InternVL2 8B needs ~9.6 GB VRAM. NVIDIA A2 16GB has 16.0 GB. With Q4_K_M quantization, expect ~32 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
34.4 tok/s
TTFT
5634 ms
Safe context
8K
Memory
9.6 GB / 16.0 GB
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs well | 32.0 tok/s | 3303 ms | 8K |
| Coding | A | Runs well | 32.0 tok/s | 6056 ms | 8K |
| Agentic Coding | S | Runs well | 32.0 tok/s | 8809 ms | 8K |
| Reasoning | A | Runs well | 32.0 tok/s | 7157 ms | 8K |
| RAG | S | Runs well | 32.0 tok/s | 11011 ms | 8K |
How InternVL2 8B (8B params) fits at each quantization level on NVIDIA A2 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 |
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 |
|---|---|---|---|---|
| 9B | S | 30.5 tok/s | ||
| 14B | S | 19.7 tok/s |
Yes, NVIDIA A2 16GB can run InternVL2 8B with a A grade (Runs well). Expected decode speed: 32.0 tok/s.
InternVL2 8B (8B parameters) requires approximately 9.6 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 NVIDIA A2 16GB, InternVL2 8B achieves approximately 32.0 tokens per second decode speed with a time-to-first-token of 6056ms using Q4_K_M quantization.
For coding workloads, InternVL2 8B on NVIDIA A2 16GB receives a A grade with 32.0 tok/s and 8K context.
On NVIDIA A2 16GB, 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-a2-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
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 |
| 14.7B | S | 18.7 tok/s |
| 21B | A | 17.4 tok/s |
| 14B | A | 19.6 tok/s |