Can InternVL2 8B run on Radeon RX 7900M 16GB?

YES — Runs Great

S86Excellent

○Estimated from fit model

InternVL2 8B needs ~9.3 GB VRAM. Radeon RX 7900M 16GB has 16.0 GB. With Q4_K_M quantization, expect ~75 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) — 9.3 GB, 74.9 tok/s, Runs well

9.3 GB required16.0 GB available

58% VRAM used

Fit status

Runs well

Decode

74.9 tok/s

TTFT

2586 ms

Safe context

Memory

9.3 GB / 16.0 GB

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsInternVL2 8B on Radeon RX 7900M 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: 74.9 tok/s decode · 2.6s TTFT (warm) · 187 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	A	Runs well	74.9 tok/s	1411 ms	8K
Coding	S	Runs well	74.9 tok/s	2586 ms	8K
Agentic Coding	S	Runs well	74.9 tok/s	3762 ms	8K
Reasoning	S	Runs well	74.9 tok/s	3056 ms	8K
RAG	S	Runs well	74.9 tok/s	4702 ms	8K

Quantization options

How InternVL2 8B (8B params) fits at each quantization level on Radeon RX 7900M 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	4.5 GB	Medium	A81
Q4_K_M	4	4.9 GB	Medium	A81
Q5_K_M	5	5.8 GB	High	A82
Q6_K	6	6.6 GB	High	A83
Q8_0Best for your GPU	8	8.6 GB	Very High	A84
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 Radeon RX 7900M 16GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen 3.5 9B	9B	S	66.5 tok/s
👁 Alibaba Qwen 3 14B	14B	S	43 tok/s
👁 Microsoft Phi-4-reasoning-plus 14B	14.7B	S	40.7 tok/s
👁 OpenAI GPT-OSS 20B	21B	A	39.3 tok/s
👁 Mistral Ministral 3 14B	14B	S	42.8 tok/s

Frequently asked questions

See all results for Radeon RX 7900M 16GB See all hardware for InternVL2 8B

URL: https://willitrunai.com/can-run/internvl2-8b-on-rx-7900m-16gb

⇱ InternVL2 8B on Radeon RX 7900M 16GB? YES