InternVL2 8B needs ~8.5 GB VRAM. Intel Arc A550M 8GB has 8.0 GB. With Q4_K_M quantization, expect ~16 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
15.8 tok/s
TTFT
12229 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.
Runtime ecosystem is narrower than CUDA
Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.
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.
Prefer CUDA if you want the path of least resistance
If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Tight fit | 24.2 tok/s | 4367 ms | 8K |
| Coding | A | Runs with offload (needs ~0.3 GB host RAM) | 15.8 tok/s | 12229 ms | 8K |
| Agentic Coding | F | Too heavy | 10.3 tok/s | 27450 ms | 8K |
| Reasoning | A | Runs with offload (needs ~0.3 GB host RAM) | 15.8 tok/s | 14452 ms | 8K |
| RAG | F | Too heavy | 10.3 tok/s | 34312 ms |
How InternVL2 8B (8B params) fits at each quantization level on Intel Arc A550M 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 |
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 | 11.5 tok/s |
Yes, Intel Arc A550M 8GB can run InternVL2 8B with a A grade (Runs with offload (needs ~0.3 GB host RAM)). Expected decode speed: 15.8 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 Intel Arc A550M 8GB, InternVL2 8B achieves approximately 15.8 tokens per second decode speed with a time-to-first-token of 12229ms using Q4_K_M quantization.
For coding workloads, InternVL2 8B on Intel Arc A550M 8GB receives a A grade with 15.8 tok/s and 8K context.
On Intel Arc A550M 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/internvl2-8b-on-arc-a550m-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 8K |
| 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 |
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.
Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.