VRAM
Memory on the graphics card; usually the main limit for local AI model size.
Component overview
ASUS Prime GeForce RTX 5060 Ti OC Edition 16GB GDDR7
ASUS graphics card
CategorygpuBrandASUS
Technical specifications
Brand
ASUS
Verified manufacturer part number
90YV0MH2-M0NA00
Release
2025 Q1
VRAM
16GB GDDR7
Architecture
Blackwell
Display Power
180W
Connector Standard
PCIe 8-pin
Minimum PSU
550W
Dual GPU Capable
No
Memory Bus
128-bit
Bandwidth
448 GB/s
CUDA Cores
4608
Tensor Cores
144
RT Cores
36
Base / Boost Clock
2407 / 2647 MHz
TDP
180W
PCIe Generation
PCIe 5.0
Slot Width
3-slot
Length
304mm
Power Connectors
1x 8-pin
Recommended PSU
550W
AI Score
78
Source
https://www.asus.com/motherboards-components/graphics-cards/prime/prime-rtx5060ti-o16g/techspec/
Inference Notes
Exact ASUS PRIME-RTX5060TI-O16G. Reference price is backed by reviewed exact-MPN offers; checkout still requires current accepted observations.
13B/14B Q4 at practical context
Everyday local LLM fit assumes quantization and moderate context; 30B is not a normal target without more VRAM.
This build handles 12B-class models better than larger dense models; choose a 24GB+ VRAM build for a practical 27B 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€748High€748Avg€7481 data point
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Pricing & Purchase
Planning estimate: €860
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Local AI examples
Examples for ASUS Prime GeForce RTX 5060 Ti OC Edition 16GB GDDR7, based on GPU VRAM or Apple unified memory plus RAM headroom. System RAM is not treated as VRAM.
Local AI examples
Examples for ASUS Prime GeForce RTX 5060 Ti OC Edition 16GB GDDR7, 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
Gemma 4 12B
A current multimodal everyday model that makes good use of a 16GB GPU.
It is the strongest comfortable general-purpose starting point for most 16GB builds.
Comfortable GPU fit
Fast
ollama run gemma4:12b-it-qat
Likely good memory headroom for this quantized model at normal context sizes.
- 32GB system/unified memory available
- 16GB effective accelerator memory for model weights and cache
Qwen3.5 9B
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.
- 32GB system/unified memory available
- 16GB 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
Needs at least 64GB system RAM; this machine reports 32GB.
- 32GB system/unified memory available
- 16GB effective accelerator memory for model weights and cache
Qwen3.5 4B
Fast private chat, short summaries, and first local-AI experiments
A current compact model for learning local AI, multilingual chat, and short document work.
Expected experience: Fast
Likely good memory headroom for this quantized model at normal context sizes.
- 32GB system/unified memory available
- 16GB effective accelerator memory for model weights and cache
Expandable technical details
Assumptions
- GPU VRAM assumption: 16GB from ASUS Prime GeForce RTX 5060 Ti OC Edition 16GB GDDR7.
- Assumed RAM: 32GB.
- 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.
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
Qwen3.5 9B technical details
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 |
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 |
Qwen3.5 4B technical details
Family: Qwen3.5
Parameters: 4B
Structure: 4B 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: 3.4 GB
Default estimate: 6 GB @ 8,192 tokens
Weights / KV / runtime / margin: 3.4 GB / 0.5 GB / 0.8 GB / 1 GB
CPU/RAM fallback: Small-model fallback only
VRAM: 4 GB minimum / 8 GB recommended
RAM: 8 GB minimum / 16 GB recommended
Current run mode: Comfortable GPU fit
Expected experience: Fast
Full GPU offload: Only when the memory estimate and context fit
Context warning: The advertised long context is not a practical default on low-memory machines.
| Context | Weights | KV | Runtime | Margin | Estimated GPU memory |
|---|---|---|---|---|---|
| 4K | 3.4 GB | 0.5 GB | 0.8 GB | 1 GB | 6 GB |
| 8K | 3.4 GB | 0.5 GB | 0.8 GB | 1 GB | 6 GB |
| 16K | 3.4 GB | 1 GB | 0.8 GB | 1 GB | 6.5 GB |
| 32K | 3.4 GB | 2 GB | 0.8 GB | 1 GB | 7.5 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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