RX 7700 XT 12GBGünstige Wahl
12 GB VRAM (+4)
C
Adds memory headroom for longer context windows and future model growth.60.7 tok/s Dekodierung
Adds memory headroom for longer context windows and future model growth.
ca. $449 MSRP
Can OpenSafetyLab MD Judge v0 2 internlm2 7b run on RX 5700 XT 8GB?
YES — Tight Fit
C52Usable
Estimated from fit model
OpenSafetyLab MD Judge v0 2 internlm2 7b needs ~6.8 GB VRAM. RX 5700 XT 8GB has 8.0 GB. With Q4_K_M quantization, expect ~55 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.
Select quantization to explore
Q4_K_M (Medium quality) — 6.8 GB, 54.6 tok/s, Tight fit
6.8 GB required8.0 GB available
85% VRAM used
Fit status
Tight fit
Decode
54.6 tok/s
TTFT
3549 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 5700 XT 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: 54.6 tok/s decode · 3.5s TTFT (warm) · 136 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 | 54.6 tok/s | 1936 ms | 40K |
| Coding | C | Tight fit | 54.6 tok/s | 3549 ms | 40K |
| Agentic Coding | C | Runs with offload | 54.6 tok/s | 5162 ms | 40K |
| Reasoning | C | Tight fit | 54.6 tok/s | 4194 ms | 40K |
| RAG | C | Runs with offload | 54.6 tok/s | 6452 ms | 40K |
Quantization options
How OpenSafetyLab MD Judge v0 2 internlm2 7b (7B params) fits at each quantization level on RX 5700 XT 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 startUpgrade-Optionen
Hardware, die OpenSafetyLab MD Judge v0 2 internlm2 7b gut ausführt
RX 6700 XT 12GBBestes Preis-Leistungs-Verhältnis
12 GB VRAM (+4)
C
Adds memory headroom for longer context windows and future model growth.46.8 tok/s Dekodierung
Adds memory headroom for longer context windows and future model growth.
ca. $479 MSRP
RX 6750 XT 12GBAMD-Upgrade
12 GB VRAM (+4)
C
Adds memory headroom for longer context windows and future model growth.53.6 tok/s Dekodierung
Adds memory headroom for longer context windows and future model growth.
ca. $549 MSRP
Frequently asked questions
Can RX 5700 XT 8GB run OpenSafetyLab MD Judge v0 2 internlm2 7b?
Yes, RX 5700 XT 8GB can run OpenSafetyLab MD Judge v0 2 internlm2 7b with a C grade (Tight fit). Expected decode speed: 54.6 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 5700 XT 8GB?
On RX 5700 XT 8GB, OpenSafetyLab MD Judge v0 2 internlm2 7b achieves approximately 54.6 tokens per second decode speed with a time-to-first-token of 3549ms using Q4_K_M quantization.
Can RX 5700 XT 8GB run OpenSafetyLab MD Judge v0 2 internlm2 7b for coding?
For coding workloads, OpenSafetyLab MD Judge v0 2 internlm2 7b on RX 5700 XT 8GB receives a C grade with 54.6 tok/s and 40K context.
What context window can OpenSafetyLab MD Judge v0 2 internlm2 7b use on RX 5700 XT 8GB?
On RX 5700 XT 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.
Embed this result▼
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<iframe src="https://willitrunai.com/embed/hf-richarderkhov--opensafetylab---md-judge-v0-2-internlm2-7b-gguf-on-rx-5700-xt-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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