Will It Run AI

Can OpenSafetyLab MD Judge v0 2 internlm2 7b run on NVIDIA T4 16GB?

YES — Runs Great

C50Usable
Estimated from fit model

OpenSafetyLab MD Judge v0 2 internlm2 7b needs ~7.9 GB VRAM. NVIDIA T4 16GB has 16.0 GB. With Q4_K_M quantization, expect ~49 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: Balanced
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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) — 7.9 GB, 48.7 tok/s, Runs well
7.9 GB required16.0 GB available
49% VRAM used

Fit status

Runs well

Decode

48.7 tok/s

TTFT

3974 ms

Safe context

174K

Memory

7.9 GB / 16.0 GB

Memory breakdown

Weights4.3 GB
KV Cache0.8 GB
Runtime1.2 GB
Headroom1.6 GB

See how fast it feels

See how fast it feelsOpenSafetyLab MD Judge v0 2 internlm2 7b on NVIDIA T4 16GB
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: 48.7 tok/s decode · 4.0s TTFT (warm) · 122 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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well48.7 tok/s2168 ms174K
CodingCRuns well48.7 tok/s3974 ms174K
Agentic CodingCRuns well48.7 tok/s5781 ms174K
ReasoningCRuns well48.7 tok/s4697 ms174K
RAGCRuns well48.7 tok/s7226 ms174K

Quantization options

How OpenSafetyLab MD Judge v0 2 internlm2 7b (7B params) fits at each quantization level on NVIDIA T4 16GB (16.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
2.7 GB
LowC46
Q3_K_S
3
3.4 GB
LowC47
NVFP4
4
3.9 GB
MediumC47
Q4_K_M
4
4.3 GB
MediumC48
Q5_K_M
5
5.0 GB
HighC48
Q6_K
6
5.7 GB
HighC49
Q8_0Best for your GPU
8
7.5 GB
Very HighC51
F16
16
14.3 GB
MaximumF0

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

Opciones de mejora

Hardware que ejecuta bien OpenSafetyLab MD Judge v0 2 internlm2 7b

RX 7900 XT 20GBMejor relación calidad-precio
20 GB VRAM (+4)800 GB/s (+480)
C
Sube la velocidad estimada de decodificación alrededor de un 101%.98 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 101%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$899 MSRP

NVIDIARTX A4500 20GBOpción económica
20 GB VRAM (+4)640 GB/s (+320)
C
Sube la velocidad estimada de decodificación alrededor de un 101%.98 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 101%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$2,000 MSRP

Frequently asked questions

Can NVIDIA T4 16GB run OpenSafetyLab MD Judge v0 2 internlm2 7b?

Yes, NVIDIA T4 16GB can run OpenSafetyLab MD Judge v0 2 internlm2 7b with a C grade (Runs well). Expected decode speed: 48.7 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 7.9 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 T4 16GB?

On NVIDIA T4 16GB, OpenSafetyLab MD Judge v0 2 internlm2 7b achieves approximately 48.7 tokens per second decode speed with a time-to-first-token of 3974ms using Q4_K_M quantization.

Can NVIDIA T4 16GB run OpenSafetyLab MD Judge v0 2 internlm2 7b for coding?

For coding workloads, OpenSafetyLab MD Judge v0 2 internlm2 7b on NVIDIA T4 16GB receives a C grade with 48.7 tok/s and 174K context.

What context window can OpenSafetyLab MD Judge v0 2 internlm2 7b use on NVIDIA T4 16GB?

On NVIDIA T4 16GB, OpenSafetyLab MD Judge v0 2 internlm2 7b can safely use up to 174K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA T4 16GBSee all hardware for OpenSafetyLab MD Judge v0 2 internlm2 7b
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