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sarvamai / sarvam_moe

sarvam-30b

32.2 billion parameters, routing 6 of 128 experts per token.

Open in the calculator →

Architecturepublished data
Quick answer
sarvam-30b needs about 20.5 GB at Q4_K_M — the format most people download — with 8,192 tokens of context. It fits entirely on 12 of 18 common devices, starting with the NVIDIA GeForce RTX 4090 (24 GB). What else fits in 24 GB. No hardware for it? Rent a GPU that runs it, priced live.

Sized at 8,192 tokens of context with the whole model in device memory, across 18 common devices. Speeds are estimates, not benchmarks.

Find the NVIDIA GeForce RTX 4090, the smallest card listed here that holds it: Amazon ↗ · eBay (new and used) ↗Store links may pay us a commission. They never decide which card is suggested — the memory arithmetic does.

Weight-file size by format

FormatSizeRangeBasis
FP1664.3 GB62.2 GB – 64.7 GBreconstructed size
Q8_034.2 GB33.0 GB – 34.4 GBreconstructed size
Q6_K26.4 GB25.5 GB – 26.5 GBreconstructed size
Q5_K_M22.9 GB22.0 GB – 23.0 GBreconstructed size
Q5_022.3 GB21.4 GB – 22.4 GBreconstructed size
Q4_K_M19.5 GB18.6 GB – 19.8 GBreconstructed size
Q4_018.4 GB17.5 GB – 18.7 GBreconstructed size
Q3_K_M16.1 GB15.0 GB – 17.3 GBreconstructed size
Q2_K12.1 GB11.2 GB – 12.6 GBreconstructed size

A file size is not the memory a run needs: the KV cache and the runtime reserve come on top, and the calculator adds both.

What the labels mean
published data
Read from a published source — a file's byte count, a model's configuration or a manufacturer's specification — or exact arithmetic on such values. Not a measurement on a machine.
reconstructed size
Weight size reconstructed from the pinned architecture, because no published file exists.
size range
Only a lower and an upper bound are claimed for this weight size.

Hardware ladder

Q4_K_M at 8,192 tokens · 12 of 18 devices hold it entirely
DeviceMemoryVerdictDecodeCalculator
NVIDIA GeForce RTX 507020.5 GB of 12 GB9 layers on system RAM~47 tok/s with offloadOpen →
AMD Radeon™ RX 9070 XT20.5 GB of 16 GB5 layers on system RAM~72 tok/s with offloadOpen →
NVIDIA GeForce RTX 408020.5 GB of 16 GB5 layers on system RAM~71 tok/s with offloadOpen →
NVIDIA GeForce RTX 508020.5 GB of 16 GB5 layers on system RAM~77 tok/s with offloadOpen →
NVIDIA GeForce RTX 5070 Ti20.5 GB of 16 GB5 layers on system RAM~75 tok/s with offloadOpen →
NVIDIA GeForce RTX 5060 Ti 16GB20.5 GB of 16 GB5 layers on system RAM~61 tok/s with offloadOpen →
NVIDIA GeForce RTX 409020.5 GB of 24 GBfits~276 tok/sOpen →
AMD Radeon™ RX 7900 XTX20.5 GB of 24 GBfits~301 tok/sOpen →
NVIDIA GeForce RTX 309020.5 GB of 24 GBfits~256 tok/sOpen →
NVIDIA GeForce RTX 509020.5 GB of 32 GBfits~491 tok/sOpen →
NVIDIA RTX 6000 Ada Generation20.5 GB of 48 GBfits~263 tok/sOpen →
Apple M4 Pro20.5 GB of 64 GBfits~65 tok/sOpen →
Apple M5 Pro20.5 GB of 64 GBfits~70 tok/sOpen →
AMD Ryzen AI Max+ 395 with Radeon 8060S20.5 GB of 128 GBfits~80 tok/sOpen →
Apple M3 Max20.5 GB of 128 GBfits~81 tok/sOpen →
Apple M4 Max20.5 GB of 128 GBfits~95 tok/sOpen →
NVIDIA DGX Spark20.5 GB of 128 GBfits~75 tok/sOpen →
Apple M2 Ultra20.5 GB of 192 GBfits~111 tok/sOpen →

Decode is a calibrated estimate from the published calibration; capacity is the manufacturer's published ceiling, not guaranteed free memory. Fitting in memory is not the same as loading: whether the runtime and version you have supports this architecture and format on that machine has not been tested here. Offloaded rows assume there is enough system RAM for the overflow — the calculator checks that against the RAM you declare. Each “Open” link keeps this model, format and context.

Run it in the cloud

Rent a GPU that runs sarvam-30b

Every rentable machine that holds the whole model, from Vast.ai and RunPod, with this site's speed estimate for each and the provider's own price, read live. Cheapest first; sort by cost per token or speed instead, or switch the format.

