InternVL2 8B needs ~8.5 GB VRAM. RX 9060 8GB has 8.0 GB. With Q4_K_M quantization, expect ~26 tok/s.
Operating mode
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.
Select quantization to explore
0.5 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~0.3 GB host RAM)
Decode
26.2 tok/s
TTFT
7398 ms
Safe context
8K
Memory
8.5 GB / 8.0 GB
Offload
10%
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.
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Tight fit | 40.0 tok/s | 2642 ms | 8K |
| Coding | A | Runs with offload (needs ~0.3 GB host RAM) | 26.2 tok/s | 7398 ms | 8K |
| Agentic Coding | F | Too heavy | 17.0 tok/s | 16605 ms | 8K |
| Reasoning | A | Runs with offload (needs ~0.3 GB host RAM) | 26.2 tok/s | 8743 ms | 8K |
| RAG | F | Too heavy | 17.0 tok/s | 20757 ms | 8K |
How InternVL2 8B (8B params) fits at each quantization level on RX 9060 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 | 4.5 GB | Medium | S85 |
Q4_K_MBest for your GPU | 4 | 4.9 GB | Medium | S85 |
Q5_K_M | 5 | 5.8 GB | High | F0 |
Q6_K | 6 | 6.6 GB | High | F0 |
Q8_0 | 8 | 8.6 GB | Very High | F0 |
F16 | 16 | 16.4 GB | Maximum | F0 |
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 99Your hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen 3.5 9B | 9B | A | 19 tok/s |
Yes, RX 9060 8GB can run InternVL2 8B with a A grade (Runs with offload (needs ~0.3 GB host RAM)). Expected decode speed: 26.2 tok/s.
InternVL2 8B (8B parameters) requires approximately 8.5 GB of memory with Q4_K_M quantization.
The recommended quantization for InternVL2 8B is Q4_K_M, which balances quality and memory efficiency.
On RX 9060 8GB, InternVL2 8B achieves approximately 26.2 tokens per second decode speed with a time-to-first-token of 7398ms using Q4_K_M quantization.
For coding workloads, InternVL2 8B on RX 9060 8GB receives a A grade with 26.2 tok/s and 8K context.
On RX 9060 8GB, 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.
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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/internvl2-8b-on-rx-9060-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: