InternVL2 8B needs ~10.4 GB VRAM. NVIDIA A10 24GB has 24.0 GB. With Q4_K_M quantization, expect ~103 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
103.1 tok/s
TTFT
1878 ms
Safe context
8K
Memory
10.4 GB / 24.0 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 | 103.1 tok/s | 1024 ms | 8K |
| Coding | A | Runs well | 103.1 tok/s | 1878 ms | 8K |
| Agentic Coding | S | Runs well | 103.1 tok/s | 2731 ms | 8K |
| Reasoning | A | Runs well | 103.1 tok/s | 2219 ms | 8K |
| RAG | S | Runs well | 103.1 tok/s | 3414 ms | 8K |
How InternVL2 8B (8B params) fits at each quantization level on NVIDIA A10 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | A77 |
Q3_K_S | 3 | 3.9 GB | Low | A77 |
NVFP4 | 4 | 4.5 GB | Medium | A78 |
Q4_K_M | 4 | 4.9 GB | Medium | A78 |
Q5_K_M | 5 | 5.8 GB | High | A78 |
Q6_K | 6 | 6.6 GB | High | A79 |
Q8_0 | 8 | 8.6 GB | Very High | A80 |
F16Best for your GPU | 16 | 16.4 GB | Maximum | A82 |
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 |
|---|---|---|---|---|
 Qwen3-Coder 30B A3B Instruct | 30.5B | S | 70.8 tok/s | |
| Qwen 3.5 27B | 27B | S | 30.7 tok/s | |
| Qwen 3.6 27B | 27B | S | 30.8 tok/s | |
| Qwen3-VL 30B A3B Instruct | 30B | S | 73.2 tok/s | |
| Qwen 3.5 9B | 9B | S | 91.6 tok/s |
Yes, NVIDIA A10 24GB can run InternVL2 8B with a A grade (Runs well). Expected decode speed: 103.1 tok/s.
InternVL2 8B (8B parameters) requires approximately 10.4 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 A10 24GB, InternVL2 8B achieves approximately 103.1 tokens per second decode speed with a time-to-first-token of 1878ms using Q4_K_M quantization.
For coding workloads, InternVL2 8B on NVIDIA A10 24GB receives a A grade with 103.1 tok/s and 8K context.
On NVIDIA A10 24GB, 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-a10-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: