InternLM 7B needs ~16.4 GB VRAM. MacBook Pro M2 Pro 32GB has 23.0 GB. With Q4_K_M quantization, expect ~33 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
32.8 tok/s
TTFT
5905 ms
Safe context
8K
Memory
16.4 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 | 32.8 tok/s | 3221 ms | 8K |
| Coding | A | Runs well | 32.8 tok/s | 5905 ms | 8K |
| Agentic Coding | A | Runs with offload (needs ~0.2 GB host RAM) | 29.8 tok/s | 9448 ms | 8K |
| Reasoning | A | Runs well | 32.8 tok/s | 6978 ms | 8K |
| RAG | A | Runs with offload (needs ~0.2 GB host RAM) | 29.8 tok/s | 11810 ms |
How InternLM 7B (7B params) fits at each quantization level on MacBook Pro M2 Pro 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | B65 |
Q3_K_S | 3 | 3.4 GB | Low | B65 |
NVFP4 | 4 |
Copy-paste commands to run InternLM 7B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "InternLM/InternLM-7B" \
--hf-file "InternLM-7B-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 |
Yes, MacBook Pro M2 Pro 32GB can run InternLM 7B with a A grade (Runs well). Expected decode speed: 32.8 tok/s.
InternLM 7B (7B parameters) requires approximately 16.4 GB of memory with Q4_K_M quantization.
The recommended quantization for InternLM 7B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M2 Pro 32GB, InternLM 7B achieves approximately 32.8 tokens per second decode speed with a time-to-first-token of 5905ms using Q4_K_M quantization.
For coding workloads, InternLM 7B on MacBook Pro M2 Pro 32GB receives a A grade with 32.8 tok/s and 8K context.
On MacBook Pro M2 Pro 32GB, InternLM 7B 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/internlm-7b-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:
| 8K |
3.9 GB |
| Medium |
| B66 |
Q4_K_M | 4 | 4.3 GB | Medium | B66 |
Q5_K_M | 5 | 5.0 GB | High | B66 |
Q6_K | 6 | 5.7 GB | High | B67 |
Q8_0 | 8 | 7.5 GB | Very High | B68 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | A71 |
| 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 |
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.