InternLM 7B needs ~15.6 GB VRAM. MacBook Pro M4 Pro 24GB has 17.3 GB. With Q4_K_M quantization, expect ~45 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
45.3 tok/s
TTFT
4275 ms
Safe context
8K
Memory
15.6 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 | 45.3 tok/s | 2332 ms | 8K |
| Coding | A | Tight fit | 45.3 tok/s | 4275 ms | 8K |
| Agentic Coding | F | Too heavy | 29.8 tok/s | 9443 ms | 8K |
| Reasoning | A | Tight fit | 45.3 tok/s | 5052 ms | 8K |
| RAG | F | Too heavy | 29.8 tok/s | 11804 ms | 8K |
How InternLM 7B (7B params) fits at each quantization level on MacBook Pro M4 Pro 24GB (17.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | B67 |
Q3_K_S | 3 | 3.4 GB | Low | B67 |
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 |
|---|---|---|---|---|
 Qwen 3.5 9B | 9B | S | 37.9 tok/s | |
 Magistral Small 2507 | 24B | A | 17.8 tok/s |
Yes, MacBook Pro M4 Pro 24GB can run InternLM 7B with a A grade (Tight fit). Expected decode speed: 45.3 tok/s.
InternLM 7B (7B parameters) requires approximately 15.6 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 M4 Pro 24GB, InternLM 7B achieves approximately 45.3 tokens per second decode speed with a time-to-first-token of 4275ms using Q4_K_M quantization.
For coding workloads, InternLM 7B on MacBook Pro M4 Pro 24GB receives a A grade with 45.3 tok/s and 8K context.
On MacBook Pro M4 Pro 24GB, 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-m4-pro-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
3.9 GB |
| Medium |
| B68 |
Q4_K_M | 4 | 4.3 GB | Medium | B68 |
Q5_K_M | 5 | 5.0 GB | High | B69 |
Q6_K | 6 | 5.7 GB | High | B69 |
Q8_0Best for your GPU | 8 | 7.5 GB | Very High | A71 |
F16 | 16 | 14.3 GB | Maximum | F0 |
| Devstral Small 2 24B Instruct | 24B | A | 17.8 tok/s |
| Qwen 3 14B | 14B | S | 23.4 tok/s |
| Qwen 3 8B | 8B | S | 42.6 tok/s |
Not always. MacBook Pro M4 Pro 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.