OpenSafetyLab MD Judge v0 2 internlm2 7b needs ~6.6 GB VRAM. GTX 1660 Ti 6GB has 6.0 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
0.6 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~0.4 GB host RAM)
Decode
22.0 tok/s
TTFT
8792 ms
Safe context
4K
Memory
6.6 GB / 6.0 GB
Offload
10%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly {ram} GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs with offload | 25.4 tok/s | 4165 ms | 4K |
| Coding | D | Very compromised | 22.0 tok/s | 8792 ms | 4K |
| Agentic Coding | F | Too heavy | 17.0 tok/s | 16547 ms | 4K |
| Reasoning | D | Very compromised | 22.0 tok/s | 10391 ms | 4K |
| RAG | F | Too heavy | 17.0 tok/s | 20683 ms | 4K |
How OpenSafetyLab MD Judge v0 2 internlm2 7b (7B params) fits at each quantization level on GTX 1660 Ti 6GB (6.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C54 |
Q3_K_SBest for your GPU | 3 | 3.4 GB | Low | C53 |
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
RTX 3050 8GBBudget pickRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 32%.
~$249 MSRP
RTX 5060 8GBBest valueRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 191%.
~$299 MSRP
RTX 5050 8GBNVIDIA upgradeRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 100%.
~$299 MSRP
Yes, GTX 1660 Ti 6GB can run OpenSafetyLab MD Judge v0 2 internlm2 7b with a D grade (Very compromised). Expected decode speed: 22.0 tok/s.
OpenSafetyLab MD Judge v0 2 internlm2 7b (7B parameters) requires approximately 6.6 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 GTX 1660 Ti 6GB, OpenSafetyLab MD Judge v0 2 internlm2 7b achieves approximately 22.0 tokens per second decode speed with a time-to-first-token of 8792ms using Q4_K_M quantization.
For coding workloads, OpenSafetyLab MD Judge v0 2 internlm2 7b on GTX 1660 Ti 6GB receives a D grade with 22.0 tok/s and 4K context.
On GTX 1660 Ti 6GB, OpenSafetyLab MD Judge v0 2 internlm2 7b can safely use up to 4K 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-gtx-1660-ti-6gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 4 |
3.9 GB |
| Medium |
| F0 |
Q4_K_M | 4 | 4.3 GB | Medium | F0 |
Q5_K_M | 5 | 5.0 GB | High | F0 |
Q6_K | 6 | 5.7 GB | High | F0 |
Q8_0 | 8 | 7.5 GB | Very High | F0 |
F16 | 16 | 14.3 GB | Maximum | F0 |
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.