Can InternVL2 8B run on RX 580 8GB?

YES — With Offload

A70Great

○Estimated from fit model

InternVL2 8B needs ~8.5 GB VRAM. RX 580 8GB has 8.0 GB. With Q4_K_M quantization, expect ~15 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, 15.4 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

15.4 tok/s

TTFT

12611 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 RX 580 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: 15.4 tok/s decode · 12.6s TTFT (warm) · 38 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.

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

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 0.3 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	24.2 tok/s	4355 ms	8K
Coding	A	Runs with offload (needs ~0.3 GB host RAM)	15.4 tok/s	12611 ms	8K
Agentic Coding	F	Too heavy	9.8 tok/s	28844 ms	8K
Reasoning	A	Runs with offload (needs ~0.3 GB host RAM)	15.4 tok/s	14904 ms	8K
RAG	F	Too heavy	9.8 tok/s	36055 ms

Quantization options

How InternVL2 8B (8B params) fits at each quantization level on RX 580 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

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 RX 580 8GB can run

Model	Params	Grade	Decode	Capabilities
👁 Alibaba Qwen 3.5 9B	9B	A	11.1 tok/s

Frequently asked questions

See all results for RX 580 8GB See all hardware for InternVL2 8B

URL: https://willitrunai.com/can-run/internvl2-8b-on-rx-580-8gb

⇱