Can InternVL2 8B run on RTX 5060 Ti 8GB?

YES — With Offload

A73Great

Estimated from fit model

InternVL2 8B needs ~8.5 GB VRAM. RTX 5060 Ti 8GB has 8.0 GB. With Q4_K_M quantization, expect ~38 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: Host offload

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) — 8.5 GB, 41.0 tok/s, Runs with offload (needs ~0.3 GB host RAM)

8.5 GB required8.0 GB available

106% VRAM needed

0.5 GB over capacity — needs offload or smaller quantization

Fit status

Runs with offload (needs ~0.3 GB host RAM)

Decode

41.0 tok/s

TTFT

4720 ms

Safe context

Memory

8.5 GB / 8.0 GB

Offload

10%

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsInternVL2 8B 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: 41.0 tok/s decode · 4.7s TTFT (warm) · 103 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

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

Increase host RAM if you keep offloading

This setup may need roughly {ram} GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Tight fit	61.2 tok/s	1726 ms	8K
Coding	A	Runs with offload	38.2 tok/s	5074 ms	8K
Agentic Coding	F	Too heavy	26.9 tok/s	10453 ms	8K
Reasoning	A	Runs with offload (needs ~0.3 GB host RAM)	41.0 tok/s	5578 ms	8K
RAG	F	Too heavy	26.9 tok/s	13066 ms	8K

Quantization options

How InternVL2 8B (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	S86
Q3_K_S	3	3.9 GB	Low	S86
NVFP4	4	4.5 GB	Medium	S85
Q4_K_MBest for your GPU	4	4.9 GB	Medium	S85
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 InternVL2 8B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "OpenGVLab/InternVL2-8B" \
  --hf-file "InternVL2-8B-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Your hardware

More models your RTX 5060 Ti 8GB can run

Model	Params	Grade	Decode	Capabilities
Qwen 3.5 9B	9B	A	30 tok/s

Frequently asked questions

Can RTX 5060 Ti 8GB run InternVL2 8B?

Yes, RTX 5060 Ti 8GB can run InternVL2 8B with a A grade (Runs with offload). Expected decode speed: 38.2 tok/s.

How much VRAM does InternVL2 8B need?

InternVL2 8B (8B parameters) requires approximately 8.5 GB of memory with Q4_K_M quantization.

What is the best quantization for InternVL2 8B?

The recommended quantization for InternVL2 8B is Q4_K_M, which balances quality and memory efficiency.

What speed will InternVL2 8B run at on RTX 5060 Ti 8GB?

On RTX 5060 Ti 8GB, InternVL2 8B achieves approximately 38.2 tokens per second decode speed with a time-to-first-token of 5074ms using Q4_K_M quantization.

Can RTX 5060 Ti 8GB run InternVL2 8B for coding?

For coding workloads, InternVL2 8B on RTX 5060 Ti 8GB receives a A grade with 38.2 tok/s and 8K context.

What context window can InternVL2 8B use on RTX 5060 Ti 8GB?

On RTX 5060 Ti 8GB, InternVL2 8B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

What should I upgrade first if InternVL2 8B feels slow on RTX 5060 Ti 8GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

See all results for RTX 5060 Ti 8GB See all hardware for InternVL2 8B

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