MacBook Pro M4 Max 36GBBudget pick
36 GB Unified (+12)
C
This setup is broadly balanced for this model.65.9 tok/s decode
~$2,499 MSRP
OpenSafetyLab MD Judge v0 2 internlm2 7b needs ~8.7 GB VRAM. RTX 4500 Ada 24GB has 24.0 GB. With Q4_K_M quantization, expect ~80 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
79.9 tok/s
TTFT
2422 ms
Safe context
315K
Memory
8.7 GB / 24.0 GB
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 79.9 tok/s | 1321 ms | 315K |
| Coding | C | Runs well | 79.9 tok/s | 2422 ms | 315K |
| Agentic Coding | C | Runs well | 79.9 tok/s | 3523 ms | 315K |
| Reasoning | C | Runs well | 79.9 tok/s | 2863 ms | 315K |
| RAG | C | Runs well | 79.9 tok/s | 4404 ms | 315K |
How OpenSafetyLab MD Judge v0 2 internlm2 7b (7B params) fits at each quantization level on RTX 4500 Ada 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C44 |
Q3_K_S | 3 | 3.4 GB | Low | C44 |
NVFP4 | 4 | 3.9 GB | Medium | C44 |
Q4_K_M | 4 | 4.3 GB | Medium | C45 |
Q5_K_M | 5 | 5.0 GB | High | C45 |
Q6_K | 6 | 5.7 GB | High | C45 |
Q8_0 | 8 | 7.5 GB | Very High | C46 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | C50 |
Copy-paste commands to run OpenSafetyLab MD Judge v0 2 internlm2 7b on your machine.
Run
lms load hf-richarderkhov--opensafetylab---md-judge-v0-2-internlm2-7b-gguf && lms server startUpgrade options
Yes, RTX 4500 Ada 24GB can run OpenSafetyLab MD Judge v0 2 internlm2 7b with a C grade (Runs well). Expected decode speed: 79.9 tok/s.
OpenSafetyLab MD Judge v0 2 internlm2 7b (7B parameters) requires approximately 8.7 GB of memory with Q4_K_M quantization.
The recommended quantization for OpenSafetyLab MD Judge v0 2 internlm2 7b is Q4_K_M, which balances quality and memory efficiency.
On RTX 4500 Ada 24GB, OpenSafetyLab MD Judge v0 2 internlm2 7b achieves approximately 79.9 tokens per second decode speed with a time-to-first-token of 2422ms using Q4_K_M quantization.
For coding workloads, OpenSafetyLab MD Judge v0 2 internlm2 7b on RTX 4500 Ada 24GB receives a C grade with 79.9 tok/s and 315K context.
On RTX 4500 Ada 24GB, OpenSafetyLab MD Judge v0 2 internlm2 7b can safely use up to 315K 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-richarderkhov--opensafetylab---md-judge-v0-2-internlm2-7b-gguf-on-rtx-4500-ada-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: