Will It Run AI

Can Vicuna 13B run on RTX 5060 Ti 8GB?

NO — Won't Fit

F0Won't run
Estimated from fit model

Vicuna 13B needs ~22.1 GB but RTX 5060 Ti 8GB only has 8.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: LowStack: BasicBottleneck: Memory capacity
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Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 22.1 GB, exceeds 8.0 GB available
22.1 GB required8.0 GB available
276% VRAM needed

14.1 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

5.3 tok/s

TTFT

36848 ms

Safe context

4K

Memory

22.1 GB / 8.0 GB

Offload

60%

Memory breakdown

Weights7.9 GB
KV Cache12.2 GB
Runtime1.2 GB
Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower
See how fast it feelsVicuna 13B on RTX 5060 Ti 8GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API. When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed. Key benefits of running locally: - Full privacy: your prompts never leave your machine - No per-token cost or rate limits - Works offline once the model is downloaded - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb: Model size Q4 (4-bit) Q8 (8-bit) FP16 7B params ~4.3 GB ~7.5 GB ~14 GB 13B params ~7.9 GB ~13.9 GB ~26 GB 70B params ~42.7 GB ~74.9 GB ~140 GB Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 5.3 tok/s decode · 36.8s TTFT (warm) · 13 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 22.1 GB, but this setup only exposes 8.0 GB of usable VRAM.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatFToo heavy6.5 tok/s16283 ms4K
CodingFToo heavy5.3 tok/s36848 ms4K
Agentic CodingFToo heavy5.3 tok/s53598 ms4K
ReasoningFToo heavy5.3 tok/s43548 ms4K
RAGFToo heavy5.3 tok/s66997 ms4K

Quantization options

How Vicuna 13B (13B params) fits at each quantization level on RTX 5060 Ti 8GB (8.0 GB usable).

QuantBitsVRAMQualityFit
Q2_KBest for your GPU
2
5.1 GB
LowA73
Q3_K_S
3
6.4 GB
LowF0
NVFP4
4
7.3 GB
MediumF0
Q4_K_M
4
7.9 GB
MediumF0
Q5_K_M
5
9.4 GB
HighF0
Q6_K
6
10.7 GB
HighF0
Q8_0
8
13.9 GB
Very HighF0
F16
16
26.7 GB
MaximumF0

Opções de upgrade

Hardware que roda bem Vicuna 13B

NVIDIARTX 4000 Ada 20GBMelhor custo-benefício
20 GB VRAM (+12)
B
Makes the model fit on the accelerator instead of staying completely out of reach.19.2 tok/s decodificação

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 262%.

~$1,250 MSRP

NVIDIARTX 3090 24GBOpção econômica
24 GB VRAM (+16)936 GB/s (+488)
A
Makes the model fit on the accelerator instead of staying completely out of reach.82.6 tok/s decodificação

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,499 MSRP

NVIDIARTX 4090 24GBUpgrade NVIDIA
24 GB VRAM (+16)1008 GB/s (+560)
A
Makes the model fit on the accelerator instead of staying completely out of reach.96.6 tok/s decodificação

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,599 MSRP

Frequently asked questions

Can RTX 5060 Ti 8GB run Vicuna 13B?

No, Vicuna 13B requires more memory than RTX 5060 Ti 8GB provides.

How much VRAM does Vicuna 13B need?

Vicuna 13B (13B parameters) requires approximately 22.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Vicuna 13B?

The recommended quantization for Vicuna 13B is Q4_K_M, which balances quality and memory efficiency.

What speed will Vicuna 13B run at on RTX 5060 Ti 8GB?

On RTX 5060 Ti 8GB, Vicuna 13B achieves approximately 5.3 tokens per second decode speed with a time-to-first-token of 36848ms using Q4_K_M quantization.

Can RTX 5060 Ti 8GB run Vicuna 13B for coding?

For coding workloads, Vicuna 13B on RTX 5060 Ti 8GB receives a F grade with 5.3 tok/s and 4K context.

What context window can Vicuna 13B use on RTX 5060 Ti 8GB?

On RTX 5060 Ti 8GB, Vicuna 13B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.

What should I upgrade first if Vicuna 13B feels slow on RTX 5060 Ti 8GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

See all results for RTX 5060 Ti 8GBSee all hardware for Vicuna 13B
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