InternVL2 8B needs ~11.2 GB VRAM. MacBook Pro M4 32GB has 23.0 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
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 | 17.7 tok/s | 5964 ms | 8K |
| Coding | A | Runs well | 17.7 tok/s | 10935 ms | 8K |
| Agentic Coding | A | Runs well | 17.7 tok/s | 15905 ms | 8K |
| Reasoning | A | Runs well | 17.7 tok/s | 12923 ms | 8K |
| RAG | A | Runs well | 17.7 tok/s | 19881 ms | 8K |
How InternVL2 8B (8B params) fits at each quantization level on MacBook Pro M4 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 |
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 | 11.7 tok/s | |
| Qwen 3.5 27B | 27B | S | 8.6 tok/s |
Yes, MacBook Pro M4 32GB can run InternVL2 8B with a A grade (Runs well). Expected decode speed: 17.7 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 M4 32GB, InternVL2 8B achieves approximately 17.7 tokens per second decode speed with a time-to-first-token of 10935ms using Q4_K_M quantization.
For coding workloads, InternVL2 8B on MacBook Pro M4 32GB receives a A grade with 17.7 tok/s and 8K context.
On MacBook Pro M4 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/internvl2-8b-on-m4-32gb" 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 |
| 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 |
| Qwen 3.6 27B | 27B | S | 7.1 tok/s |
| Qwen3-VL 30B A3B Instruct | 30B | S | 12.4 tok/s |
| Qwen 3.5 9B | 9B | S | 15.6 tok/s |
Not always. MacBook Pro M4 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.