MiniCPM-V 2.6 8B needs ~10.3 GB VRAM. MacBook Air M4 24GB has 17.3 GB. With Q4_K_M quantization, expect ~18 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
17.5 tok/s
TTFT
11056 ms
Safe context
2K
Memory
10.3 GB / 17.3 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 | 17.5 tok/s | 6031 ms | 2K |
| Coding | A | Runs well | 17.5 tok/s | 11056 ms | 2K |
| Agentic Coding | A | Runs well | 17.5 tok/s | 16082 ms | 2K |
| Reasoning | A | Runs well | 17.5 tok/s | 13067 ms | 2K |
| RAG | A | Runs well | 17.5 tok/s | 20103 ms | 2K |
How MiniCPM-V 2.6 8B (8B params) fits at each quantization level on MacBook Air M4 24GB (17.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | A77 |
Q3_K_S | 3 | 3.9 GB | Low | A78 |
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 |
|---|---|---|---|---|
 Qwen 3.5 9B | 9B | S | 15.6 tok/s | |
 Magistral Small 2507 | 24B | A | 7.3 tok/s |
Yes, MacBook Air M4 24GB can run MiniCPM-V 2.6 8B with a A grade (Runs well). Expected decode speed: 17.5 tok/s.
MiniCPM-V 2.6 8B (8B parameters) requires approximately 10.3 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 Air M4 24GB, MiniCPM-V 2.6 8B achieves approximately 17.5 tokens per second decode speed with a time-to-first-token of 11056ms using Q4_K_M quantization.
For coding workloads, MiniCPM-V 2.6 8B on MacBook Air M4 24GB receives a A grade with 17.5 tok/s and 2K context.
On MacBook Air M4 24GB, 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-m4-air-24gb" 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 |
| A78 |
Q4_K_M | 4 | 4.9 GB | Medium | A78 |
Q5_K_M | 5 | 5.8 GB | High | A79 |
Q6_K | 6 | 6.6 GB | High | A80 |
Q8_0Best for your GPU | 8 | 8.6 GB | Very High | A82 |
F16 | 16 | 16.4 GB | Maximum | F0 |
| Devstral Small 2 24B Instruct | 24B | A | 7.3 tok/s |
| Qwen 3 14B | 14B | S | 9.6 tok/s |
 Phi-4-reasoning-plus 14B | 14.7B | S | 9.4 tok/s |
Not always. MacBook Air M4 24GB 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.