InternVL2 8B needs ~11.2 GB VRAM. MacBook Pro M2 Pro 32GB has 23.0 GB. With Q4_K_M quantization, expect ~31 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
30.8 tok/s
TTFT
6278 ms
Safe context
8K
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 | 30.8 tok/s | 3424 ms | 8K |
| Coding | A | Runs well | 30.8 tok/s | 6278 ms | 8K |
| Agentic Coding | A | Runs well | 30.8 tok/s | 9131 ms | 8K |
| Reasoning | A | Runs well | 30.8 tok/s | 7419 ms | 8K |
| RAG | A | Runs well | 30.8 tok/s | 11414 ms | 8K |
How InternVL2 8B (8B params) fits at each quantization level on MacBook Pro M2 Pro 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | A77 |
Q3_K_S | 3 | 3.9 GB | Low | A77 |
NVFP4 | 4 | 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 | A79 |
Q8_0 | 8 | 8.6 GB | Very High | A80 |
F16Best for your GPU | 16 | 16.4 GB | Maximum | A82 |
Copy-paste commands to run InternVL2 8B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "OpenGVLab/InternVL2-8B" \
--hf-file "InternVL2-8B-Q4_K_M.gguf" \
-c 4096 -ngl 99Your hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen3-Coder 30B A3B Instruct | 30.5B | A | 19 tok/s | |
| Qwen 3.5 27B | 27B | S | 8.5 tok/s | |
| Qwen 3.6 27B | 27B | S | 7 tok/s | |
| Qwen3-VL 30B A3B Instruct | 30B | S | 20.1 tok/s | |
| Qwen 3.5 9B | 9B | S | 27.4 tok/s |
Yes, MacBook Pro M2 Pro 32GB can run InternVL2 8B with a A grade (Runs well). Expected decode speed: 30.8 tok/s.
InternVL2 8B (8B parameters) requires approximately 11.2 GB of memory with Q4_K_M quantization.
The recommended quantization for InternVL2 8B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M2 Pro 32GB, InternVL2 8B achieves approximately 30.8 tokens per second decode speed with a time-to-first-token of 6278ms using Q4_K_M quantization.
For coding workloads, InternVL2 8B on MacBook Pro M2 Pro 32GB receives a A grade with 30.8 tok/s and 8K context.
On MacBook Pro M2 Pro 32GB, InternVL2 8B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.
Not always. MacBook Pro M2 Pro 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/internvl2-8b-on-m2-pro-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: