internlm2 math plus 20b i1 needs ~20.6 GB VRAM. MacBook Pro M4 Pro 48GB has 34.6 GB. With Q4_K_M quantization, expect ~22 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
22.4 tok/s
TTFT
8643 ms
Safe context
111K
Memory
20.6 GB / 34.6 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 | 22.4 tok/s | 4714 ms | 111K |
| Coding | C | Runs well | 22.4 tok/s | 8643 ms | 111K |
| Agentic Coding | C | Runs well | 22.4 tok/s | 12572 ms | 111K |
| Reasoning | C | Runs well | 22.4 tok/s | 10215 ms | 111K |
| RAG | C | Runs well | 22.4 tok/s | 15715 ms | 111K |
How internlm2 math plus 20b i1 (20B params) fits at each quantization level on MacBook Pro M4 Pro 48GB (34.6 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 7.8 GB | Low | C44 |
Q3_K_S | 3 | 9.8 GB | Low | C45 |
NVFP4 | 4 |
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
RTX PRO 5000 Blackwell 48GBBudget pickRaises estimated decode speed by about 313%.
Adds memory headroom for longer context windows and future model growth.
~$4,999 MSRP
NVIDIA A100 40GBBest valueRaises estimated decode speed by about 378%.
~$10,000 MSRP
Yes, MacBook Pro M4 Pro 48GB can run internlm2 math plus 20b i1 with a C grade (Runs well). Expected decode speed: 22.4 tok/s.
internlm2 math plus 20b i1 (20B parameters) requires approximately 20.6 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 M4 Pro 48GB, internlm2 math plus 20b i1 achieves approximately 22.4 tokens per second decode speed with a time-to-first-token of 8643ms using Q4_K_M quantization.
For coding workloads, internlm2 math plus 20b i1 on MacBook Pro M4 Pro 48GB receives a C grade with 22.4 tok/s and 111K context.
On MacBook Pro M4 Pro 48GB, internlm2 math plus 20b i1 can safely use up to 111K 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-mradermacher--internlm2-math-plus-20b-i1-gguf-on-m4-pro-48gb" 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 |
| C45 |
Q4_K_M | 4 | 12.2 GB | Medium | C46 |
Q5_K_M | 5 | 14.4 GB | High | C47 |
Q6_K | 6 | 16.4 GB | High | C48 |
Q8_0Best for your GPU | 8 | 21.4 GB | Very High | C48 |
F16 | 16 | 41.0 GB | Maximum | F0 |
Not always. MacBook Pro M4 Pro 48GB 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.