MD Judge v0 2 internlm2 7b i1 needs ~12.9 GB VRAM. MacBook Pro M1 Max 64GB has 46.1 GB. With Q4_K_M quantization, expect ~52 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
51.5 tok/s
TTFT
3758 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 | 51.5 tok/s | 2050 ms | 663K |
| Coding | C | Runs well | 51.5 tok/s | 3758 ms | 663K |
| Agentic Coding | C | Runs well | 51.5 tok/s | 5466 ms | 663K |
| Reasoning | C | Runs well | 51.5 tok/s | 4441 ms | 663K |
| RAG | C | Runs well | 51.5 tok/s | 6832 ms | 663K |
How MD Judge v0 2 internlm2 7b i1 (7B params) fits at each quantization level on MacBook Pro M1 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 | 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 | C41 |
Q8_0 | 8 | 7.5 GB | Very High | C42 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | C44 |
Copy-paste commands to run MD Judge v0 2 internlm2 7b i1 on your machine.
Run
lms load hf-mradermacher--md-judge-v0-2-internlm2-7b-i1-gguf && lms server startOpções de upgrade
Raises estimated decode speed by about 70%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 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 90%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Yes, MacBook Pro M1 Max 64GB can run MD Judge v0 2 internlm2 7b i1 with a C grade (Runs well). Expected decode speed: 51.5 tok/s.
MD Judge v0 2 internlm2 7b i1 (7B parameters) requires approximately 12.9 GB of memory with Q4_K_M quantization.
The recommended quantization for MD Judge v0 2 internlm2 7b i1 is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M1 Max 64GB, MD Judge v0 2 internlm2 7b i1 achieves approximately 51.5 tokens per second decode speed with a time-to-first-token of 3758ms using Q4_K_M quantization.
For coding workloads, MD Judge v0 2 internlm2 7b i1 on MacBook Pro M1 Max 64GB receives a C grade with 51.5 tok/s and 663K context.
On MacBook Pro M1 Max 64GB, MD Judge v0 2 internlm2 7b i1 can safely use up to 663K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Not always. MacBook Pro M1 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.
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<iframe src="https://willitrunai.com/embed/hf-mradermacher--md-judge-v0-2-internlm2-7b-i1-gguf-on-m1-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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