MD Judge v0 2 internlm2 7b i1 needs ~7.9 GB VRAM. RTX 4060 Ti 16GB has 16.0 GB. With Q4_K_M quantization, expect ~49 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
49.2 tok/s
TTFT
3932 ms
Safe context
174K
Memory
7.9 GB / 16.0 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 | 49.2 tok/s | 2145 ms | 174K |
| Coding | C | Runs well | 49.2 tok/s | 3932 ms | 174K |
| Agentic Coding | C | Runs well | 49.2 tok/s | 5719 ms | 174K |
| Reasoning | C | Runs well | 49.2 tok/s | 4647 ms | 174K |
| RAG | C | Runs well | 49.2 tok/s | 7149 ms | 174K |
How MD Judge v0 2 internlm2 7b i1 (7B params) fits at each quantization level on RTX 4060 Ti 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C46 |
Q3_K_S | 3 | 3.4 GB | Low | C47 |
NVFP4 | 4 | 3.9 GB | Medium | C47 |
Q4_K_M | 4 | 4.3 GB | Medium | C48 |
Q5_K_M | 5 | 5.0 GB | High | C48 |
Q6_K | 6 | 5.7 GB | High | C49 |
Q8_0Best for your GPU | 8 | 7.5 GB | Very High | C51 |
F16 | 16 | 14.3 GB | Maximum | F0 |
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 startOpciones de mejora
Sube la velocidad estimada de decodificación alrededor de un 99%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$899 MSRP
RTX A4500 20GBOpción económicaSube la velocidad estimada de decodificación alrededor de un 99%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$2,000 MSRP
Yes, RTX 4060 Ti 16GB can run MD Judge v0 2 internlm2 7b i1 with a C grade (Runs well). Expected decode speed: 49.2 tok/s.
MD Judge v0 2 internlm2 7b i1 (7B parameters) requires approximately 7.9 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 RTX 4060 Ti 16GB, MD Judge v0 2 internlm2 7b i1 achieves approximately 49.2 tokens per second decode speed with a time-to-first-token of 3932ms using Q4_K_M quantization.
For coding workloads, MD Judge v0 2 internlm2 7b i1 on RTX 4060 Ti 16GB receives a C grade with 49.2 tok/s and 174K context.
On RTX 4060 Ti 16GB, MD Judge v0 2 internlm2 7b i1 can safely use up to 174K 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--md-judge-v0-2-internlm2-7b-i1-gguf-on-rtx-4060-ti-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: