Can InternVL2 8B run on RTX 5000 Ada Laptop 16GB?

YES — Runs Great

S87Excellent

Estimated from fit model

InternVL2 8B needs ~9.6 GB VRAM. RTX 5000 Ada Laptop 16GB has 16.0 GB. With Q4_K_M quantization, expect ~93 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, 92.6 tok/s, Runs well

9.6 GB required16.0 GB available

60% VRAM used

Fit status

Runs well

Decode

92.6 tok/s

TTFT

2090 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 5000 Ada Laptop 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: 92.6 tok/s decode · 2.1s TTFT (warm) · 232 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	92.6 tok/s	1140 ms	8K
Coding	S	Runs well	92.6 tok/s	2090 ms	8K
Agentic Coding	S	Runs well	92.6 tok/s	3040 ms	8K
Reasoning	S	Runs well	92.6 tok/s	2470 ms	8K
RAG	S	Runs well	92.6 tok/s	3800 ms	8K

Quantization options

How InternVL2 8B (8B params) fits at each quantization level on RTX 5000 Ada Laptop 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 5000 Ada Laptop 16GB can run

Model	Params	Grade	Decode	Capabilities
Qwen 3.5 9B	9B	S	82.3 tok/s
Qwen 3 14B	14B	S	53.2 tok/s

Frequently asked questions

Can RTX 5000 Ada Laptop 16GB run InternVL2 8B?

Yes, RTX 5000 Ada Laptop 16GB can run InternVL2 8B with a S grade (Runs well). Expected decode speed: 92.6 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 5000 Ada Laptop 16GB?

On RTX 5000 Ada Laptop 16GB, InternVL2 8B achieves approximately 92.6 tokens per second decode speed with a time-to-first-token of 2090ms using Q4_K_M quantization.

Can RTX 5000 Ada Laptop 16GB run InternVL2 8B for coding?

For coding workloads, InternVL2 8B on RTX 5000 Ada Laptop 16GB receives a S grade with 92.6 tok/s and 8K context.

What context window can InternVL2 8B use on RTX 5000 Ada Laptop 16GB?

On RTX 5000 Ada Laptop 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 5000 Ada Laptop 16GB See all hardware for InternVL2 8B

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