MiniCPM-V 2.6 8B needs ~9.2 GB VRAM. RTX 4080 Laptop 12GB has 12.0 GB. With Q4_K_M quantization, expect ~74 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
74.2 tok/s
TTFT
2608 ms
Safe context
2K
Memory
9.2 GB / 12.0 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 | S | Runs well | 74.2 tok/s | 1423 ms | 2K |
| Coding | S | Runs well | 74.2 tok/s | 2608 ms | 2K |
| Agentic Coding | A | Tight fit | 74.2 tok/s | 3794 ms | 2K |
| Reasoning | S | Runs well | 74.2 tok/s | 3082 ms | 2K |
| RAG | A | Tight fit | 74.2 tok/s | 4742 ms | 2K |
How MiniCPM-V 2.6 8B (8B params) fits at each quantization level on RTX 4080 Laptop 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | A80 |
Q3_K_S | 3 | 3.9 GB | Low | A81 |
NVFP4 | 4 | 4.5 GB | Medium | A82 |
Q4_K_M | 4 | 4.9 GB | Medium | A82 |
Q5_K_M | 5 | 5.8 GB | High | A83 |
Q6_K | 6 | 6.6 GB | High | A83 |
Q8_0Best for your GPU | 8 | 8.6 GB | Very High | A82 |
F16 | 16 | 16.4 GB | Maximum | F0 |
Copy-paste commands to run MiniCPM-V 2.6 8B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "openbmb/MiniCPM-V-2_6" \
--hf-file "MiniCPM-V-2_6-Q4_K_M.gguf" \
-c 4096 -ngl 99Your hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen 3.5 9B | 9B | S | 66 tok/s | |
| Qwen 3 14B | 14B | A | 25.4 tok/s | |
 Ministral 3 14B | 14B | A | 25.3 tok/s | |
 Phi-4 14B | 14B | A | 23 tok/s | |
| Qwen 2.5 14B | 14B | A | 23.6 tok/s |
Yes, RTX 4080 Laptop 12GB can run MiniCPM-V 2.6 8B with a S grade (Runs well). Expected decode speed: 74.2 tok/s.
MiniCPM-V 2.6 8B (8B parameters) requires approximately 9.2 GB of memory with Q4_K_M quantization.
The recommended quantization for MiniCPM-V 2.6 8B is Q4_K_M, which balances quality and memory efficiency.
On RTX 4080 Laptop 12GB, MiniCPM-V 2.6 8B achieves approximately 74.2 tokens per second decode speed with a time-to-first-token of 2608ms using Q4_K_M quantization.
For coding workloads, MiniCPM-V 2.6 8B on RTX 4080 Laptop 12GB receives a S grade with 74.2 tok/s and 2K context.
On RTX 4080 Laptop 12GB, MiniCPM-V 2.6 8B can safely use up to 2K tokens of context. The model's official context limit is 2K, 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/minicpm-v-2.6-8b-on-rtx-4080-laptop-12gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: