MacBook Pro M3 Pro 36GBBudget pick
36 GB Unified (+12)
C
Adds memory headroom for longer context windows and future model growth.9 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
internlm2 5 20b chat needs ~18.0 GB VRAM. MacBook Pro M4 Pro 24GB has 17.3 GB. With Q4_K_M quantization, expect ~16 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
0.7 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~0.5 GB host RAM)
Decode
20.6 tok/s
TTFT
9385 ms
Safe context
11K
Memory
18.0 GB / 17.3 GB
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
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.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs with offload | 22.4 tok/s | 4714 ms | 11K |
| Coding | C | Runs with offload | 15.9 tok/s | 12200 ms | 11K |
| Agentic Coding | D | Very compromised (needs ~1.9 GB host RAM) | 17.4 tok/s | 16152 ms | 11K |
| Reasoning | C | Runs with offload (needs ~0.5 GB host RAM) | 20.6 tok/s | 11091 ms | 11K |
| RAG | D | Very compromised (needs ~1.9 GB host RAM) | 17.4 tok/s | 20190 ms |
How internlm2 5 20b chat (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 | C51 |
Q3_K_S | 3 | 9.8 GB | Low | C51 |
NVFP4 | 4 |
Copy-paste commands to run internlm2 5 20b chat on your machine.
Run
lms load hf-bartowski--internlm2-5-20b-chat-gguf && lms server startUpgrade options
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Raises estimated decode speed by about 37%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Raises estimated decode speed by about 73%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Yes, MacBook Pro M4 Pro 24GB can run internlm2 5 20b chat with a C grade (Runs with offload). Expected decode speed: 15.9 tok/s.
internlm2 5 20b chat (20B parameters) requires approximately 18.0 GB of memory with Q4_K_M quantization.
The recommended quantization for internlm2 5 20b chat is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M4 Pro 24GB, internlm2 5 20b chat achieves approximately 15.9 tokens per second decode speed with a time-to-first-token of 12200ms using Q4_K_M quantization.
For coding workloads, internlm2 5 20b chat on MacBook Pro M4 Pro 24GB receives a C grade with 15.9 tok/s and 11K context.
On MacBook Pro M4 Pro 24GB, internlm2 5 20b chat can safely use up to 11K tokens of context. The model's official context limit is —, 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/hf-bartowski--internlm2-5-20b-chat-gguf-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:
| 11K |
11.2 GB |
| Medium |
| C50 |
Q4_K_MBest for your GPU | 4 | 12.2 GB | Medium | C50 |
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 |
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
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.