internlm2 limarp chat 20b needs ~25.8 GB VRAM. MacBook Pro M2 Max 96GB has 69.1 GB. With Q4_K_M quantization, expect ~19 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
19.0 tok/s
TTFT
10181 ms
Safe context
312K
Memory
25.8 GB / 69.1 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 | 19.0 tok/s | 5553 ms | 312K |
| Coding | C | Runs well | 19.0 tok/s | 10181 ms | 312K |
| Agentic Coding | C | Runs well | 19.0 tok/s | 14808 ms | 312K |
| Reasoning | C | Runs well | 19.0 tok/s | 12032 ms | 312K |
| RAG | C | Runs well | 19.0 tok/s | 18510 ms | 312K |
How internlm2 limarp chat 20b (20B params) fits at each quantization level on MacBook Pro M2 Max 96GB (69.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 7.8 GB | Low | C40 |
Q3_K_S | 3 | 9.8 GB | Low | C40 |
NVFP4 | 4 | 11.2 GB | Medium | C41 |
Q4_K_M | 4 | 12.2 GB | Medium | C41 |
Q5_K_M | 5 | 14.4 GB | High | C41 |
Q6_K | 6 | 16.4 GB | High | C42 |
Q8_0 | 8 | 21.4 GB | Very High | C43 |
F16Best for your GPU | 16 | 41.0 GB | Maximum | C47 |
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 startアップグレードオプション
Raises estimated decode speed by about 100%.
Adds memory headroom for longer context windows and future model growth.
〜$3,999 MSRP
Raises estimated decode speed by about 90%.
Adds memory headroom for longer context windows and future model growth.
〜$3,999 MSRP
Raises estimated decode speed by about 87%.
Adds memory headroom for longer context windows and future model growth.
〜$4,999 MSRP
Yes, MacBook Pro M2 Max 96GB can run internlm2 limarp chat 20b with a C grade (Runs well). Expected decode speed: 19.0 tok/s.
internlm2 limarp chat 20b (20B parameters) requires approximately 25.8 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 M2 Max 96GB, internlm2 limarp chat 20b achieves approximately 19.0 tokens per second decode speed with a time-to-first-token of 10181ms using Q4_K_M quantization.
For coding workloads, internlm2 limarp chat 20b on MacBook Pro M2 Max 96GB receives a C grade with 19.0 tok/s and 312K context.
On MacBook Pro M2 Max 96GB, internlm2 limarp chat 20b can safely use up to 312K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Not always. MacBook Pro M2 Max 96GB 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.
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-m2-max-96gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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