Can OpenSafetyLab MD Judge v0 2 internlm2 7b run on RTX 2060 Super 8GB?

YES — Tight Fit

C52Usable

Estimated from fit model

OpenSafetyLab MD Judge v0 2 internlm2 7b needs ~7.1 GB VRAM. RTX 2060 Super 8GB has 8.0 GB. With Q4_K_M quantization, expect ~61 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 7.1 GB, 60.9 tok/s, Tight fit

7.1 GB required8.0 GB available

89% VRAM used

Fit status

Tight fit

Decode

60.9 tok/s

TTFT

3181 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 2060 Super 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: 60.9 tok/s decode · 3.2s TTFT (warm) · 152 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Tight fit	60.9 tok/s	1735 ms	34K
Coding	C	Tight fit	60.9 tok/s	3181 ms	34K
Agentic Coding	C	Runs with offload	60.9 tok/s	4628 ms	34K
Reasoning	C	Tight fit	60.9 tok/s	3760 ms	34K
RAG	C	Runs with offload	60.9 tok/s	5784 ms	34K

Quantization options

How OpenSafetyLab MD Judge v0 2 internlm2 7b (7B params) fits at each quantization level on RTX 2060 Super 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

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 start

Upgrade options

Hardware that runs OpenSafetyLab MD Judge v0 2 internlm2 7b well

RTX 3060 12GBBudget pick

12 GB VRAM (+4)

Adds memory headroom for longer context windows and future model growth.55.6 tok/s decode

Adds memory headroom for longer context windows and future model growth.

~$329 MSRP

RTX 5070 12GBBest value

12 GB VRAM (+4)672 GB/s (+224)

Raises estimated decode speed by about 61%.98 tok/s decode

Raises estimated decode speed by about 61%.

Adds memory headroom for longer context windows and future model growth.

~$549 MSRP

RTX 4070 Super 12GBNVIDIA upgrade

12 GB VRAM (+4)504 GB/s (+56)

Raises estimated decode speed by about 49%.90.9 tok/s decode

Raises estimated decode speed by about 49%.

Adds memory headroom for longer context windows and future model growth.

~$599 MSRP

Frequently asked questions

Can RTX 2060 Super 8GB run OpenSafetyLab MD Judge v0 2 internlm2 7b?

Yes, RTX 2060 Super 8GB can run OpenSafetyLab MD Judge v0 2 internlm2 7b with a C grade (Tight fit). Expected decode speed: 60.9 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 2060 Super 8GB?

On RTX 2060 Super 8GB, OpenSafetyLab MD Judge v0 2 internlm2 7b achieves approximately 60.9 tokens per second decode speed with a time-to-first-token of 3181ms using Q4_K_M quantization.

Can RTX 2060 Super 8GB run OpenSafetyLab MD Judge v0 2 internlm2 7b for coding?

For coding workloads, OpenSafetyLab MD Judge v0 2 internlm2 7b on RTX 2060 Super 8GB receives a C grade with 60.9 tok/s and 34K context.

What context window can OpenSafetyLab MD Judge v0 2 internlm2 7b use on RTX 2060 Super 8GB?

On RTX 2060 Super 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.

See all results for RTX 2060 Super 8GB See all hardware for OpenSafetyLab MD Judge v0 2 internlm2 7b

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