InternVL2 8B needs ~10.3 GB VRAM. MacBook Pro M3 24GB has 17.3 GB. With Q4_K_M quantization, expect ~15 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
15.0 tok/s
TTFT
12924 ms
Safe context
8K
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 | 15.0 tok/s | 7050 ms | 8K |
| Coding | A | Runs well | 15.0 tok/s | 12924 ms | 8K |
| Agentic Coding | A | Runs well | 15.0 tok/s | 18799 ms | 8K |
| Reasoning | A | Runs well | 15.0 tok/s | 15274 ms | 8K |
| RAG | A | Runs well | 15.0 tok/s | 23499 ms | 8K |
How InternVL2 8B (8B params) fits at each quantization level on MacBook Pro M3 24GB (17.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | A79 |
Q3_K_S | 3 | 3.9 GB | Low | A79 |
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 | 13.3 tok/s | |
 Magistral Small 2507 | 24B | B | 3.8 tok/s |
Yes, MacBook Pro M3 24GB can run InternVL2 8B with a A grade (Runs well). Expected decode speed: 15.0 tok/s.
InternVL2 8B (8B parameters) requires approximately 10.3 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 M3 24GB, InternVL2 8B achieves approximately 15.0 tokens per second decode speed with a time-to-first-token of 12924ms using Q4_K_M quantization.
For coding workloads, InternVL2 8B on MacBook Pro M3 24GB receives a A grade with 15.0 tok/s and 8K context.
On MacBook Pro M3 24GB, 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-m3-24gb" 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 |
| A80 |
Q4_K_M | 4 | 4.9 GB | Medium | A80 |
Q5_K_M | 5 | 5.8 GB | High | A81 |
Q6_K | 6 | 6.6 GB | High | A82 |
Q8_0Best for your GPU | 8 | 8.6 GB | Very High | A84 |
F16 | 16 | 16.4 GB | Maximum | F0 |
| Devstral Small 2 24B Instruct | 24B | B | 3.8 tok/s |
| Qwen 3 14B | 14B | S | 8.6 tok/s |
 Phi-4-reasoning-plus 14B | 14.7B | S | 8.2 tok/s |
Not always. MacBook Pro M3 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.