InternVL2 8B needs ~9.5 GB VRAM. MacBook Pro M1 Pro 16GB has 11.5 GB. With Q4_K_M quantization, expect ~29 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
Tight fit
Decode
28.6 tok/s
TTFT
6760 ms
Safe context
8K
Memory
9.5 GB / 11.5 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 | S | Runs well | 28.6 tok/s | 3687 ms | 8K |
| Coding | A | Tight fit | 28.6 tok/s | 6760 ms | 8K |
| Agentic Coding | A | Runs with offload | 28.6 tok/s | 9833 ms | 8K |
| Reasoning | A | Tight fit | 28.6 tok/s | 7990 ms | 8K |
| RAG | A | Runs with offload | 28.6 tok/s | 12292 ms | 8K |
How InternVL2 8B (8B params) fits at each quantization level on MacBook Pro M1 Pro 16GB (11.5 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | A82 |
Q3_K_S | 3 | 3.9 GB | Low | A83 |
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 |
|---|---|---|---|---|
 Qwen 3.5 9B | 9B | S | 25.5 tok/s | |
| Qwen 3 14B | 14B | A | 12.8 tok/s |
Yes, MacBook Pro M1 Pro 16GB can run InternVL2 8B with a A grade (Tight fit). Expected decode speed: 28.6 tok/s.
InternVL2 8B (8B parameters) requires approximately 9.5 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 M1 Pro 16GB, InternVL2 8B achieves approximately 28.6 tokens per second decode speed with a time-to-first-token of 6760ms using Q4_K_M quantization.
For coding workloads, InternVL2 8B on MacBook Pro M1 Pro 16GB receives a A grade with 28.6 tok/s and 8K context.
On MacBook Pro M1 Pro 16GB, 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-m1-pro-16gb" 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 |
| A84 |
Q4_K_M | 4 | 4.9 GB | Medium | A85 |
Q5_K_M | 5 | 5.8 GB | High | A85 |
Q6_KBest for your GPU | 6 | 6.6 GB | High | A84 |
Q8_0 | 8 | 8.6 GB | Very High | F0 |
F16 | 16 | 16.4 GB | Maximum | F0 |
 Ministral 3 14B | 14B | B | 12.7 tok/s |
Not always. MacBook Pro M1 Pro 16GB 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.