internlm2 limarp chat 20b needs ~19.3 GB VRAM. MacBook Pro M3 Pro 36GB has 25.9 GB. With Q4_K_M quantization, expect ~9 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
9.0 tok/s
TTFT
21570 ms
Safe context
61K
Memory
19.3 GB / 25.9 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 | C | Runs well | 9.0 tok/s | 11765 ms | 61K |
| Coding | C | Runs well | 9.0 tok/s | 21570 ms | 61K |
| Agentic Coding | C | Tight fit | 9.0 tok/s | 31375 ms | 61K |
| Reasoning | C | Runs well | 9.0 tok/s | 25492 ms | 61K |
| RAG | C | Tight fit | 9.0 tok/s | 39218 ms | 61K |
How internlm2 limarp chat 20b (20B params) fits at each quantization level on MacBook Pro M3 Pro 36GB (25.9 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 7.8 GB | Low | C46 |
Q3_K_S | 3 | 9.8 GB | Low | C47 |
NVFP4 | 4 |
Copy-paste commands to run internlm2 limarp chat 20b on your machine.
Run
lms load hf-intervitens-archive--internlm2-limarp-chat-20b-gguf && lms server startUpgrade options
Raises estimated decode speed by about 296%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Raises estimated decode speed by about 119%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Raises estimated decode speed by about 149%.
Adds memory headroom for longer context windows and future model growth.
~$2,999 MSRP
Yes, MacBook Pro M3 Pro 36GB can run internlm2 limarp chat 20b with a C grade (Runs well). Expected decode speed: 9.0 tok/s.
internlm2 limarp chat 20b (20B parameters) requires approximately 19.3 GB of memory with Q4_K_M quantization.
The recommended quantization for internlm2 limarp chat 20b is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M3 Pro 36GB, internlm2 limarp chat 20b achieves approximately 9.0 tokens per second decode speed with a time-to-first-token of 21570ms using Q4_K_M quantization.
For coding workloads, internlm2 limarp chat 20b on MacBook Pro M3 Pro 36GB receives a C grade with 9.0 tok/s and 61K context.
On MacBook Pro M3 Pro 36GB, internlm2 limarp chat 20b can safely use up to 61K 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-intervitens-archive--internlm2-limarp-chat-20b-gguf-on-m3-pro-36gb" 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 |
| C48 |
Q4_K_M | 4 | 12.2 GB | Medium | C49 |
Q5_K_M | 5 | 14.4 GB | High | C50 |
Q6_KBest for your GPU | 6 | 16.4 GB | High | C49 |
Q8_0 | 8 | 21.4 GB | Very High | F0 |
F16 | 16 | 41.0 GB | Maximum | F0 |
Not always. MacBook Pro M3 Pro 36GB 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.