Prices · 18:30 UTC, 22 Sept
26 configurations in stock · 7 out of stock
MachineSpeedPer hourPer M tokensWhereRent
2× RTX 306024 GBcheapest~197 tok/sFaster than you read$0.11$0.15Vast.aiMarketplace · 98.0% reliableRent on Vast.ai
1× RTX 309024 GBrecommendedbest value~256 tok/sFaster than you read$0.12$0.13Vast.aiMarketplace · 99.5% reliableRunPod $0.50/h Rent on Vast.ai
1× RTX 3090 Ti24 GB~276 tok/sFaster than you read$0.19$0.19Vast.aiMarketplace · 99.6% reliableRunPod $0.27/h Rent on Vast.ai
2× RTX 407024 GB~276 tok/sFaster than you read$0.19$0.19Vast.aiMarketplace · 99.8% reliableRent on Vast.ai
2× RTX 4060 Ti 16GB32 GB~157 tok/sFaster than you read$0.22$0.40Vast.aiMarketplace · 99.7% reliableRent on Vast.ai
1× L424 GB~82 tok/sFaster than you read$0.26$0.86Vast.aiMarketplace · 98.9% reliableRunPod $0.49/h Rent on Vast.ai
2× RTX 5060 Ti 16GB32 GB~245 tok/sFaster than you read$0.26$0.29Vast.aiMarketplace · 99.5% reliableRent on Vast.ai
1× RTX 6000 Ada48 GB~263 tok/sFaster than you read$0.26$0.28Vast.aiMarketplace · 99.9% reliableRunPod $0.84/h Rent on Vast.ai

First time renting a GPU? How it works, in four steps
  1. Create an account and add credit. Both providers are prepaid: $10 is enough to try any card on this page for hours. A new RunPod account that arrives through a referral link gets a one-time $5 credit when it first adds $10.
  2. Pick the card and the count shown here, and a template: llama.cpp or vLLM for an OpenAI-compatible API, or one with a web chat if you only want to talk to the model. “How to launch it” below gives the exact image and command.
  3. Wait for the download. The weights are fetched on the machine; tens of gigabytes take a few minutes on a data-centre connection. You pay by the second from the moment it starts.
  4. Stop it when you are done. A running machine bills even when idle. A stopped one costs nothing per hour, though its disk is usually still billed until you delete it.
How to launch it

No Q4_K_M file of sarvam-30b is catalogued here, so there is no exact command to vouch for. Start the machine from the llama.cpp server image ghcr.io/ggml-org/llama.cpp:server-cuda and point -hf at a Q4_K_M build from the publisher or a quantizer you trust — search Hugging Face for one. Check its file size against the memory figure on this page before you rent.

Every machine holds the whole model at Q4_K_M and 8,192 tokens of context, no offload. Speeds are this site’s single-stream decode estimates; prices are what each provider’s own API quoted, on-demand, for the whole machine. Vast hosts below 98% measured reliability are left out.

Referral links Vast.ai, RunPod and Novita pay us a share of what you spend if you sign up through these buttons. It costs you nothing, and it never decides an order or a recommendation: both are computed from the live price and the speed, and options that pay us nothing are listed and recommended on the same terms. How we rank

Buying a card instead, or paying by the token? Compare a year of sarvam-30b three ways →

Under the hood

Architecture, read from the publisher’s file

The numbers every figure above is computed from, with the file they came from.

Architecture

✓ Architecture read from the published config.json

Retrieved 2026-09-12 at pinned commit 071ae95e9336; parameter count from the safetensors index at the same revision.

Architecture
sarvam_moe
Layers
19
Hidden size
4,096
Attention heads
64
KV heads
4
Head dimension
64
Feed-forward width
8,192
Vocabulary
262,144
Context ceiling
131,072
RoPE theta
8,000,000
Experts
128
Experts / token
6
Expert width
1,024
Shared experts
1
Shared expert width
1,024

Where the memory goes

tokenembedding× 19 decoder blocksGrouped-query attention64 query · 4 KV headsfull contextRouted experts6 of 128 per tokenall residentoutputprojectiongrows with contextcapacity ≠ traffic

Grouped-query attention shares each key/value head across 16 query heads, so the KV cache is 6% of what multi-head attention would need at the same context.

Open the exact official config.json ↗

More from sarvamai

sarvam-105b

Use this answer in your own product

As JSON, from the API
curl -s https://llmbottleneck.com/v1/analyze \
  -H "Authorization: Bearer $LLMB_KEY" -H "content-type: application/json" \
  -d '{"model":"sarvamai-sarvam-30b","quantization":"Q4_K_M","context":8192,"hardware":"nvidia-geforce-rtx-4090"}'

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On your page, as a widget
<script src="https://llmbottleneck.com/widget.js"
  data-model="sarvamai-sarvam-30b" data-quantization="Q4_K_M"
  data-hardware="nvidia-geforce-rtx-4090" data-context="8192"></script>

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Citing this page

LLM Bottleneck. “sarvam-30b VRAM and hardware requirements.” Architecture from sarvamai/sarvam-30b at revision 071ae95e9336, retrieved 2026-09-12. https://llmbottleneck.com/models/sarvamai-sarvam-30b

Every figure above is either the published value or a reconstruction whose measured error is on the accuracy page.

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catalogue 2026-10-03models 327devices 135