Can OpenSafetyLab MD Judge v0 2 internlm2 7b run on NVIDIA B200 180GB?

YES — Runs Great

C44Usable

Estimated from fit model

OpenSafetyLab MD Judge v0 2 internlm2 7b needs ~24.3 GB VRAM. NVIDIA B200 180GB has 180.0 GB. With Q4_K_M quantization, expect ~98 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: 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) — 24.3 GB, 98.0 tok/s, Runs well

24.3 GB required180.0 GB available

14% VRAM used

Fit status

Runs well

Decode

98.0 tok/s

TTFT

1976 ms

Safe context

3.1M

Memory

24.3 GB / 180.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.8 GB

Runtime1.2 GB

Headroom18.0 GB

See how fast it feels

See how fast it feelsOpenSafetyLab   MD Judge v0 2 internlm2 7b on NVIDIA B200 180GB
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: 98.0 tok/s decode · 2.0s TTFT (warm) · 245 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	Runs well	98.0 tok/s	1078 ms	3.1M
Coding	C	Runs well	98.0 tok/s	1976 ms	3.1M
Agentic Coding	C	Runs well	98.0 tok/s	2873 ms	3.1M
Reasoning	C	Runs well	98.0 tok/s	2335 ms	3.1M
RAG	C	Runs well	98.0 tok/s	3592 ms	3.1M

Inference speed

OpenSafetyLab MD Judge v0 2 internlm2 7b inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for OpenSafetyLab MD Judge v0 2 internlm2 7b at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~98 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	98.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	98.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	98.0

Quantization options

How OpenSafetyLab MD Judge v0 2 internlm2 7b (7B params) fits at each quantization level on NVIDIA B200 180GB (180.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	D37
Q3_K_S	3	3.4 GB	Low	D37
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

Mac Studio M3 Ultra 256GBBudget pick

256 GB Unified (+76)

This setup is broadly balanced for this model.98 tok/s decode

~$6,999 MSRP

Frequently asked questions

Can NVIDIA B200 180GB run OpenSafetyLab MD Judge v0 2 internlm2 7b?

Yes, NVIDIA B200 180GB can run OpenSafetyLab MD Judge v0 2 internlm2 7b with a C grade (Runs well). Expected decode speed: 98.0 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 24.3 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 NVIDIA B200 180GB?

On NVIDIA B200 180GB, OpenSafetyLab MD Judge v0 2 internlm2 7b achieves approximately 98.0 tokens per second decode speed with a time-to-first-token of 1976ms using Q4_K_M quantization.

Can NVIDIA B200 180GB run OpenSafetyLab MD Judge v0 2 internlm2 7b for coding?

For coding workloads, OpenSafetyLab MD Judge v0 2 internlm2 7b on NVIDIA B200 180GB receives a C grade with 98.0 tok/s and 3.1M context.

What context window can OpenSafetyLab MD Judge v0 2 internlm2 7b use on NVIDIA B200 180GB?

On NVIDIA B200 180GB, OpenSafetyLab MD Judge v0 2 internlm2 7b can safely use up to 3.1M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA B200 180GB See all hardware for OpenSafetyLab MD Judge v0 2 internlm2 7b

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