Can OpenSafetyLab MD Judge v0 2 internlm2 7b run on RX 7600 8GB?

YES — Tight Fit

C51Usable

Estimated from fit model

OpenSafetyLab MD Judge v0 2 internlm2 7b needs ~6.8 GB VRAM. RX 7600 8GB has 8.0 GB. With Q4_K_M quantization, expect ~39 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: LowStack: StandardBottleneck: 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) — 6.8 GB, 39.1 tok/s, Tight fit

6.8 GB required8.0 GB available

85% VRAM used

Fit status

Tight fit

Decode

39.1 tok/s

TTFT

4949 ms

Safe context

40K

Memory

6.8 GB / 8.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.8 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsOpenSafetyLab   MD Judge v0 2 internlm2 7b on RX 7600 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: 39.1 tok/s decode · 4.9s TTFT (warm) · 98 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	39.1 tok/s	2699 ms	40K
Coding	C	Tight fit	39.1 tok/s	4949 ms	40K
Agentic Coding	C	Runs with offload	39.1 tok/s	7198 ms	40K
Reasoning	C	Tight fit	39.1 tok/s	5849 ms	40K
RAG	C	Runs with offload	39.1 tok/s	8998 ms	40K

Quantization options

How OpenSafetyLab MD Judge v0 2 internlm2 7b (7B params) fits at each quantization level on RX 7600 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 start

Opciones de mejora

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

RX 7700 XT 12GBOpción económica

12 GB VRAM (+4)432 GB/s (+144)

Sube la velocidad estimada de decodificación alrededor de un 55%.60.7 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 55%.

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

~$449 MSRP

RX 6700 XT 12GBMejor relación calidad-precio

12 GB VRAM (+4)384 GB/s (+96)

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.46.8 tok/s decodificación

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

~$479 MSRP

RX 9070 16GBMejora AMD

16 GB VRAM (+8)640 GB/s (+352)

Sube la velocidad estimada de decodificación alrededor de un 138%.92.9 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 138%.

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

~$479 MSRP

Frequently asked questions

Can RX 7600 8GB run OpenSafetyLab MD Judge v0 2 internlm2 7b?

Yes, RX 7600 8GB can run OpenSafetyLab MD Judge v0 2 internlm2 7b with a C grade (Tight fit). Expected decode speed: 39.1 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 6.8 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 RX 7600 8GB?

On RX 7600 8GB, OpenSafetyLab MD Judge v0 2 internlm2 7b achieves approximately 39.1 tokens per second decode speed with a time-to-first-token of 4949ms using Q4_K_M quantization.

Can RX 7600 8GB run OpenSafetyLab MD Judge v0 2 internlm2 7b for coding?

For coding workloads, OpenSafetyLab MD Judge v0 2 internlm2 7b on RX 7600 8GB receives a C grade with 39.1 tok/s and 40K context.

What context window can OpenSafetyLab MD Judge v0 2 internlm2 7b use on RX 7600 8GB?

On RX 7600 8GB, OpenSafetyLab MD Judge v0 2 internlm2 7b can safely use up to 40K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RX 7600 8GB See all hardware for OpenSafetyLab MD Judge v0 2 internlm2 7b

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