internlm2 math plus 7b IMat needs ~12.9 GB VRAM. MacBook Pro M3 Max 64GB has 46.1 GB. With Q4_K_M quantization, expect ~56 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
56.2 tok/s
TTFT
3444 ms
Safe context
663K
Memory
12.9 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 | 56.2 tok/s | 1879 ms | 663K |
| Coding | C | Runs well | 56.2 tok/s | 3444 ms | 663K |
| Agentic Coding | C | Runs well | 56.2 tok/s | 5010 ms | 663K |
| Reasoning | C | Runs well | 56.2 tok/s | 4071 ms | 663K |
| RAG | C | Runs well | 56.2 tok/s | 6263 ms | 663K |
How internlm2 math plus 7b IMat (7B params) fits at each quantization level on MacBook Pro M3 Max 64GB (46.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C41 |
Q3_K_S | 3 | 3.4 GB | Low | C41 |
NVFP4 | 4 |
Copy-paste commands to run internlm2 math plus 7b IMat on your machine.
Run
lms load hf-legraphista--internlm2-math-plus-7b-imat-gguf && lms server startUpgrade options
Raises estimated decode speed by about 56%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Raises estimated decode speed by about 74%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Raises estimated decode speed by about 74%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Yes, MacBook Pro M3 Max 64GB can run internlm2 math plus 7b IMat with a C grade (Runs well). Expected decode speed: 56.2 tok/s.
internlm2 math plus 7b IMat (7B parameters) requires approximately 12.9 GB of memory with Q4_K_M quantization.
The recommended quantization for internlm2 math plus 7b IMat is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M3 Max 64GB, internlm2 math plus 7b IMat achieves approximately 56.2 tokens per second decode speed with a time-to-first-token of 3444ms using Q4_K_M quantization.
For coding workloads, internlm2 math plus 7b IMat on MacBook Pro M3 Max 64GB receives a C grade with 56.2 tok/s and 663K context.
On MacBook Pro M3 Max 64GB, internlm2 math plus 7b IMat can safely use up to 663K 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-legraphista--internlm2-math-plus-7b-imat-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>
Preview:
3.9 GB |
| Medium |
| C41 |
Q4_K_M | 4 | 4.3 GB | Medium | C41 |
Q5_K_M | 5 | 5.0 GB | High | C41 |
Q6_K | 6 | 5.7 GB | High | C42 |
Q8_0 | 8 | 7.5 GB | Very High | C42 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | C44 |
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.