Can Vicuna 13B run on RTX PRO 4500 Blackwell 32GB?

YES — Runs Great

A78Great
Estimated from fit model

Vicuna 13B needs ~24.5 GB VRAM. RTX PRO 4500 Blackwell 32GB has 32.0 GB. With Q4_K_M quantization, expect ~95 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) 24.5 GB, 94.9 tok/s, Runs well
24.5 GB required32.0 GB available
77% VRAM used

Fit status

Runs well

Decode

94.9 tok/s

TTFT

2040 ms

Safe context

4K

Memory

24.5 GB / 32.0 GB

Memory breakdown

Weights7.9 GB
KV Cache12.2 GB
Runtime1.2 GB
Headroom3.2 GB

See how fast it feels

See how fast it feelsVicuna 13B on RTX PRO 4500 Blackwell 32GB
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: 94.9 tok/s decode · 2.0s TTFT (warm) · 237 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

WorkloadGradeFitDecodeTTFTContext
ChatARuns well94.9 tok/s1113 ms4K
CodingARuns well94.9 tok/s2040 ms4K
Agentic CodingBVery compromised (needs ~1 GB host RAM)54.7 tok/s5144 ms4K
ReasoningARuns well94.9 tok/s2411 ms4K
RAGBVery compromised (needs ~1 GB host RAM)54.7 tok/s6430 ms4K

Inference speed

Vicuna 13B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Vicuna 13B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~151 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / MacMemoryQuantSpeed (tok/s)Fits?
NVIDIARTX 5090 32GB
32 GBQ4_K_M151.4Fits
NVIDIARTX 4090 24GB
24 GBQ4_K_M96.6Offloads
RX 7900 XTX 24GB
24 GBQ4_K_M87.2Offloads
NVIDIARTX 3090 24GB
24 GBQ4_K_M82.6Offloads
Mac Studio M3 Ultra 256GB
256 GBQ4_K_M70.2Fits
Mac Studio M2 Ultra 128GB
128 GBQ4_K_M58.5Fits
Mac Studio M1 Ultra 128GB
128 GBQ4_K_M55.5Fits
MacBook Pro M4 Max 128GB
128 GBQ4_K_M38.2Fits
MacBook Pro M4 Max 64GB
64 GBQ4_K_M38.2Fits
MacBook Pro M3 Max 64GB
64 GBQ4_K_M30.3Fits
MacBook Pro M1 Max 64GB
64 GBQ4_K_M27.7Fits
NVIDIARTX 4080 Super 16GB
16 GBQ4_K_M27.1Too big
MacBook Pro M4 Pro 48GB
48 GBQ4_K_M23.3Fits
NVIDIARTX 4070 12GB
12 GBQ4_K_M9.5Too big
NVIDIARTX 3060 12GB
12 GBQ4_K_M6.0Too big
NVIDIARTX 4060 8GB
8 GBQ4_K_M3.8Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Vicuna 13B (13B params) fits at each quantization level on RTX PRO 4500 Blackwell 32GB (32.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
5.1 GB
LowB64
Q3_K_S
3
6.4 GB
LowB65
NVFP4
4
7.3 GB
MediumB65
Q4_K_M
4
7.9 GB
MediumB65
Q5_K_M
5
9.4 GB
HighB66
Q6_K
6
10.7 GB
HighB67
Q8_0
8
13.9 GB
Very HighB68
F16Best for your GPU
16
26.7 GB
MaximumB69

Get started

Copy-paste commands to run Vicuna 13B on your machine.

Run

ollama run vicuna:13b

Your hardware

More models your RTX PRO 4500 Blackwell 32GB can run

ModelParamsGradeDecodeCapabilities
AlibabaQwen3-Coder 30B A3B Instruct30.5BS113.8 tok/s
AlibabaQwen 3.5 27B27BS49.4 tok/s
AlibabaQwen 3.6 27B27BS49.5 tok/s
AlibabaQwen 3.6 35B A3B35BS95.6 tok/s
AlibabaQwen3-VL 30B A3B Instruct30BS117.7 tok/s

Frequently asked questions

Can RTX PRO 4500 Blackwell 32GB run Vicuna 13B?

Yes, RTX PRO 4500 Blackwell 32GB can run Vicuna 13B with a A grade (Runs well). Expected decode speed: 94.9 tok/s.

How much VRAM does Vicuna 13B need?

Vicuna 13B (13B parameters) requires approximately 24.5 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 RTX PRO 4500 Blackwell 32GB?

On RTX PRO 4500 Blackwell 32GB, Vicuna 13B achieves approximately 94.9 tokens per second decode speed with a time-to-first-token of 2040ms using Q4_K_M quantization.

Can RTX PRO 4500 Blackwell 32GB run Vicuna 13B for coding?

For coding workloads, Vicuna 13B on RTX PRO 4500 Blackwell 32GB receives a A grade with 94.9 tok/s and 4K context.

What context window can Vicuna 13B use on RTX PRO 4500 Blackwell 32GB?

On RTX PRO 4500 Blackwell 32GB, 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.

See all results for RTX PRO 4500 Blackwell 32GBSee all hardware for Vicuna 13B
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