Can Vicuna 7B run on RTX 4000 Ada 20GB?

YES — Runs Great

B56Good

Estimated from fit model

Vicuna 7B needs ~15.3 GB VRAM. RTX 4000 Ada 20GB has 20.0 GB. With Q4_K_M quantization, expect ~66 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: 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) — 15.3 GB, 65.8 tok/s, Runs well

15.3 GB required20.0 GB available

77% VRAM used

Fit status

Runs well

Decode

65.8 tok/s

TTFT

2944 ms

Safe context

Memory

15.3 GB / 20.0 GB

Memory breakdown

Weights4.3 GB

KV Cache7.8 GB

Runtime1.2 GB

Headroom2.0 GB

See how fast it feels

See how fast it feelsVicuna 7B on RTX 4000 Ada 20GB
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: 65.8 tok/s decode · 2.9s TTFT (warm) · 164 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	65.8 tok/s	1606 ms	4K
Coding	B	Runs well	65.8 tok/s	2944 ms	4K
Agentic Coding	C	Very compromised (needs ~0.6 GB host RAM)	36.4 tok/s	7729 ms	4K
Reasoning	B	Runs well	65.8 tok/s	3479 ms	4K
RAG	C	Very compromised (needs ~0.6 GB host RAM)	36.4 tok/s	9662 ms	4K

Quantization options

How Vicuna 7B (7B params) fits at each quantization level on RTX 4000 Ada 20GB (20.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C46
Q3_K_S	3	3.4 GB	Low	C46
NVFP4	4

Get started

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

Run

ollama run vicuna

Upgrade options

Hardware that runs Vicuna 7B well

RTX 3090 24GBBudget pick

24 GB VRAM (+4)936 GB/s (+576)

Raises estimated decode speed by about 49%.98 tok/s decode

Raises estimated decode speed by about 49%.

~$1,499 MSRP

RTX 4090 24GBBest value

24 GB VRAM (+4)1008 GB/s (+648)

Raises estimated decode speed by about 49%.98 tok/s decode

Raises estimated decode speed by about 49%.

~$1,599 MSRP

RTX PRO 4000 Blackwell 24GBNVIDIA upgrade

24 GB VRAM (+4)672 GB/s (+312)

Raises estimated decode speed by about 49%.98 tok/s decode

Raises estimated decode speed by about 49%.

~$1,599 MSRP

Frequently asked questions

Can RTX 4000 Ada 20GB run Vicuna 7B?

Yes, RTX 4000 Ada 20GB can run Vicuna 7B with a B grade (Runs well). Expected decode speed: 65.8 tok/s.

How much VRAM does Vicuna 7B need?

Vicuna 7B (7B parameters) requires approximately 15.3 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 RTX 4000 Ada 20GB?

On RTX 4000 Ada 20GB, Vicuna 7B achieves approximately 65.8 tokens per second decode speed with a time-to-first-token of 2944ms using Q4_K_M quantization.

Can RTX 4000 Ada 20GB run Vicuna 7B for coding?

For coding workloads, Vicuna 7B on RTX 4000 Ada 20GB receives a B grade with 65.8 tok/s and 4K context.

What context window can Vicuna 7B use on RTX 4000 Ada 20GB?

On RTX 4000 Ada 20GB, 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 RTX 4000 Ada 20GB See all hardware for Vicuna 7B

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