GPU memory and RAM
GPU memory usually limits model size; RAM supports apps, data, and model offload.
Component overview
ASUS Turbo Radeon AI PRO R9700 32GB
ASUS graphics card
CategoryGraphics cardsBrandASUS
Technical specifications
Brand
ASUS
Verified manufacturer part number
90YV0MN0-M0NM00
Release
2025 Q1
VRAM
32GB GDDR6 ECC
Architecture
RDNA 4
Display Power
300W
Connector Standard
1x 16-pin
Minimum PSU
750W
Dual GPU Capable
No
Memory Bus
256-bit
Bandwidth
640 GB/s
Stream Processors
4096
Tensor Cores
128
RT Cores
64
Base / Boost Clock
2350 / 2920 MHz
TDP
300W
PCIe Generation
PCIe 5.0
Slot Width
2-slot
Length
267mm
Power Connectors
1x 16-pin
Recommended PSU
750W
FP32
47.8 TFLOPS
Inference Notes
Exact ASUS 90YV0MN0-M0NM00 with 32GB VRAM. Linux, ROCm, framework, ECC behavior, and the target workload are qualified before quote; CUDA-only tools need an NVIDIA alternative.
30B-class Q4 only with caveats
Fit assumes quantization, moderate context, and runtime validation; 70B is not a normal target without 48GB+ VRAM or large unified memory.
This build can explore 27B/35B-class models; choose 48GB+ VRAM for a defensible 70B-class GPU target.
AI terms in plain language
CUDA
NVIDIA’s software layer for many AI tools. Macs and AMD GPUs do not run CUDA workflows the same way.
Apple unified memory
Memory shared by the CPU, GPU, macOS, and apps; it is not the same as NVIDIA VRAM.
7B / 8B / 14B / 70B
Approximate model size in billions of parameters; larger models usually need more memory.
Quantized models
Lower-precision 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
LoRA and QLoRA train small adapters and need fewer resources than full fine-tuning.
Throughput (tokens/s)
Model output speed. Compare the same model, quantization, context, runtime, and user count; prompt processing is a separate measurement.
Price estimate history
Uses market prices where available and reference estimates otherwise.
Component estimate: €1,927·Reference estimate·Quote estimate: €2,327·Component service and order handling: +15%
Low€1,733High€1,927Avg€1,8167 data points
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Pricing & Purchase
Component estimate€1,927
Reference estimateLatest graph value: €1,927
Planning estimate with component service€2,327
€2,327
Estimated total. Final price confirmed before ordering.
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Local AI examples
Local AI examples
Good fit for private chatGood fit for coding helpGood fit for document summariesNot ideal for 70B+ models
Recommended model
Qwen3.6 27B
A strong dense model for 24GB to 32GB GPUs and higher-memory Apple systems.
Comfortable GPU fit
Fast
ollama run qwen3.6:27b
Likely good memory headroom for this quantized model at normal context sizes.
- 64GB system/unified memory available
- 32GB effective accelerator memory for model weights and cache
Qwen3.6 35B-A3B
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.
- 64GB system/unified memory available
Gemma 4 12B
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.
- 64GB system/unified memory available
Expandable technical details
Assumptions
- GPU VRAM assumption: 32GB from ASUS Turbo Radeon AI PRO R9700 32GB.
- Assumed RAM: 64GB.
- Ratings include model weights, estimated KV cache, runtime overhead, and safety margin for one local model running at a time. Treat them as fit guidance, not a speed guarantee.
- GPU product pages assume a sensible amount of system RAM for this VRAM class. Complete build pages show page-specific RAM fit.
Qwen3.6 27B technical details
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 |
Swipe the table sideways to inspect every memory estimate.
Qwen3.6 35B-A3B technical details
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 |
Swipe the table sideways to inspect every memory estimate.
Gemma 4 12B technical details
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
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.
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