Can InternVL2 8B run on RTX 4070 Ti Super 16GB?

YES — Runs Great

S87Excellent

Estimated from fit model

InternVL2 8B needs ~9.6 GB VRAM. RTX 4070 Ti Super 16GB has 16.0 GB. With Q4_K_M quantization, expect ~112 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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) — 9.6 GB, 112.0 tok/s, Runs well

9.6 GB required16.0 GB available

60% VRAM used

Fit status

Runs well

Decode

112.0 tok/s

TTFT

1729 ms

Safe context

Memory

9.6 GB / 16.0 GB

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsInternVL2 8B on RTX 4070 Ti Super 16GB
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: 112.0 tok/s decode · 1.7s TTFT (warm) · 280 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Runs well	112.0 tok/s	943 ms	8K
Coding	S	Runs well	112.0 tok/s	1729 ms	8K
Agentic Coding	S	Runs well	112.0 tok/s	2514 ms	8K
Reasoning	S	Runs well	112.0 tok/s	2043 ms	8K
RAG	S	Runs well	112.0 tok/s	3143 ms	8K

Quantization options

How InternVL2 8B (8B params) fits at each quantization level on RTX 4070 Ti Super 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	A79
Q3_K_S	3	3.9 GB	Low	A80
NVFP4	4

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 4070 Ti Super 16GB can run

Model	Params	Grade	Decode	Capabilities
Qwen 3.5 9B	9B	S	105.3 tok/s
Qwen 3 14B	14B	S	68 tok/s

Frequently asked questions

Can RTX 4070 Ti Super 16GB run InternVL2 8B?

Yes, RTX 4070 Ti Super 16GB can run InternVL2 8B with a S grade (Runs well). Expected decode speed: 112.0 tok/s.

How much VRAM does InternVL2 8B need?

InternVL2 8B (8B parameters) requires approximately 9.6 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 4070 Ti Super 16GB?

On RTX 4070 Ti Super 16GB, InternVL2 8B achieves approximately 112.0 tokens per second decode speed with a time-to-first-token of 1729ms using Q4_K_M quantization.

Can RTX 4070 Ti Super 16GB run InternVL2 8B for coding?

For coding workloads, InternVL2 8B on RTX 4070 Ti Super 16GB receives a S grade with 112.0 tok/s and 8K context.

What context window can InternVL2 8B use on RTX 4070 Ti Super 16GB?

On RTX 4070 Ti Super 16GB, 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.

See all results for RTX 4070 Ti Super 16GB See all hardware for InternVL2 8B

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