Will It Run AI

Can LLaVA 1.5 7B run on RTX 3080 10GB?

NO — Won't Fit

F0Won't run
Estimated from fit model

LLaVA 1.5 7B needs ~14.3 GB but RTX 3080 10GB only has 10.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: MediumStack: BasicBottleneck: Memory capacity
Share:

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) — 14.3 GB, exceeds 10.0 GB available
14.3 GB required10.0 GB available
143% VRAM needed

4.3 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

47.9 tok/s

TTFT

4041 ms

Safe context

4K

Memory

14.3 GB / 10.0 GB

Offload

30%

Memory breakdown

Weights4.3 GB
KV Cache7.8 GB
Runtime1.2 GB
Headroom1.0 GB

See how fast it feels

With memory offload — actual speed may be lower
See how fast it feelsLLaVA 1.5 7B on RTX 3080 10GB
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: 47.9 tok/s decode · 4.0s TTFT (warm) · 120 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 14.3 GB, but this setup only exposes 10.0 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
ChatARuns with offload (needs ~0.2 GB host RAM)93.9 tok/s1125 ms4K
CodingFToo heavy47.9 tok/s4041 ms4K
Agentic CodingFToo heavy20.3 tok/s13878 ms4K
ReasoningFToo heavy47.9 tok/s4776 ms4K
RAGFToo heavy20.3 tok/s17348 ms4K

Quantization options

How LLaVA 1.5 7B (7B params) fits at each quantization level on RTX 3080 10GB (10.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
2.7 GB
LowB69
Q3_K_S
3
3.4 GB
LowB70
NVFP4
4
3.9 GB
MediumA71
Q4_K_M
4
4.3 GB
MediumA71
Q5_K_M
5
5.0 GB
HighA71
Q6_KBest for your GPU
6
5.7 GB
HighA70
Q8_0
8
7.5 GB
Very HighF0
F16
16
14.3 GB
MaximumF0

升级选项

能流畅运行 LLaVA 1.5 7B 的硬件

NVIDIARTX 5060 Ti 16GB经济之选
16 GB VRAM (+6)
A
Makes the model fit on the accelerator instead of staying completely out of reach.65 tok/s 解码

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.

~$449 MSRP

NVIDIARTX 4060 Ti 16GB最佳性价比
16 GB VRAM (+6)
B
Makes the model fit on the accelerator instead of staying completely out of reach.49.2 tok/s 解码

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.

~$499 MSRP

NVIDIARTX 2000 Ada 16GBNVIDIA 升级
16 GB VRAM (+6)
B
Makes the model fit on the accelerator instead of staying completely out of reach.51.3 tok/s 解码

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.

~$625 MSRP

Frequently asked questions

Can RTX 3080 10GB run LLaVA 1.5 7B?

No, LLaVA 1.5 7B requires more memory than RTX 3080 10GB provides.

How much VRAM does LLaVA 1.5 7B need?

LLaVA 1.5 7B (7B parameters) requires approximately 14.3 GB of memory with Q4_K_M quantization.

What is the best quantization for LLaVA 1.5 7B?

The recommended quantization for LLaVA 1.5 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will LLaVA 1.5 7B run at on RTX 3080 10GB?

On RTX 3080 10GB, LLaVA 1.5 7B achieves approximately 47.9 tokens per second decode speed with a time-to-first-token of 4041ms using Q4_K_M quantization.

Can RTX 3080 10GB run LLaVA 1.5 7B for coding?

For coding workloads, LLaVA 1.5 7B on RTX 3080 10GB receives a F grade with 47.9 tok/s and 4K context.

What context window can LLaVA 1.5 7B use on RTX 3080 10GB?

On RTX 3080 10GB, LLaVA 1.5 7B 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 LLaVA 1.5 7B feels slow on RTX 3080 10GB?

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 3080 10GBSee all hardware for LLaVA 1.5 7B
Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/llava-1.5-7b-on-rtx-3080-10gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: