Can llava llama 3 8b v1 1 run on RTX 5060 Ti 8GB?

YES — With Offload

C53Usable

Estimated from fit model

llava llama 3 8b v1 1 needs ~7.8 GB VRAM. RTX 5060 Ti 8GB has 8.0 GB. With Q4_K_M quantization, expect ~57 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: LowStack: BasicBottleneck: Balanced

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) — 7.8 GB, 56.9 tok/s, Runs with offload

7.8 GB required8.0 GB available

98% VRAM used

Fit status

Runs with offload

Decode

56.9 tok/s

TTFT

3401 ms

Safe context

19K

Memory

7.8 GB / 8.0 GB

Memory breakdown

Weights4.9 GB

KV Cache0.9 GB

Runtime1.2 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsllava llama 3 8b v1 1 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: 56.9 tok/s decode · 3.4s TTFT (warm) · 142 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Tight fit	56.9 tok/s	1855 ms	19K
Coding	C	Runs with offload	56.9 tok/s	3401 ms	19K
Agentic Coding	C	Very compromised (needs ~0.4 GB host RAM)	36.2 tok/s	7777 ms	19K
Reasoning	C	Runs with offload	56.9 tok/s	4020 ms	19K
RAG	C	Very compromised (needs ~0.4 GB host RAM)	36.2 tok/s	9721 ms	19K

Quantization options

How llava llama 3 8b v1 1 (8B params) fits at each quantization level on RTX 5060 Ti 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C54
Q3_K_S	3	3.9 GB	Low	C54
NVFP4	4	4.5 GB	Medium	C53
Q4_K_MBest for your GPU	4	4.9 GB	Medium	C53
Q5_K_M	5	5.8 GB	High	F0
Q6_K	6	6.6 GB	High	F0
Q8_0	8	8.6 GB	Very High	F0
F16	16	16.4 GB	Maximum	F0

Get started

Copy-paste commands to run llava llama 3 8b v1 1 on your machine.

Run

lms load hf-xtuner--llava-llama-3-8b-v1-1-gguf && lms server start

Upgrade options

Hardware that runs llava llama 3 8b v1 1 well

RTX 3060 12GBBudget pick

12 GB VRAM (+4)

Adds memory headroom for longer context windows and future model growth.48.7 tok/s decode

Adds memory headroom for longer context windows and future model growth.

~$329 MSRP

RTX 5070 12GBBest value

12 GB VRAM (+4)672 GB/s (+224)

Raises estimated decode speed by about 53%.86.8 tok/s decode

Raises estimated decode speed by about 53%.

Adds memory headroom for longer context windows and future model growth.

~$549 MSRP

RTX 4070 Super 12GBNVIDIA upgrade

12 GB VRAM (+4)504 GB/s (+56)

Raises estimated decode speed by about 40%.79.5 tok/s decode

Raises estimated decode speed by about 40%.

Adds memory headroom for longer context windows and future model growth.

~$599 MSRP

Frequently asked questions

Can RTX 5060 Ti 8GB run llava llama 3 8b v1 1?

Yes, RTX 5060 Ti 8GB can run llava llama 3 8b v1 1 with a C grade (Runs with offload). Expected decode speed: 56.9 tok/s.

How much VRAM does llava llama 3 8b v1 1 need?

llava llama 3 8b v1 1 (8B parameters) requires approximately 7.8 GB of memory with Q4_K_M quantization.

What is the best quantization for llava llama 3 8b v1 1?

The recommended quantization for llava llama 3 8b v1 1 is Q4_K_M, which balances quality and memory efficiency.

What speed will llava llama 3 8b v1 1 run at on RTX 5060 Ti 8GB?

On RTX 5060 Ti 8GB, llava llama 3 8b v1 1 achieves approximately 56.9 tokens per second decode speed with a time-to-first-token of 3401ms using Q4_K_M quantization.

Can RTX 5060 Ti 8GB run llava llama 3 8b v1 1 for coding?

For coding workloads, llava llama 3 8b v1 1 on RTX 5060 Ti 8GB receives a C grade with 56.9 tok/s and 19K context.

What context window can llava llama 3 8b v1 1 use on RTX 5060 Ti 8GB?

On RTX 5060 Ti 8GB, llava llama 3 8b v1 1 can safely use up to 19K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if llava llama 3 8b v1 1 feels slow on RTX 5060 Ti 8GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

See all results for RTX 5060 Ti 8GB See all hardware for llava llama 3 8b v1 1

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<iframe src="https://willitrunai.com/embed/hf-xtuner--llava-llama-3-8b-v1-1-gguf-on-rtx-5060-ti-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Tight fit

56.9 tok/s

1855 ms

19K

Coding

Runs with offload

56.9 tok/s

3401 ms

19K

Agentic Coding

Very compromised (needs ~0.4 GB host RAM)

36.2 tok/s

7777 ms

19K

Reasoning

Runs with offload

56.9 tok/s

4020 ms

19K

RAG

Very compromised (needs ~0.4 GB host RAM)

36.2 tok/s

9721 ms

19K

Quantization options

How llava llama 3 8b v1 1 (8B params) fits at each quantization level on RTX 5060 Ti 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C54
Q3_K_S	3	3.9 GB	Low	C54
NVFP4	4	4.5 GB	Medium	C53
Q4_K_MBest for your GPU	4	4.9 GB	Medium	C53
Q5_K_M	5	5.8 GB	High	F0
Q6_K	6	6.6 GB	High	F0
Q8_0	8	8.6 GB	Very High	F0
F16	16	16.4 GB	Maximum	F0

Frequently asked questions

Can RTX 5060 Ti 8GB run llava llama 3 8b v1 1?

Yes, RTX 5060 Ti 8GB can run llava llama 3 8b v1 1 with a C grade (Runs with offload). Expected decode speed: 56.9 tok/s.

How much VRAM does llava llama 3 8b v1 1 need?

llava llama 3 8b v1 1 (8B parameters) requires approximately 7.8 GB of memory with Q4_K_M quantization.

What is the best quantization for llava llama 3 8b v1 1?

The recommended quantization for llava llama 3 8b v1 1 is Q4_K_M, which balances quality and memory efficiency.

What speed will llava llama 3 8b v1 1 run at on RTX 5060 Ti 8GB?

On RTX 5060 Ti 8GB, llava llama 3 8b v1 1 achieves approximately 56.9 tokens per second decode speed with a time-to-first-token of 3401ms using Q4_K_M quantization.

Can RTX 5060 Ti 8GB run llava llama 3 8b v1 1 for coding?

For coding workloads, llava llama 3 8b v1 1 on RTX 5060 Ti 8GB receives a C grade with 56.9 tok/s and 19K context.

What context window can llava llama 3 8b v1 1 use on RTX 5060 Ti 8GB?

On RTX 5060 Ti 8GB, llava llama 3 8b v1 1 can safely use up to 19K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if llava llama 3 8b v1 1 feels slow on RTX 5060 Ti 8GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.