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LiquidAI / lfm2

LFM2.5-1.2B-Instruct

1.17 billion parameters.

Open in the calculator →

Architecturepublished data
Quick answer
LFM2.5-1.2B-Instruct needs about 1.63 GB at Q4_K_M — the format most people download — with 8,192 tokens of context. It fits entirely on 18 of 18 common devices, starting with the NVIDIA GeForce RTX 5070 (12 GB). What else fits in 12 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 5070, 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
FP162.34 GB1.64 GB – 2.36 GBsize range
Q8_01.25 GBexactpublished data
Q6_K0.96 GBexactpublished data
Q5_K_M0.84 GBexactpublished data
Q5_00.82 GB0.56 GB – 1.29 GBsize range
Q4_K_M0.73 GBexactpublished data
Q4_00.68 GB0.46 GB – 1.29 GBsize range
Q3_K_M0.59 GB0.35 GB – 1.29 GBsize range
Q2_K0.46 GB0.27 GB – 1.29 GBsize range

4 of these are published files. Open the pinned GGUF repository ↗

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 · 18 of 18 devices hold it entirely
DeviceMemoryVerdictDecodeCalculator
NVIDIA GeForce RTX 50701.63 GB of 12 GBfits~439 tok/sOpen →
AMD Radeon™ RX 9070 XT1.63 GB of 16 GBfits~479 tok/sOpen →
NVIDIA GeForce RTX 40801.63 GB of 16 GBfits~469 tok/sOpen →
NVIDIA GeForce RTX 50801.63 GB of 16 GBfits~627 tok/sOpen →
NVIDIA GeForce RTX 5070 Ti1.63 GB of 16 GBfits~586 tok/sOpen →
NVIDIA GeForce RTX 5060 Ti 16GB1.63 GB of 16 GBfits~293 tok/sOpen →
NVIDIA GeForce RTX 40901.63 GB of 24 GBfits~659 tok/sOpen →
AMD Radeon™ RX 7900 XTX1.63 GB of 24 GBfits~719 tok/sOpen →
NVIDIA GeForce RTX 30901.63 GB of 24 GBfits~612 tok/sOpen →
NVIDIA GeForce RTX 50901.63 GB of 32 GBfits~1171 tok/sOpen →
NVIDIA RTX 6000 Ada Generation1.63 GB of 48 GBfits~627 tok/sOpen →
Apple M4 Pro1.63 GB of 64 GBfits~103 tok/sOpen →
Apple M5 Pro1.63 GB of 64 GBfits~107 tok/sOpen →
AMD Ryzen AI Max+ 395 with Radeon 8060S1.63 GB of 128 GBfits~192 tok/sOpen →
Apple M3 Max1.63 GB of 128 GBfits~118 tok/sOpen →
Apple M4 Max1.63 GB of 128 GBfits~130 tok/sOpen →
NVIDIA DGX Spark1.63 GB of 128 GBfits~178 tok/sOpen →
Apple M2 Ultra1.63 GB of 192 GBfits~141 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 LFM2.5-1.2B-Instruct

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
28 configurations in stock · 5 out of stock
MachineSpeedPer hourPer M tokensWhereRent
1× RTX 306012 GBrecommendedcheapestbest value~235 tok/sFaster than you read$0.036$0.042Vast.aiMarketplace · 99.8% reliableRent on Vast.ai
1× RTX 308010 GB~496 tok/sFaster than you read$0.082$0.046Vast.aiMarketplace · 98.3% reliableRent on Vast.ai
1× RTX 30708 GB~292 tok/sFaster than you read$0.082$0.078Vast.aiMarketplace · 99.1% reliableRunPod $0.13/h Rent on Vast.ai
1× RTX 4060 Ti 16GB16 GB~188 tok/sFaster than you read$0.090$0.13Vast.aiMarketplace · 99.4% reliableRent on Vast.ai
1× RTX 407012 GB~329 tok/sFaster than you read$0.096$0.081Vast.aiMarketplace · 99.8% reliableRent on Vast.ai
1× RTX 309024 GB~611 tok/sFaster than you read$0.12$0.055Vast.aiMarketplace · 99.5% reliableRunPod $0.50/h Rent on Vast.ai
1× RTX 5060 Ti 16GB16 GB~292 tok/sFaster than you read$0.12$0.12Vast.aiMarketplace · 99.1% reliableRent on Vast.ai
1× RTX 3080 Ti12 GB~596 tok/sFaster than you read$0.14$0.063Vast.aiMarketplace · 99.9% reliableRent 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 Q4_K_M on it (llama.cpp)
Container image
ghcr.io/ggml-org/llama.cpp:server-cuda
Start command / arguments
-hf LiquidAI/LFM2.5-1.2B-Instruct-GGUF:Q4_K_M -c 8192 -ngl 999 --host 0.0.0.0 --port 8080
Expose port
8080 — an OpenAI-compatible API at /v1

Starting from a saved llama.cpp template instead? Leave the arguments empty and set these variables — only the first two change between models:

LLAMA_ARG_HF_REPO=LiquidAI/LFM2.5-1.2B-Instruct-GGUF:Q4_K_M
LLAMA_ARG_CTX_SIZE=8192
LLAMA_ARG_N_GPU_LAYERS=999
LLAMA_ARG_HOST=0.0.0.0
LLAMA_ARG_PORT=8080

Already have the runtime? llama-server -hf LiquidAI/LFM2.5-1.2B-Instruct-GGUF:Q4_K_M -c 8192 -ngl 999 --host 0.0.0.0 --port 8080. Weights: LiquidAI/LFM2.5-1.2B-Instruct-GGUF. The CUDA image is for NVIDIA cards; an AMD machine needs llama.cpp's ROCm build.

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 LFM2.5-1.2B-Instruct 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 0f604ada3f76; parameter count from the safetensors index at the same revision.

Architecture
lfm2
Layers
16
Hidden size
2,048
Attention heads
32
KV heads
8
Feed-forward width
12,288
Vocabulary
65,536
Context ceiling
128,000
RoPE theta
1,000,000

Where the memory goes

tokenembedding× 16 decoder blocksGrouped-query attention32 query · 8 KV headsfull contextFeed-forwardone networkall activeoutputprojectiongrows with contextfixed per token

Grouped-query attention shares each key/value head across 4 query heads, so the KV cache is 25% of what multi-head attention would need at the same context.

Open the exact official config.json ↗

More from LiquidAI

LFM2.5-2.6BLFM2.5-230MLFM2.5-350MLFM2.5-8B-A1BLFM2.5-VL-3B

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":"liquidai-lfm2-5-1-2b-instruct","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="liquidai-lfm2-5-1-2b-instruct" data-quantization="Q4_K_M"
  data-hardware="nvidia-geforce-rtx-4090" data-context="8192"></script>

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Citing this page

LLM Bottleneck. “LFM2.5-1.2B-Instruct VRAM and hardware requirements.” Architecture from LiquidAI/LFM2.5-1.2B-Instruct at revision 0f604ada3f76, retrieved 2026-09-01. https://llmbottleneck.com/models/liquidai-lfm2-5-1-2b-instruct

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