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meituan-longcat / architecture

LongCat-2.0

1775.6 billion parameters.

Open in the calculator →

Architecturepublished data
Quick answer
LongCat-2.0 needs about 1091.2 GB at Q4_K_M — the format most people download — with 8,192 tokens of context. None of the 18 common devices listed below holds it entirely at this setting. 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.

Weight-file size by format

FormatSizeRangeBasis
FP163552.7 GB2485.8 GB – 3586.7 GBsize range
Q8_01888.1 GB1320.6 GB – 1908.0 GBsize range
Q6_K1458.2 GB1019.6 GB – 1474.3 GBsize range
Q5_K_M1268.4 GB854.5 GB – 1474.3 GBsize range
Q5_01239.1 GB854.5 GB – 1474.3 GBsize range
Q4_K_M1090.0 GB699.1 GB – 1474.3 GBsize range
Q4_01033.2 GB699.1 GB – 1474.3 GBsize range
Q3_K_M888.0 GB534.1 GB – 1474.3 GBsize range
Q2_K703.1 GB407.8 GB – 1474.3 GBsize range

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 · 0 of 18 devices hold it entirely
DeviceMemoryVerdictDecodeCalculator
NVIDIA GeForce RTX 50701091.2 GB of 12 GB38 layers on system RAM<0.1 tok/s with offloadOpen →
AMD Radeon™ RX 9070 XT1091.2 GB of 16 GB38 layers on system RAM<0.1 tok/s with offloadOpen →
NVIDIA GeForce RTX 40801091.2 GB of 16 GB38 layers on system RAM<0.1 tok/s with offloadOpen →
NVIDIA GeForce RTX 50801091.2 GB of 16 GB38 layers on system RAM<0.1 tok/s with offloadOpen →
NVIDIA GeForce RTX 5070 Ti1091.2 GB of 16 GB38 layers on system RAM<0.1 tok/s with offloadOpen →
NVIDIA GeForce RTX 5060 Ti 16GB1091.2 GB of 16 GB38 layers on system RAM<0.1 tok/s with offloadOpen →
NVIDIA GeForce RTX 40901091.2 GB of 24 GB38 layers on system RAM<0.1 tok/s with offloadOpen →
AMD Radeon™ RX 7900 XTX1091.2 GB of 24 GB38 layers on system RAM<0.1 tok/s with offloadOpen →
NVIDIA GeForce RTX 30901091.2 GB of 24 GB38 layers on system RAM<0.1 tok/s with offloadOpen →
NVIDIA GeForce RTX 50901091.2 GB of 32 GB37 layers on system RAM<0.1 tok/s with offloadOpen →
NVIDIA RTX 6000 Ada Generation1091.2 GB of 48 GB37 layers on system RAM<0.1 tok/s with offloadOpen →
Apple M4 Pro1091.2 GB of 64 GBdoes not fitnot calibratedOpen →
Apple M5 Pro1091.2 GB of 64 GBdoes not fitnot calibratedOpen →
AMD Ryzen AI Max+ 395 with Radeon 8060S1091.2 GB of 128 GBdoes not fitnot calibratedOpen →
Apple M3 Max1091.2 GB of 128 GBdoes not fitnot calibratedOpen →
Apple M4 Max1091.2 GB of 128 GBdoes not fitnot calibratedOpen →
NVIDIA DGX Spark1091.2 GB of 128 GBdoes not fitnot calibratedOpen →
Apple M2 Ultra1091.2 GB of 192 GBdoes not fitnot calibratedOpen →

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 LongCat-2.0

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
1 configuration in stock · 4 out of stock
MachineSpeedPer hourPer M tokensWhereRent
8× H2001128 GBrecommendedcheapestbest valuefastest~23 tok/sAbout reading pace$26.52$308.24Vast.aiMarketplace · 99.3% reliableRent on Vast.ai
No GPU at all

Use LongCat-2.0 by the token

For one person chatting, that is about 257× cheaper than the best-value rented card above ($308.24 per million tokens). A rented GPU pays off when you keep it busy — many requests batched together, long agent runs — or when the data must stay on a machine you control, or the exact file you want is not served anywhere.

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 LongCat-2.0 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 LongCat-2.0 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 2026-09-01 at pinned commit 834bf5ffe304; parameter count from the safetensors index at the same revision.

Layers
38
Hidden size
8,192
Attention heads
64
Vocabulary
163,840
Context ceiling
262,144
RoPE theta
1,000,000
Experts
768
Latent KV rank
512
Open the exact official config.json ↗

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":"meituan-longcat-longcat-2-0","quantization":"Q4_K_M","context":8192,"hardware":"nvidia-geforce-rtx-4090"}'

Same engine, same evidence, every field sourced. Free key in one step →

On your page, as a widget
<script src="https://llmbottleneck.com/widget.js"
  data-model="meituan-longcat-longcat-2-0" data-quantization="Q4_K_M"
  data-hardware="nvidia-geforce-rtx-4090" data-context="8192"></script>

No key needed. Unbranded, with your own buy button, on Pro and Business →

Citing this page

LLM Bottleneck. “LongCat-2.0 VRAM and hardware requirements.” Architecture from meituan-longcat/LongCat-2.0 at revision 834bf5ffe304, retrieved 2026-09-01. https://llmbottleneck.com/models/meituan-longcat-longcat-2-0

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