RTX 3060 12GBOpção econômica12 GB VRAM (+4)
C
Adds memory headroom for longer context windows and future model growth.55.6 tok/s decodificação
Adds memory headroom for longer context windows and future model growth.
~$329 MSRP
Can OpenSafetyLab MD Judge v0 2 internlm2 7b run on RTX 5060 Ti 8GB?
YES — Tight Fit
C52Usable
Estimated from fit model
OpenSafetyLab MD Judge v0 2 internlm2 7b needs ~7.1 GB VRAM. RTX 5060 Ti 8GB has 8.0 GB. With Q4_K_M quantization, expect ~65 tok/s.
Runtime: OllamaCapacity: TightBandwidth: LowStack: BasicBottleneck: Balanced
Operating mode
Choose the run profile you care about
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
Q4_K_M (Medium quality) — 7.1 GB, 65.0 tok/s, Tight fit
7.1 GB required8.0 GB available
89% VRAM used
Fit status
Tight fit
Decode
65.0 tok/s
TTFT
2976 ms
Safe context
34K
Memory
7.1 GB / 8.0 GB
Memory breakdown
Weights4.3 GB
KV Cache0.8 GB
Runtime1.2 GB
Headroom0.8 GB
See how fast it feels
See how fast it feelsOpenSafetyLab MD Judge v0 2 internlm2 7b on RTX 5060 Ti 8GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 65.0 tok/s decode · 3.0s TTFT (warm) · 163 tok/s prefill
What limits this setup
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.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 65.0 tok/s | 1623 ms | 34K |
| Coding | C | Tight fit | 65.0 tok/s | 2976 ms | 34K |
| Agentic Coding | C | Runs with offload | 65.0 tok/s | 4329 ms | 34K |
| Reasoning | C | Tight fit | 65.0 tok/s | 3517 ms | 34K |
| RAG | C | Runs with offload | 65.0 tok/s | 5411 ms | 34K |
Quantization options
How OpenSafetyLab MD Judge v0 2 internlm2 7b (7B params) fits at each quantization level on RTX 5060 Ti 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C53 |
Q3_K_S | 3 | 3.4 GB | Low | C53 |
NVFP4 | 4 | 3.9 GB | Medium | C53 |
Q4_K_M | 4 | 4.3 GB | Medium | C53 |
Q5_K_MBest for your GPU | 5 | 5.0 GB | High | C52 |
Q6_K | 6 | 5.7 GB | High | F0 |
Q8_0 | 8 | 7.5 GB | Very High | F0 |
F16 | 16 | 14.3 GB | Maximum | F0 |
Get started
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 startOpções de upgrade
Hardware que roda bem OpenSafetyLab MD Judge v0 2 internlm2 7b
RTX 3060 12GBOpção econômicaRTX 5070 12GBMelhor custo-benefício12 GB VRAM (+4)672 GB/s (+224)
C
Raises estimated decode speed by about 51%.98 tok/s decodificação
Raises estimated decode speed by about 51%.
Adds memory headroom for longer context windows and future model growth.
~$549 MSRP
RTX 4070 Super 12GBUpgrade NVIDIA12 GB VRAM (+4)504 GB/s (+56)
C
Raises estimated decode speed by about 40%.90.9 tok/s decodificação
Raises estimated decode speed by about 40%.
Adds memory headroom for longer context windows and future model growth.
~$599 MSRP
Frequently asked questions
Can RTX 5060 Ti 8GB run OpenSafetyLab MD Judge v0 2 internlm2 7b?
Yes, RTX 5060 Ti 8GB can run OpenSafetyLab MD Judge v0 2 internlm2 7b with a C grade (Tight fit). Expected decode speed: 65.0 tok/s.
How much VRAM does OpenSafetyLab MD Judge v0 2 internlm2 7b need?
OpenSafetyLab MD Judge v0 2 internlm2 7b (7B parameters) requires approximately 7.1 GB of memory with Q4_K_M quantization.
What is the best quantization for OpenSafetyLab MD Judge v0 2 internlm2 7b?
The recommended quantization for OpenSafetyLab MD Judge v0 2 internlm2 7b is Q4_K_M, which balances quality and memory efficiency.
What speed will OpenSafetyLab MD Judge v0 2 internlm2 7b run at on RTX 5060 Ti 8GB?
On RTX 5060 Ti 8GB, OpenSafetyLab MD Judge v0 2 internlm2 7b achieves approximately 65.0 tokens per second decode speed with a time-to-first-token of 2976ms using Q4_K_M quantization.
Can RTX 5060 Ti 8GB run OpenSafetyLab MD Judge v0 2 internlm2 7b for coding?
For coding workloads, OpenSafetyLab MD Judge v0 2 internlm2 7b on RTX 5060 Ti 8GB receives a C grade with 65.0 tok/s and 34K context.
What context window can OpenSafetyLab MD Judge v0 2 internlm2 7b use on RTX 5060 Ti 8GB?
On RTX 5060 Ti 8GB, OpenSafetyLab MD Judge v0 2 internlm2 7b can safely use up to 34K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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<iframe src="https://willitrunai.com/embed/hf-richarderkhov--opensafetylab---md-judge-v0-2-internlm2-7b-gguf-on-rtx-5060-ti-8gb" 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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