Sarvam AI/Dense

Sarvam AISarvam 30B

Indian multilingual model. Strong Hindi, Tamil, Telugu, Bengali + English. Apache 2.0.

chatmultilingual
30B
Parameters
32K
Context length
14
Benchmarks
14
Quantizations
Architecture
Dense
Released
2026-03-06
Layers
48
KV Heads
8
Head Dim
128
Family
other

Quantization Options

QuantBitsVRAMQuality
IQ3_XXS3.2512.7 GBlow
IQ3_XS3.513.6 GBlow
Q3_K_S3.6414.1 GBlow
IQ3_M3.7614.6 GBlow
Q3_K_M415.5 GBlow
Q3_K_L4.316.6 GBmoderate
IQ4_XS4.4617.2 GBmoderate
Q4_K_S4.6718.0 GBmoderate
Q4_K_M4.8918.8 GBgood
Q5_K_S5.5721.4 GBgood
Q5_K_M5.721.9 GBgood
Q6_K6.5625.1 GBexcellent
Q8_08.532.4 GBlossless
FP161660.5 GBlossless

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

READY TO RUN THIS?RENT BY THE HOUR

RENT A GPU AND RUN SARVAM 30B NOW

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

Community Ratings

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Benchmarks (14)

MATH97.0
MBPP92.7
HumanEval92.1
MMLU-PRO80.0
AIME80.0
GPQA Diamond66.5
τ²-Bench34.5
SWE-bench34.0
IFBench26.5
SciCode19.2
AA Intelligence12.3
AA Coding7.9
HLE7.0
Terminal-Bench2.3

Run this model

Easiest way to get started·Beginners
DOCS ↗
curl -fsSL https://ollama.com/install.sh | sh
$ollama run other:30b-q4_K_M

Tag may need adjustment — check ollama.com/library/other for available tags.

▸ 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.

AMD RX 7900 XT
20 GB VRAM • 800 GB/s
AMD
$849
NVIDIA A10M
20 GB VRAM • 500 GB/s
NVIDIA
NVIDIA RTX A4500
20 GB VRAM • 640 GB/s
NVIDIA
$2000
NVIDIA RTX 4090
24 GB VRAM • 1008 GB/s
NVIDIA
$1599
NVIDIA RTX 3090 Ti
24 GB VRAM • 1008 GB/s
NVIDIA
$999
NVIDIA RTX 3090
24 GB VRAM • 936 GB/s
NVIDIA
$850
AMD RX 7900 XTX
24 GB VRAM • 960 GB/s
AMD
$999
Apple M4 Pro (24GB)
24 GB VRAM • 273 GB/s
APPLE
$1399
NVIDIA L4 24GB
24 GB VRAM • 300 GB/s
NVIDIA
$2500
NVIDIA A10 24GB
24 GB VRAM • 600 GB/s
NVIDIA
$3500
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 Quadro RTX 6000
24 GB VRAM • 672 GB/s
NVIDIA
$4000
NVIDIA GeForce RTX 3090
24 GB VRAM • 936 GB/s
NVIDIA
$1499
NVIDIA A10 PCIe
24 GB VRAM • 600 GB/s
NVIDIA
NVIDIA A10G
24 GB VRAM • 600 GB/s
NVIDIA
NVIDIA RTX A5000
24 GB VRAM • 768 GB/s
NVIDIA
$2500
NVIDIA GeForce RTX 4090
24 GB VRAM • 1010 GB/s
NVIDIA
$1599
NVIDIA L40 CNX
24 GB VRAM • 864 GB/s
NVIDIA
$5000

Find the best GPU for Sarvam 30B

Build Hardware for Sarvam 30B
▸ SPEC SHEET

Sarvam 30B30B Dense.

▸ SPECIFICATIONS
PARAMETERS
30B
ARCHITECTURE
Dense Transformer
CONTEXT LENGTH
32K tokens
CAPABILITIES
chat, multilingual
RELEASE DATE
2026-03-06
PROVIDER
Sarvam AI
FAMILY
other
▸ VRAM REQUIREMENTS
QUANTBPWVRAMQUALITY
IQ3_XXS3.2512.7 GB82%
IQ3_XS3.513.6 GB84%
Q3_K_S3.6414.1 GB85%
IQ3_M3.7614.6 GB86%
Q3_K_M415.5 GB88%
Q3_K_L4.316.6 GB90%
IQ4_XS4.4617.2 GB92%
Q4_K_S4.6718.0 GB93%
Q4_K_M4.8918.8 GB94%
Q5_K_S5.5721.4 GB96%
Q5_K_M5.721.9 GB96%
Q6_K6.5625.1 GB97%
Q8_08.532.4 GB100%
FP161660.5 GB100%
§ 01BENCHMARK SCORES
HumanEval92.1
MMLU-PRO80.0
MATH97.0
MBPP92.7
SWE-bench34.0
AIME80.0
GPQA Diamond66.5
HLE7.0
AA Intelligence12.3
AA Coding7.9
aa_ifbench26.5
aa_terminal_bench2.3
aa_tau234.5
aa_scicode19.2