MiniCPM-V 2.6 8B needs ~11.2 GB VRAM. MacBook Pro M2 Max 32GB has 23.0 GB. With Q4_K_M quantization, expect ~48 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
51.1 tok/s
TTFT
3788 ms
Safe context
2K
Memory
11.2 GB / 23.0 GB
This setup is broadly balanced for this model.
Shared-memory contention still exists
The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs well | 47.5 tok/s | 2221 ms | 2K |
| Coding | A | Runs well | 47.5 tok/s | 4072 ms | 2K |
| Agentic Coding | A | Runs well | 47.5 tok/s | 5923 ms | 2K |
| Reasoning | A | Runs well | 47.5 tok/s | 4813 ms | 2K |
| RAG | A | Runs well | 47.5 tok/s | 7404 ms | 2K |
How MiniCPM-V 2.6 8B (8B params) fits at each quantization level on MacBook Pro M2 Max 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | A75 |
Q3_K_S | 3 | 3.9 GB | Low | A76 |
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 99Your hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen3-Coder 30B A3B Instruct | 30.5B | A | 31.5 tok/s | |
| Qwen 3.5 27B | 27B | S | 14.1 tok/s |
Yes, MacBook Pro M2 Max 32GB can run MiniCPM-V 2.6 8B with a A grade (Runs well). Expected decode speed: 47.5 tok/s.
MiniCPM-V 2.6 8B (8B parameters) requires approximately 11.2 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 MacBook Pro M2 Max 32GB, MiniCPM-V 2.6 8B achieves approximately 47.5 tokens per second decode speed with a time-to-first-token of 4072ms using Q4_K_M quantization.
For coding workloads, MiniCPM-V 2.6 8B on MacBook Pro M2 Max 32GB receives a A grade with 47.5 tok/s and 2K context.
On MacBook Pro M2 Max 32GB, 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-m2-max-32gb" 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 |
| A76 |
Q4_K_M | 4 | 4.9 GB | Medium | A76 |
Q5_K_M | 5 | 5.8 GB | High | A77 |
Q6_K | 6 | 6.6 GB | High | A77 |
Q8_0 | 8 | 8.6 GB | Very High | A79 |
F16Best for your GPU | 16 | 16.4 GB | Maximum | A80 |
| Qwen 3.6 27B | 27B | S | 11.6 tok/s |
| Qwen3-VL 30B A3B Instruct | 30B | S | 33.3 tok/s |
| Qwen 3.5 9B | 9B | S | 45.4 tok/s |
Not always. MacBook Pro M2 Max 32GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.