LiquidAI / lfm2

LFM2.5-230M

Name: LFM2.5-230M architecture and memory requirements
Creator: LLM Bottleneck
License: https://creativecommons.org/licenses/by/4.0/

229.7 million parameters.

Open in the calculator →

Architecturepublished data

Quick answer

LFM2.5-230M needs about 1.05 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.

Smallest device that fitsNVIDIA GeForce RTX 507012 GB · 672 GB/s~1844 tok/s Faster than you readOpen in the calculator →
Fastest in this listNVIDIA GeForce RTX 509032 GB · 1792 GB/s~4918 tok/s Faster than you readOpen in the calculator →

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

every format, with its evidence

Format	Size	Range	Basis
FP16	0.46 GB	0.32 GB – 0.47 GB	size range
Q8_0	0.25 GB	exact	published data
Q6_K	0.19 GB	exact	published data
Q5_K_M	0.17 GB	exact	published data
Q5_0	0.16 GB	0.11 GB – 0.35 GB	size range
Q4_K_M	0.15 GB	exact	published data
Q4_0	0.13 GB	0.09 GB – 0.35 GB	size range
Q3_K_M	0.11 GB	0.07 GB – 0.35 GB	size range
Q2_K	0.09 GB	0.05 GB – 0.35 GB	size 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

Device	Memory	Verdict	Decode	Calculator
NVIDIA GeForce RTX 5070	1.05 GB of 12 GB	fits	~1844 tok/s	Open →
AMD Radeon™ RX 9070 XT	1.05 GB of 16 GB	fits	~2013 tok/s	Open →
NVIDIA GeForce RTX 4080	1.05 GB of 16 GB	fits	~1967 tok/s	Open →
NVIDIA GeForce RTX 5080	1.05 GB of 16 GB	fits	~2635 tok/s	Open →
NVIDIA GeForce RTX 5070 Ti	1.05 GB of 16 GB	fits	~2459 tok/s	Open →
NVIDIA GeForce RTX 5060 Ti 16GB	1.05 GB of 16 GB	fits	~1230 tok/s	Open →
NVIDIA GeForce RTX 4090	1.05 GB of 24 GB	fits	~2766 tok/s	Open →
AMD Radeon™ RX 7900 XTX	1.05 GB of 24 GB	fits	~3019 tok/s	Open →
NVIDIA GeForce RTX 3090	1.05 GB of 24 GB	fits	~2569 tok/s	Open →
NVIDIA GeForce RTX 5090	1.05 GB of 32 GB	fits	~4918 tok/s	Open →
NVIDIA RTX 6000 Ada Generation	1.05 GB of 48 GB	fits	~2635 tok/s	Open →
Apple M4 Pro	1.05 GB of 64 GB	fits	~150 tok/s	Open →
Apple M5 Pro	1.05 GB of 64 GB	fits	~153 tok/s	Open →
AMD Ryzen AI Max+ 395 with Radeon 8060S	1.05 GB of 128 GB	fits	~805 tok/s	Open →
Apple M3 Max	1.05 GB of 128 GB	fits	~158 tok/s	Open →
Apple M4 Max	1.05 GB of 128 GB	fits	~162 tok/s	Open →
NVIDIA DGX Spark	1.05 GB of 128 GB	fits	~749 tok/s	Open →
Apple M2 Ultra	1.05 GB of 192 GB	fits	~166 tok/s	Open →

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 LFM2.5-230M

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

Machine	Speed	Per hour	Per M tokens	Where	Rent
1× RTX 306012 GBrecommendedcheapestbest value	~988 tok/sFaster than you read	$0.036	$0.010	Vast.aiMarketplace · 99.8% reliable	Rent on Vast.ai
1× RTX 308010 GB	~2085 tok/sFaster than you read	$0.082	$0.011	Vast.aiMarketplace · 98.3% reliable	Rent on Vast.ai
1× RTX 30708 GB	~1229 tok/sFaster than you read	$0.082	$0.019	Vast.aiMarketplace · 99.1% reliableRunPod $0.13/h	Rent on Vast.ai
1× RTX 4060 Ti 16GB16 GB	~790 tok/sFaster than you read	$0.090	$0.031	Vast.aiMarketplace · 99.4% reliable	Rent on Vast.ai
1× RTX 407012 GB	~1383 tok/sFaster than you read	$0.096	$0.019	Vast.aiMarketplace · 99.8% reliable	Rent on Vast.ai
1× RTX 309024 GB	~2568 tok/sFaster than you read	$0.12	$0.013	Vast.aiMarketplace · 99.5% reliableRunPod $0.50/h	Rent on Vast.ai
1× RTX 5060 Ti 16GB16 GB	~1229 tok/sFaster than you read	$0.12	$0.028	Vast.aiMarketplace · 99.1% reliable	Rent on Vast.ai
1× RTX 3080 Ti12 GB	~2503 tok/sFaster than you read	$0.14	$0.015	Vast.aiMarketplace · 99.9% reliable	Rent on Vast.ai

First time renting a GPU? How it works, in four steps

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.
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.
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.
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/LFM2.5-230M-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/LFM2.5-230M-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/LFM2.5-230M-GGUF:Q4_K_M -c 8192 -ngl 999 --host 0.0.0.0 --port 8080. Weights: LiquidAI/LFM2.5-230M-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 LFM2.5-230M 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.json · pinned 40cb2ad3b304

✓ Architecture read from the published config.json

Retrieved 2026-09-01 at pinned commit 40cb2ad3b304; parameter count from the safetensors index at the same revision.

Architecture: lfm2
Layers: 14
Hidden size: 1,024
Attention heads: 16
KV heads: 8
Feed-forward width: 2,560
Vocabulary: 65,536
Context ceiling: 128,000
RoPE theta: 1,000,000

Where the memory goes

Grouped-query attention shares each key/value head across 2 query heads, so the KV cache is 50% of what multi-head attention would need at the same context.

Open the exact official config.json ↗

More from LiquidAI

LFM2.5-1.2B-Instruct LFM2.5-2.6B LFM2.5-350M LFM2.5-8B-A1B LFM2.5-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-lfm2-5-230m","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-lfm2-5-230m" 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. “LFM2.5-230M VRAM and hardware requirements.” Architecture from LiquidAI/LFM2.5-230M at revision 40cb2ad3b304, retrieved 2026-09-01. https://llmbottleneck.com/models/liquidai-lfm2-5-230m

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