InternVL2 8B needs ~8.9 GB VRAM. Intel Arc Pro A60 12GB has 12.0 GB. With Q4_K_M quantization, expect ~41 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
Fit status
Runs well
Decode
41.4 tok/s
TTFT
4671 ms
Safe context
8K
Memory
8.9 GB / 12.0 GB
The raw memory story may look fine, but the software ecosystem is still a constraint here.
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.
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | S | Runs well | 41.4 tok/s | 2548 ms | 8K |
| Coding | S | Runs well | 41.4 tok/s | 4671 ms | 8K |
| Agentic Coding | A | Tight fit | 41.4 tok/s | 6794 ms | 8K |
| Reasoning | S | Runs well | 41.4 tok/s | 5520 ms | 8K |
| RAG | A | Tight fit | 41.4 tok/s | 8492 ms | 8K |
How InternVL2 8B (8B params) fits at each quantization level on Intel Arc Pro A60 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | A82 |
Q3_K_S | 3 | 3.9 GB | Low | A83 |
NVFP4 | 4 | 4.5 GB | Medium | A84 |
Q4_K_M | 4 | 4.9 GB | Medium | A84 |
Q5_K_M | 5 | 5.8 GB | High | A85 |
Q6_K | 6 | 6.6 GB | High | A84 |
Q8_0Best for your GPU | 8 | 8.6 GB | Very High | A84 |
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 | S | 36.8 tok/s | |
| Qwen 3 14B | 14B | A | 14.9 tok/s | |
 Phi-4-reasoning-plus 14B | 14.7B | A | 12 tok/s | |
 Ministral 3 14B | 14B | A | 14.8 tok/s |
Yes, Intel Arc Pro A60 12GB can run InternVL2 8B with a S grade (Runs well). Expected decode speed: 41.4 tok/s.
InternVL2 8B (8B parameters) requires approximately 8.9 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 Pro A60 12GB, InternVL2 8B achieves approximately 41.4 tokens per second decode speed with a time-to-first-token of 4671ms using Q4_K_M quantization.
For coding workloads, InternVL2 8B on Intel Arc Pro A60 12GB receives a S grade with 41.4 tok/s and 8K context.
On Intel Arc Pro A60 12GB, 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.
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.
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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/internvl2-8b-on-arc-pro-a60-12gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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