MiniCPM-V 2.6 8B needs ~8.8 GB VRAM. RTX 2000 Ada Laptop 8GB has 8.0 GB. With Q4_K_M quantization, expect ~23 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
0.8 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~0.5 GB host RAM)
Decode
25.1 tok/s
TTFT
7724 ms
Safe context
2K
Memory
8.8 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.
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.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly {ram} GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs with offload | 41.2 tok/s | 2565 ms | 2K |
| Coding | B | Very compromised | 23.3 tok/s | 8304 ms | 2K |
| Agentic Coding | F | Too heavy | 16.5 tok/s | 17108 ms | 2K |
| Reasoning | A | Very compromised (needs ~0.5 GB host RAM) | 25.1 tok/s | 9129 ms | 2K |
| RAG | F | Too heavy | 16.5 tok/s | 21385 ms | 2K |
How MiniCPM-V 2.6 8B (8B params) fits at each quantization level on RTX 2000 Ada Laptop 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | A84 |
Q3_K_S | 3 | 3.9 GB | Low | A84 |
NVFP4 | 4 |
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 99Upgrade options
RTX 3060 12GBBudget pickRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 108%.
~$329 MSRP
RTX 5060 Ti 16GBBest valueRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 144%.
~$449 MSRP
RTX 4060 Ti 16GBNVIDIA upgradeRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 84%.
~$499 MSRP
Yes, RTX 2000 Ada Laptop 8GB can run MiniCPM-V 2.6 8B with a B grade (Very compromised). Expected decode speed: 23.3 tok/s.
MiniCPM-V 2.6 8B (8B parameters) requires approximately 8.8 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 2000 Ada Laptop 8GB, MiniCPM-V 2.6 8B achieves approximately 23.3 tokens per second decode speed with a time-to-first-token of 8304ms using Q4_K_M quantization.
For coding workloads, MiniCPM-V 2.6 8B on RTX 2000 Ada Laptop 8GB receives a B grade with 23.3 tok/s and 2K context.
On RTX 2000 Ada Laptop 8GB, 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-2000-ada-laptop-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| Medium |
| A84 |
Q4_K_MBest for your GPU | 4 | 4.9 GB | Medium | A83 |
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.