Can InternVL2 8B run on GTX 1080 Ti 11GB?

YES — Runs Great

S88Excellent

○Estimated from fit model

InternVL2 8B needs ~8.8 GB VRAM. GTX 1080 Ti 11GB has 11.0 GB. With Q4_K_M quantization, expect ~63 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: StandardBottleneck: 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) — 8.8 GB, 62.9 tok/s, Runs well

8.8 GB required11.0 GB available

80% VRAM used

Fit status

Runs well

Decode

62.9 tok/s

TTFT

3078 ms

Safe context

Memory

8.8 GB / 11.0 GB

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom1.1 GB

See how fast it feels

See how fast it feelsInternVL2 8B on GTX 1080 Ti 11GB
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: 62.9 tok/s decode · 3.1s TTFT (warm) · 157 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Runs well	62.9 tok/s	1679 ms	8K
Coding	S	Runs well	62.9 tok/s	3078 ms	8K
Agentic Coding	A	Runs with offload	62.9 tok/s	4477 ms	8K
Reasoning	S	Runs well	62.9 tok/s	3637 ms	8K
RAG	A	Runs with offload	62.9 tok/s	5596 ms	8K

Quantization options

How InternVL2 8B (8B params) fits at each quantization level on GTX 1080 Ti 11GB (11.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	A83
Q3_K_S	3	3.9 GB	Low	A84
NVFP4	4	4.5 GB	Medium	A85
Q4_K_M	4	4.9 GB	Medium	S85
Q5_K_M	5	5.8 GB	High	A85
Q6_KBest for your GPU	6	6.6 GB	High	A84
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 GTX 1080 Ti 11GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen 3.5 9B	9B	S	55.9 tok/s
👁 Alibaba Qwen 3 14B	14B	A	18.3 tok/s
👁 Mistral Ministral 3 14B	14B	B	18.2 tok/s

Frequently asked questions

See all results for GTX 1080 Ti 11GB See all hardware for InternVL2 8B

URL: https://willitrunai.com/can-run/internvl2-8b-on-gtx-1080-ti-11gb