Can Vicuna 13B run on RTX PRO 4000 Blackwell 24GB?
YES — With Offload
A74Great○Estimated from fit model
Vicuna 13B needs ~23.7 GB VRAM. RTX PRO 4000 Blackwell 24GB has 24.0 GB. With Q4_K_M quantization, expect ~71 tok/s.
Runtime: OllamaCapacity: OffloadBandwidth: MediumStack: 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) — 23.7 GB, 71.2 tok/s, Runs with offload
23.7 GB required24.0 GB available
Fit status
Runs with offload
Memory breakdown
Weights7.9 GB
KV Cache12.2 GB
Runtime1.2 GB
Headroom2.4 GB
See how fast it feels
See how fast it feelsVicuna 13B on RTX PRO 4000 Blackwell 24GB
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: 71.2 tok/s decode · 2.7s TTFT (warm) · 178 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Best improvement path
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|
| Chat | A | Runs well | 71.2 tok/s | 1484 ms | 4K |
| Coding | A | Runs with offload | 71.2 tok/s | 2720 ms | 4K |
| Agentic Coding | F | Too heavy | 23.9 tok/s | 11793 ms | 4K |
| Reasoning | A | Runs with offload | 71.2 tok/s | 3214 ms | 4K |
| RAG | F | Too heavy | 23.9 tok/s | 14741 ms | 4K |
Quantization options
How Vicuna 13B (13B params) fits at each quantization level on RTX PRO 4000 Blackwell 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|
Q2_K | 2 | 5.1 GB | Low | B66 |
Q3_K_S | 3 | 6.4 GB | Low | B67 |
NVFP4 | 4 |
Get started
Copy-paste commands to run Vicuna 13B on your machine.
Your hardware
More models your RTX PRO 4000 Blackwell 24GB can run
| Model | Params | Grade | Decode | Capabilities |
|---|
👁 Alibaba
Qwen3-Coder 30B A3B Instruct | 30.5B | S | 85.4 tok/s |
👁 Alibaba
Qwen 3.5 27B | 27B | S | 37 tok/s |
Frequently asked questions
Q8_0Best for your GPU | 8 | 13.9 GB | Very High | A71 |
👁 Alibaba
Qwen3-VL 30B A3B Instruct | 30B | S | 88.3 tok/s |
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
