | €311 | €1,733 | €812 | €605 | €150 | €132 | €90 | €44 |
| 15 Jul | €3,876 | 0 | 8 | €311 | €1,733 | €812 | €605 | €150 | €132 | €90 | €44 |
| 18 Jul | €3,876 | 0 | 8 | €311 | €1,733 | €812 | €605 | €150 | €132 | €90 | €44 |
| 21 Jul | €3,876 | 1 | 7 | €311 | €1,733 | €812 | €605 | €150 | €132 | €90 | €44 |
| 24 Jul | €3,876 | 0 | 8 | €311 | €1,733 | €812 | €605 | €150 | €132 | €90 | €44 |
| 27 Jul | €3,876 | 0 | 8 | €311 | €1,733 | €812 | €605 | €150 | €132 | €90 | €44 |
| 30 Jul | €3,876 | 8 | 0 | €311 | €1,733 | €812 | €605 | €150 | €132 | €90 | €44 |
| 2 Aug | €3,876 | 0 | 8 | €311 | €1,733 | €812 | €605 | €150 | €132 | €90 | €44 |
CPU
AMD Ryzen 7 9700X
GPU
ASUS Turbo Radeon AI PRO R9700 32GB
VRAM
32 GB
RAM
64 GB
Storage
4000 GB
Recommended model class
7B-14B LoRA/QLoRA after exact ROCm workload validation
Throughput
Training throughput varies by model, quantization, context, and batch size
System power
~430 W
Recommended PSU
750 W
Cooling
Dual-tower air cooling
Component rows show Estonian market or reference prices. Service, assembly, and configuration are shown separately below.
| Component | Product | Estimated market price |
|---|---|---|
| CPU | AMD Ryzen 7 9700X | €311 |
| GPU | ASUS Turbo Radeon AI PRO R9700 32GB | €1,733 |
| RAM | Patriot Viper Venom 64GB (2x32GB) DDR5-6000 CL30 AMD EXPO | €812 |
| Storage | WD Black SN850X 4TB | €605 |
| Motherboard | Gigabyte B650 EAGLE AX | €151 |
| PSU | SeaSonic Focus GX-750 750W | €132 |
| Case | Fractal Design Pop Air Black TG Clear Tint | €90 |
| Cooler | Thermalright Phantom Spirit 120 | €44 |
| Estimated component total | €3,878 | |
| Service / assembly / configuration fee (18.54%) | €719 | |
| Estimated build total | €4,597 | |
Examples for Radeon AI PRO 32GB Storage Alternative, based on GPU VRAM or Apple unified memory plus RAM headroom. System RAM is not treated as VRAM.
Recommended model
A strong dense model for 24GB to 32GB GPUs and higher-memory Apple systems.
It is the practical quality target when the machine has enough memory for a larger dense model.
Fast
Likely good memory headroom for this quantized model at normal context sizes.
Coding agents, repository analysis, and complex local assistant workflows
A capable MoE model that gives 32GB and 48GB workstations a meaningfully heavier coding target.
Expected experience: Usable
Practical only with Q4-style quantization and moderate context; larger context can require offload or a smaller model.
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.
High-end local chat experiments on 48GB GPUs or 96GB+ Apple systems
A clear upper-limit example for checking whether a machine can attempt a 70B-class model.
Expected experience: Not recommended
This model needs roughly 51GB GPU memory at Q4_K_M with moderate context; this machine has about 32GB VRAM.
Private assistant chat, document understanding, and image or audio analysis
A current multimodal everyday model that makes good use of a 16GB GPU.
Expected experience: Fast
Likely good memory headroom for this quantized model at normal context sizes.
