InternLM 20B needs ~36.2 GB but MacBook Pro M4 Pro 24GB only has 17.3 GB. Try a smaller quantization or lighter model.
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
18.9 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
8.7 tok/s
TTFT
22226 ms
Safe context
4K
Memory
36.2 GB / 17.3 GB
Offload
50%
Usable shared or unified memory is the main blocker for this model.
Not enough usable memory
The model needs 36.2 GB, but this setup only exposes 17.3 GB of usable shared or unified memory.
Move to a larger memory pool
A larger unified-memory SKU or a discrete high-bandwidth GPU is the cleanest way to make this model practical.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 10.8 tok/s | 9789 ms | 4K |
| Coding | F | Too heavy | 6.7 tok/s | 28894 ms | 4K |
| Agentic Coding | F | Too heavy | 8.7 tok/s | 32329 ms | 4K |
| Reasoning | F | Too heavy | 8.7 tok/s | 26267 ms | 4K |
| RAG | F | Too heavy | 8.7 tok/s | 40411 ms | 4K |
How InternLM 20B (20B params) fits at each quantization level on MacBook Pro M4 Pro 24GB (17.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 7.8 GB | Low | B59 |
Q3_K_S | 3 | 9.8 GB | Low | B59 |
NVFP4 | 4 |
Upgrade options
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$1,099 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$1,599 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$2,499 MSRP
No, InternLM 20B requires more memory than MacBook Pro M4 Pro 24GB provides.
InternLM 20B (20B parameters) requires approximately 36.2 GB of memory with Q5_K_M quantization.
The recommended quantization for InternLM 20B is Q5_K_M, which balances quality and memory efficiency.
On MacBook Pro M4 Pro 24GB, InternLM 20B achieves approximately 6.7 tokens per second decode speed with a time-to-first-token of 28894ms using Q5_K_M quantization.
For coding workloads, InternLM 20B on MacBook Pro M4 Pro 24GB receives a F grade with 6.7 tok/s and 4K context.
On MacBook Pro M4 Pro 24GB, InternLM 20B can safely use up to 4K 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-20b-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:
11.2 GB |
| Medium |
| B59 |
Q4_K_MBest for your GPU | 4 | 12.2 GB | Medium | B58 |
Q5_K_M | 5 | 14.4 GB | High | F0 |
Q6_K | 6 | 16.4 GB | High | F0 |
Q8_0 | 8 | 21.4 GB | Very High | F0 |
F16 | 16 | 41.0 GB | Maximum | F0 |
Move to a larger memory pool. A larger unified-memory SKU or a discrete high-bandwidth GPU is the cleanest way to make this model practical.
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.
