Mac Studio M3 Ultra 256GBOpción económica
256 GB Unified (+76)
B
Este setup está bastante equilibrado para este modelo.70.2 tok/s decodificación
~$6,999 MSRP
Can Vicuna 13B run on NVIDIA B200 180GB?
YES — Runs Great
B67Good
Estimated from fit model
Vicuna 13B needs ~39.3 GB VRAM. NVIDIA B200 180GB has 180.0 GB. With Q4_K_M quantization, expect ~182 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: HighStack: 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.
Select quantization to explore
Q4_K_M (Medium quality) — 39.3 GB, 182.0 tok/s, Runs well
39.3 GB required180.0 GB available
22% VRAM used
Fit status
Runs well
Decode
182.0 tok/s
TTFT
1064 ms
Safe context
4K
Memory
39.3 GB / 180.0 GB
Memory breakdown
Weights7.9 GB
KV Cache12.2 GB
Runtime1.2 GB
Headroom18.0 GB
See how fast it feels
See how fast it feelsVicuna 13B on NVIDIA B200 180GB
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: 182.0 tok/s decode · 1.1s TTFT (warm) · 455 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 | 182.0 tok/s | 580 ms | 4K |
| Coding | B | Runs well | 182.0 tok/s | 1064 ms | 4K |
| Agentic Coding | B | Runs well | 182.0 tok/s | 1547 ms | 4K |
| Reasoning | B | Runs well | 182.0 tok/s | 1257 ms | 4K |
| RAG | B | Runs well | 182.0 tok/s | 1934 ms | 4K |
Quantization options
How Vicuna 13B (13B params) fits at each quantization level on NVIDIA B200 180GB (180.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.1 GB | Low | B58 |
Q3_K_S | 3 | 6.4 GB | Low | B58 |
NVFP4 | 4 | 7.3 GB | Medium | B58 |
Q4_K_M | 4 | 7.9 GB | Medium | B58 |
Q5_K_M | 5 | 9.4 GB | High | B58 |
Q6_K | 6 | 10.7 GB | High | B58 |
Q8_0 | 8 | 13.9 GB | Very High | B58 |
F16Best for your GPU | 16 | 26.7 GB | Maximum | B59 |
Get started
Copy-paste commands to run Vicuna 13B on your machine.
Run
ollama run vicuna:13bOpciones de mejora
Hardware que ejecuta bien Vicuna 13B
Frequently asked questions
Can NVIDIA B200 180GB run Vicuna 13B?
Yes, NVIDIA B200 180GB can run Vicuna 13B with a B grade (Runs well). Expected decode speed: 182.0 tok/s.
How much VRAM does Vicuna 13B need?
Vicuna 13B (13B parameters) requires approximately 39.3 GB of memory with Q4_K_M quantization.
What is the best quantization for Vicuna 13B?
The recommended quantization for Vicuna 13B is Q4_K_M, which balances quality and memory efficiency.
What speed will Vicuna 13B run at on NVIDIA B200 180GB?
On NVIDIA B200 180GB, Vicuna 13B achieves approximately 182.0 tokens per second decode speed with a time-to-first-token of 1064ms using Q4_K_M quantization.
Can NVIDIA B200 180GB run Vicuna 13B for coding?
For coding workloads, Vicuna 13B on NVIDIA B200 180GB receives a B grade with 182.0 tok/s and 4K context.
What context window can Vicuna 13B use on NVIDIA B200 180GB?
On NVIDIA B200 180GB, Vicuna 13B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.
Embed this result▼
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/vicuna-13b-on-b200-180gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: