VRAM
Memory on the graphics card; usually the main limit for local AI model size.
Component overview
ASUS Turbo Radeon AI PRO R9700 32GB
ASUS graphics card
CategorygpuBrandASUS
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
AI Score
94
Source
https://www.asus.com/us/motherboards-components/graphics-cards/turbo/turbo-ai-pro-r9700-32g/techspec/
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
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.
Estimated Market Pricing
Estonian market planning estimate. A written quote includes the exact price and confirmed availability.
Low€1,733High€1,733Avg€1,7331 data point
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Pricing & Purchase
Planning estimate: €1,993
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Local AI examples
Examples for ASUS Turbo Radeon AI PRO R9700 32GB, based on GPU VRAM or Apple unified memory plus RAM headroom. System RAM is not treated as VRAM.
Local AI examples
Examples for ASUS Turbo Radeon AI PRO R9700 32GB, based on GPU VRAM or Apple unified memory plus RAM headroom. System RAM is not treated as VRAM.
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.
It is the practical quality target when the machine has enough memory for a larger dense model.
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
- 32GB effective accelerator memory for model weights and cache
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
- 32GB effective accelerator memory for model weights and cache
Llama 3.3 70B Instruct
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.
- 64GB system/unified memory available
- 32GB effective accelerator memory for model weights and cache
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 |
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 |
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 |
Research sources
Researched: 2026-07-30
Llama 3.3 70B Instruct technical details
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 |
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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