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URL: https://willitrunai.com/can-run/vicuna-7b-on-rx-7700s-8gb

⇱ Vicuna 7B on Radeon RX 7700S 8GB? No — Alternatives

Can Vicuna 7B run on Radeon RX 7700S 8GB?

NO — Won't Fit

F0Won't run
○Estimated from fit model

Vicuna 7B needs ~13.8 GB but Radeon RX 7700S 8GB only has 8.0 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: LowStack: StandardBottleneck: Memory capacity
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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) — 13.8 GB, exceeds 8.0 GB available
13.8 GB required8.0 GB available
173% VRAM needed

5.8 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

9.5 tok/s

TTFT

20385 ms

Safe context

4K

Memory

13.8 GB / 8.0 GB

Offload

40%

Memory breakdown

Weights4.3 GB
KV Cache7.8 GB
Runtime0.9 GB
Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower
See how fast it feelsVicuna 7B on Radeon RX 7700S 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: 9.5 tok/s decode · 20.4s TTFT (warm) · 24 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 13.8 GB, but this setup only exposes 8.0 GB of usable VRAM.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatFToo heavy19.2 tok/s5513 ms4K
CodingFToo heavy9.5 tok/s20385 ms4K
Agentic CodingFToo heavy6.0 tok/s47176 ms4K
ReasoningFToo heavy9.5 tok/s24091 ms4K
RAGFToo heavy6.0 tok/s58970 ms4K

Quantization options

How Vicuna 7B (7B params) fits at each quantization level on Radeon RX 7700S 8GB (8.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
2.7 GB
LowC54
Q3_K_S
3
3.4 GB
LowC54
NVFP4
4
3.9 GB
MediumC54
Q4_K_M
4
4.3 GB
MediumC54
Q5_K_MBest for your GPU
5
5.0 GB
HighC53
Q6_K
6
5.7 GB
HighF0
Q8_0
8
7.5 GB
Very HighF0
F16
16
14.3 GB
MaximumF0

Upgrade options

Hardware that runs Vicuna 7B well

RX 7600 XT 16GBBudget pick
16 GB VRAM (+8)
C
Makes the model fit on the accelerator instead of staying completely out of reach.39.1 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$329 MSRP

RX 9060 XT 16GBBest value
16 GB VRAM (+8)320 GB/s (+32)
C
Makes the model fit on the accelerator instead of staying completely out of reach.47.2 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$349 MSRP

RX 7700 XT 12GBAMD upgrade
12 GB VRAM (+4)432 GB/s (+144)
D
Makes the model fit on the accelerator instead of staying completely out of reach.32 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 237%.

~$449 MSRP

RX 7900 XT 20GBBiggest leap
20 GB VRAM (+12)800 GB/s (+512)
B
Makes the model fit on the accelerator instead of staying completely out of reach.98 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$899 MSRP

Frequently asked questions

See all results for Radeon RX 7700S 8GBSee all hardware for Vicuna 7B