Will It Run AI

Can OpenChat 3.5 7B Starling v2.0 i1 run on Radeon PRO W7600 8GB?

YES — Tight Fit

C51Usable
Estimated from fit model

OpenChat 3.5 7B Starling v2.0 i1 needs ~6.8 GB VRAM. Radeon PRO W7600 8GB has 8.0 GB. With Q4_K_M quantization, expect ~40 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, 39.8 tok/s, Tight fit
6.8 GB required8.0 GB available
85% VRAM used

Fit status

Tight fit

Decode

39.8 tok/s

TTFT

4865 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 feelsOpenChat 3.5 7B Starling v2.0 i1 on Radeon PRO W7600 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: 39.8 tok/s decode · 4.9s TTFT (warm) · 100 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 well39.8 tok/s2654 ms40K
CodingCTight fit39.8 tok/s4865 ms40K
Agentic CodingCRuns with offload39.8 tok/s7076 ms40K
ReasoningCTight fit39.8 tok/s5750 ms40K
RAGCRuns with offload39.8 tok/s8846 ms40K

Quantization options

How OpenChat 3.5 7B Starling v2.0 i1 (7B params) fits at each quantization level on Radeon PRO W7600 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 OpenChat 3.5 7B Starling v2.0 i1 on your machine.

Run

lms load hf-mradermacher--openchat-3-5-7b-starling-v2-0-i1-gguf && lms server start

Opciones de mejora

Hardware que ejecuta bien OpenChat 3.5 7B Starling v2.0 i1

RX 7700 XT 12GBOpción económica
12 GB VRAM (+4)432 GB/s (+144)
C
Sube la velocidad estimada de decodificación alrededor de un 53%.60.7 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 53%.

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)
C
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)
C
Sube la velocidad estimada de decodificación alrededor de un 133%.92.9 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 133%.

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

~$479 MSRP

Frequently asked questions

Can Radeon PRO W7600 8GB run OpenChat 3.5 7B Starling v2.0 i1?

Yes, Radeon PRO W7600 8GB can run OpenChat 3.5 7B Starling v2.0 i1 with a C grade (Tight fit). Expected decode speed: 39.8 tok/s.

How much VRAM does OpenChat 3.5 7B Starling v2.0 i1 need?

OpenChat 3.5 7B Starling v2.0 i1 (7B parameters) requires approximately 6.8 GB of memory with Q4_K_M quantization.

What is the best quantization for OpenChat 3.5 7B Starling v2.0 i1?

The recommended quantization for OpenChat 3.5 7B Starling v2.0 i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will OpenChat 3.5 7B Starling v2.0 i1 run at on Radeon PRO W7600 8GB?

On Radeon PRO W7600 8GB, OpenChat 3.5 7B Starling v2.0 i1 achieves approximately 39.8 tokens per second decode speed with a time-to-first-token of 4865ms using Q4_K_M quantization.

Can Radeon PRO W7600 8GB run OpenChat 3.5 7B Starling v2.0 i1 for coding?

For coding workloads, OpenChat 3.5 7B Starling v2.0 i1 on Radeon PRO W7600 8GB receives a C grade with 39.8 tok/s and 40K context.

What context window can OpenChat 3.5 7B Starling v2.0 i1 use on Radeon PRO W7600 8GB?

On Radeon PRO W7600 8GB, OpenChat 3.5 7B Starling v2.0 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 Radeon PRO W7600 8GBSee all hardware for OpenChat 3.5 7B Starling v2.0 i1
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<iframe src="https://willitrunai.com/embed/hf-mradermacher--openchat-3-5-7b-starling-v2-0-i1-gguf-on-radeon-pro-w7600-8gb" 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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