Assumptions
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: Comfortable GPU fit
Expected experience: Fast
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 |
Family: Qwen3.6
Parameters: 35B
Structure: 35B total / 3B active MoE
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: 24 GB
Default estimate: 31.5 GB @ 8,192 tokens
Weights / KV / runtime / margin: 24 GB / 2.5 GB / 2 GB / 3 GB
CPU/RAM fallback: Not recommended
VRAM: 32 GB minimum / 48 GB recommended
RAM: 64 GB minimum / 64 GB recommended
Current run mode: Practical quantized GPU fit
Expected experience: Usable
Full GPU offload: Only when the memory estimate and context fit
Context warning: Long repository context can use the remaining headroom quickly, especially on 32GB GPUs.
| Context | Weights | KV | Runtime | Margin | Estimated GPU memory |
|---|---|---|---|---|---|
| 4K | 24 GB | 1.5 GB | 2 GB | 3 GB | 30.5 GB |
| 8K | 24 GB | 2.5 GB | 2 GB | 3 GB | 31.5 GB |
| 16K | 24 GB | 5 GB | 2 GB | 3.5 GB | 34.5 GB |
| 32K | 24 GB | 10 GB | 2 GB | 4 GB | 40 GB |
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 |
Family: Meta Llama 3.3
Parameters: 70.60B
Structure: 70.6B dense
License: Llama 3.3 Community License; allowed with terms; gated access. For guidance only; review the model licence before commercial use.
Native context: 131,072 tokens
Quantization: Q4_K_M
Approx. Q4 weights: 42.5 GB
Default estimate: 51 GB @ 4,096 tokens
Weights / KV / runtime / margin: 42.5 GB / 3 GB / 1.5 GB / 4 GB
CPU/RAM fallback: Not recommended
VRAM: 48 GB minimum / 64 GB recommended
RAM: 64 GB minimum / 96 GB recommended
Current run mode: Not realistic here
Expected experience: Not recommended
Full GPU offload: Often limited
Context warning: The Q4 weights alone use about 42.5GB before KV cache, runtime overhead, and desktop headroom.
| Context | Weights | KV | Runtime | Margin | Estimated GPU memory |
|---|---|---|---|---|---|
| 4K | 42.5 GB | 3 GB | 1.5 GB | 4 GB | 51 GB |
| 8K | 42.5 GB | 6 GB | 1.5 GB | 4 GB | 54 GB |
| 16K | 42.5 GB | 12 GB | 1.5 GB | 4.5 GB | 60.5 GB |
| 32K | 42.5 GB | 24 GB | 1.5 GB | 5.5 GB | 73.5 GB |
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 |
Research sources
Researched: 2026-07-30
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.
VRAM
Memory on the graphics card; usually the main limit for local AI model size.
System RAM vs GPU memory
RAM helps apps and data work; GPU memory usually decides which model size can run quickly.
CUDA
NVIDIA’s software layer for many AI tools. Macs and AMD GPUs do not run CUDA workflows the same way.
Apple unified memory
Apple Silicon memory shared by CPU, GPU, macOS, and apps. Useful for Mac AI, but not the same as NVIDIA VRAM.
7B / 8B / 14B / 70B
Approximate model size in billions of parameters. Larger numbers usually need more memory and may run slower.
Quantized models
Compressed models, such as Q4, that use less memory with possible quality or speed tradeoffs.
Context length
How much text the model can keep in mind at once. Longer context uses more memory.
Inference
Running an existing model for chat, coding help, summaries, or document workflows.
Fine-tuning vs adapter tuning
Fine-tuning adapts a model; adapter tuning, such as LoRA/QLoRA, is a lighter way to steer an existing model with examples.
Dual-GPU limitations
Two GPUs do not automatically combine VRAM into one large pool. Software must explicitly support multiple GPUs.
eGPU limitations
External GPUs need enclosure, driver, and runtime validation, and usually do not mean macOS graphics or gaming acceleration.
Planning estimate / written quote
The displayed estimate helps with budgeting. A written quote contains the exact price, confirmed availability, and any proposed substitutions.
Here are similar setups in this category.
Planning estimate
About €4,597
The displayed amount is a planning estimate. The written quote includes the exact total and confirmed availability.
The chart shows the estimated Estonian parts total before service and assembly. New price checks replace baseline estimates automatically. No payment is collected when you request a quote.
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