24 GB VRAM (+4)936 GB/s (+576)
B
Raises estimated decode speed by about 49%.98 tok/s decode
Raises estimated decode speed by about 49%.
~$1,499 MSRP
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
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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.
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
4K
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 vicunaUpgrade options