internlm2 math plus 20b i1 needs ~22.4 GB VRAM. MacBook Pro M3 Max 64GB has 46.1 GB. With Q4_K_M quantization, expect ~20 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
19.7 tok/s
TTFT
9841 ms
Safe context
178K
Memory
22.4 GB / 46.1 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 | 19.7 tok/s | 5368 ms | 178K |
| Coding | C | Runs well | 19.7 tok/s | 9841 ms | 178K |
| Agentic Coding | C | Runs well | 19.7 tok/s | 14315 ms | 178K |
| Reasoning | C | Runs well | 19.7 tok/s | 11631 ms | 178K |
| RAG | C | Runs well | 19.7 tok/s | 17893 ms | 178K |
How internlm2 math plus 20b i1 (20B params) fits at each quantization level on MacBook Pro M3 Max 64GB (46.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 7.8 GB | Low | C42 |
Q3_K_S | 3 | 9.8 GB | Low | C43 |
NVFP4 | 4 | 11.2 GB | Medium | C43 |
Q4_K_M | 4 | 12.2 GB | Medium | C43 |
Q5_K_M | 5 | 14.4 GB | High | C44 |
Q6_K | 6 | 16.4 GB | High | C45 |
Q8_0Best for your GPU | 8 | 21.4 GB | Very High | C46 |
F16 | 16 | 41.0 GB | Maximum | F0 |
Copy-paste commands to run internlm2 math plus 20b i1 on your machine.
Run
lms load hf-mradermacher--internlm2-math-plus-20b-i1-gguf && lms server startUpgrade options
Raises estimated decode speed by about 131%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Raises estimated decode speed by about 112%.
~$3,999 MSRP
Yes, MacBook Pro M3 Max 64GB can run internlm2 math plus 20b i1 with a C grade (Runs well). Expected decode speed: 19.7 tok/s.
internlm2 math plus 20b i1 (20B parameters) requires approximately 22.4 GB of memory with Q4_K_M quantization.
The recommended quantization for internlm2 math plus 20b i1 is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M3 Max 64GB, internlm2 math plus 20b i1 achieves approximately 19.7 tokens per second decode speed with a time-to-first-token of 9841ms using Q4_K_M quantization.
For coding workloads, internlm2 math plus 20b i1 on MacBook Pro M3 Max 64GB receives a C grade with 19.7 tok/s and 178K context.
On MacBook Pro M3 Max 64GB, internlm2 math plus 20b i1 can safely use up to 178K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Not always. MacBook Pro M3 Max 64GB 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-mradermacher--internlm2-math-plus-20b-i1-gguf-on-m3-max-64gb" 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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