Can Vicuna 7B run on Radeon PRO W7700 16GB?

YES — Tight Fit

C54Usable

Estimated from fit model

Vicuna 7B needs ~14.6 GB VRAM. Radeon PRO W7700 16GB has 16.0 GB. With Q4_K_M quantization, expect ~80 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: MediumStack: 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) — 14.6 GB, 79.6 tok/s, Tight fit

14.6 GB required16.0 GB available

91% VRAM used

Fit status

Tight fit

Decode

79.6 tok/s

TTFT

2433 ms

Safe context

Memory

14.6 GB / 16.0 GB

Memory breakdown

Weights4.3 GB

KV Cache7.8 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsVicuna 7B on Radeon PRO W7700 16GB
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: 79.6 tok/s decode · 2.4s TTFT (warm) · 199 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	B	Runs well	79.6 tok/s	1327 ms	4K
Coding	C	Tight fit	79.6 tok/s	2433 ms	4K
Agentic Coding	F	Too heavy	29.4 tok/s	9575 ms	4K
Reasoning	C	Tight fit	79.6 tok/s	2875 ms	4K
RAG	F	Too heavy	29.4 tok/s	11968 ms	4K

Quantization options

How Vicuna 7B (7B params) fits at each quantization level on Radeon PRO W7700 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C47
Q3_K_S	3	3.4 GB	Low	C48
NVFP4	4	3.9 GB	Medium	C48
Q4_K_M	4	4.3 GB	Medium	C49
Q5_K_M	5	5.0 GB	High	C49
Q6_K	6	5.7 GB	High	C50
Q8_0Best for your GPU	8	7.5 GB	Very High	C52
F16	16	14.3 GB	Maximum	F0

Get started

Copy-paste commands to run Vicuna 7B on your machine.

Run

ollama run vicuna

Opciones de mejora

Hardware que ejecuta bien Vicuna 7B

RX 7900 XT 20GBOpción económica

20 GB VRAM (+4)800 GB/s (+224)

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.98 tok/s decodificación

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

~$899 MSRP

RX 7900 XTX 24GBMejor relación calidad-precio

24 GB VRAM (+8)960 GB/s (+384)

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.98 tok/s decodificación

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

~$999 MSRP

Frequently asked questions

Can Radeon PRO W7700 16GB run Vicuna 7B?

Yes, Radeon PRO W7700 16GB can run Vicuna 7B with a C grade (Tight fit). Expected decode speed: 79.6 tok/s.

How much VRAM does Vicuna 7B need?

Vicuna 7B (7B parameters) requires approximately 14.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Vicuna 7B?

The recommended quantization for Vicuna 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will Vicuna 7B run at on Radeon PRO W7700 16GB?

On Radeon PRO W7700 16GB, Vicuna 7B achieves approximately 79.6 tokens per second decode speed with a time-to-first-token of 2433ms using Q4_K_M quantization.

Can Radeon PRO W7700 16GB run Vicuna 7B for coding?

For coding workloads, Vicuna 7B on Radeon PRO W7700 16GB receives a C grade with 79.6 tok/s and 4K context.

What context window can Vicuna 7B use on Radeon PRO W7700 16GB?

On Radeon PRO W7700 16GB, Vicuna 7B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.

See all results for Radeon PRO W7700 16GB See all hardware for Vicuna 7B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/vicuna-7b-on-radeon-pro-w7700-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: