FitMyLLM
LMSYS/Dense

LMSYSVicuna 33B

Vicuna 33B — largest Vicuna. Good instruction following for its era.

chat
33B
Parameters
4K
Context length
7
Benchmarks
14
Quantizations
0
Architecture
Dense
Released
2023-06-22
Layers
60
KV Heads
52
Head Dim
128
Family
llama

Quantization Options

QuantBitsVRAM @ 4KQuality
IQ3_XXS3.25
13.9 GB
low
IQ3_XS3.5
14.9 GB
low
Q3_K_S3.64
15.5 GB
low
IQ3_M3.76
16.0 GB
low
Q3_K_M4
17.0 GB
low
Q3_K_L4.3
18.2 GB
moderate
IQ4_XS4.46
18.9 GB
moderate
Q4_K_S4.67
19.8 GB
moderate
Q4_K_M4.89
20.7 GB
good
Q5_K_S5.57
23.5 GB
good
Q5_K_M5.7
24.0 GB
good
Q6_K6.56
27.5 GB
excellent
Q8_08.5
35.6 GB
lossless
FP1616
66.5 GB
lossless

Select your GPU above to see speed estimates and compatibility for each quantization.

Deploying for a team or in production? Size GPUs, cost & scaling in Enterprise →
READY TO RUN THIS?RENT BY THE HOUR

RENT A GPU AND RUN VICUNA 33B NOW

Spin up an A100 / H100 / 4090 in ~60s. Pay by the second. Cancel anytime.

Community Ratings

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Benchmarks (7)

Arena Elo1106
IFEval46.0
BBH26.0
MMLU-PRO17.1
MUSR7.2
MATH4.3
GPQA2.6

Run this model

Easiest way to get started·Beginners
DOCS ↗
curl -fsSL https://ollama.com/install.sh | sh
$ollama run vicuna:33b-q4_K_M

Downloads and runs automatically. Add --verbose for speed stats.

▸ SETUP GUIDE
>_

Auto-setup with fitmyllm CLI

Detects your GPU, recommends the best model, downloads it, and starts chatting — zero config. Benchmarks your speed and contributes anonymous data to improve predictions.

pip install fitmyllmthen run fitmyllmLearn more
Auto-detect GPULive tok/s in chatSpeed benchmarks9 inference engines

GPUs that can run this model

At Q4_K_M quantization. Sorted by minimum VRAM.

Apple M4 Pro (24GB)
24 GB VRAM • 273 GB/s
APPLE
$1399
NVIDIA L4 24GB
24 GB VRAM • 300 GB/s
NVIDIA
$2500
Apple M2 (24GB)
24 GB VRAM • 100 GB/s
APPLE
$999
Apple M3 (24GB)
24 GB VRAM • 100 GB/s
APPLE
$999
Apple M4 (24GB)
24 GB VRAM • 120 GB/s
APPLE
$699
NVIDIA Tesla M40 24 GB
24 GB VRAM • 288 GB/s
NVIDIA
NVIDIA Tesla P10
24 GB VRAM • 694 GB/s
NVIDIA
NVIDIA Tesla P40
24 GB VRAM • 347 GB/s
NVIDIA
NVIDIA RTX A5000
24 GB VRAM • 768 GB/s
NVIDIA
$2500
NVIDIA L40 CNX
24 GB VRAM • 864 GB/s
NVIDIA
$5000
NVIDIA L40G
24 GB VRAM • 864 GB/s
NVIDIA
$5000

Find the best GPU for Vicuna 33B

Build Hardware for Vicuna 33B

Vicuna 33B — largest Vicuna. Good instruction following for its era.

▸ SPEC SHEET

Vicuna 33B33B Dense.

▸ SPECIFICATIONS
PARAMETERS
33B
ARCHITECTURE
Dense Transformer
CONTEXT LENGTH
4K tokens
CAPABILITIES
chat
RELEASE DATE
2023-06-22
PROVIDER
LMSYS
FAMILY
llama
▸ VRAM REQUIREMENTS
QUANTBPWVRAMQUALITY
IQ3_XXS3.2513.9 GB82%
IQ3_XS3.514.9 GB84%
Q3_K_S3.6415.5 GB85%
IQ3_M3.7616.0 GB86%
Q3_K_M417.0 GB88%
Q3_K_L4.318.2 GB90%
IQ4_XS4.4618.9 GB92%
Q4_K_S4.6719.8 GB93%
Q4_K_M4.8920.7 GB94%
Q5_K_S5.5723.5 GB96%
Q5_K_M5.724.0 GB96%
Q6_K6.5627.5 GB97%
Q8_08.535.6 GB100%
FP161666.5 GB100%
§ 01BENCHMARK SCORES
MMLU-PRO17.1
MATH4.3
IFEval46.0
BBH26.0
GPQA2.6
MUSR7.2
Arena Elo1106.0
§ 02RUN COMMAND

Run Vicuna 33B locally with Ollama — needs 20.7 GB VRAM at Q4_K_M:

$ollama run vicuna:33b
§ 03COMPATIBLE GPUs
30 @ Q4_K_M