MiniCPM-V 2.6 8B needs ~8.5 GB VRAM. RX 6600 XT 8GB has 8.0 GB. With Q4_K_M quantization, expect ~19 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
0.5 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~0.3 GB host RAM)
Decode
18.5 tok/s
TTFT
10486 ms
Safe context
2K
Memory
8.5 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 0.3 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Tight fit | 28.2 tok/s | 3745 ms | 2K |
| Coding | B | Runs with offload (needs ~0.3 GB host RAM) | 18.5 tok/s | 10486 ms | 2K |
| Agentic Coding | F | Too heavy | 12.0 tok/s | 23538 ms | 2K |
| Reasoning | B | Runs with offload (needs ~0.3 GB host RAM) | 18.5 tok/s | 12393 ms | 2K |
| RAG | F | Too heavy | 12.0 tok/s | 29423 ms |
How MiniCPM-V 2.6 8B (8B params) fits at each quantization level on RX 6600 XT 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
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 99%.
~$329 MSRP
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 140%.
~$349 MSRP
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 209%.
~$449 MSRP
Yes, RX 6600 XT 8GB can run MiniCPM-V 2.6 8B with a B grade (Runs with offload (needs ~0.3 GB host RAM)). Expected decode speed: 18.5 tok/s.
MiniCPM-V 2.6 8B (8B parameters) requires approximately 8.5 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 RX 6600 XT 8GB, MiniCPM-V 2.6 8B achieves approximately 18.5 tokens per second decode speed with a time-to-first-token of 10486ms using Q4_K_M quantization.
For coding workloads, MiniCPM-V 2.6 8B on RX 6600 XT 8GB receives a B grade with 18.5 tok/s and 2K context.
On RX 6600 XT 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-rx-6600-xt-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 2K |
| 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.