Will It Run AI

Can MD Judge v0 2 internlm2 7b i1 run on RX 6650 XT 8GB?

YES — Tight Fit

C50Usable
Estimated from fit model

MD Judge v0 2 internlm2 7b i1 needs ~6.8 GB VRAM. RX 6650 XT 8GB has 8.0 GB. With Q4_K_M quantization, expect ~33 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: LowStack: StandardBottleneck: 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) — 6.8 GB, 33.4 tok/s, Tight fit
6.8 GB required8.0 GB available
85% VRAM used

Fit status

Tight fit

Decode

33.4 tok/s

TTFT

5789 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 feelsMD Judge v0 2 internlm2 7b i1 on RX 6650 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: 33.4 tok/s decode · 5.8s TTFT (warm) · 84 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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well33.4 tok/s3158 ms40K
CodingCTight fit33.4 tok/s5789 ms40K
Agentic CodingCRuns with offload33.4 tok/s8420 ms40K
ReasoningCTight fit33.4 tok/s6842 ms40K
RAGCRuns with offload33.4 tok/s10525 ms40K

Quantization options

How MD Judge v0 2 internlm2 7b i1 (7B params) fits at each quantization level on RX 6650 XT 8GB (8.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
2.7 GB
LowC53
Q3_K_S
3
3.4 GB
LowC53
NVFP4
4
3.9 GB
MediumC53
Q4_K_M
4
4.3 GB
MediumC53
Q5_K_MBest for your GPU
5
5.0 GB
HighC52
Q6_K
6
5.7 GB
HighF0
Q8_0
8
7.5 GB
Very HighF0
F16
16
14.3 GB
MaximumF0

Get started

Copy-paste commands to run MD Judge v0 2 internlm2 7b i1 on your machine.

Run

lms load hf-mradermacher--md-judge-v0-2-internlm2-7b-i1-gguf && lms server start

Opciones de mejora

Hardware que ejecuta bien MD Judge v0 2 internlm2 7b i1

RX 7700 XT 12GBOpción económica
12 GB VRAM (+4)432 GB/s (+152)
C
Sube la velocidad estimada de decodificación alrededor de un 82%.60.7 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 82%.

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 (+104)
C
Sube la velocidad estimada de decodificación alrededor de un 40%.46.8 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 40%.

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 (+360)
C
Sube la velocidad estimada de decodificación alrededor de un 178%.92.9 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 178%.

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

~$479 MSRP

Frequently asked questions

Can RX 6650 XT 8GB run MD Judge v0 2 internlm2 7b i1?

Yes, RX 6650 XT 8GB can run MD Judge v0 2 internlm2 7b i1 with a C grade (Tight fit). Expected decode speed: 33.4 tok/s.

How much VRAM does MD Judge v0 2 internlm2 7b i1 need?

MD Judge v0 2 internlm2 7b i1 (7B parameters) requires approximately 6.8 GB of memory with Q4_K_M quantization.

What is the best quantization for MD Judge v0 2 internlm2 7b i1?

The recommended quantization for MD Judge v0 2 internlm2 7b i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will MD Judge v0 2 internlm2 7b i1 run at on RX 6650 XT 8GB?

On RX 6650 XT 8GB, MD Judge v0 2 internlm2 7b i1 achieves approximately 33.4 tokens per second decode speed with a time-to-first-token of 5789ms using Q4_K_M quantization.

Can RX 6650 XT 8GB run MD Judge v0 2 internlm2 7b i1 for coding?

For coding workloads, MD Judge v0 2 internlm2 7b i1 on RX 6650 XT 8GB receives a C grade with 33.4 tok/s and 40K context.

What context window can MD Judge v0 2 internlm2 7b i1 use on RX 6650 XT 8GB?

On RX 6650 XT 8GB, MD Judge v0 2 internlm2 7b i1 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 6650 XT 8GBSee all hardware for MD Judge v0 2 internlm2 7b i1
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