The LPDDR5 memory is listed with a 16GB maximum; no RAM upgrade is assumed.
Listing evidence: 2026-09-25. Amazon.com base price visible in an owner-supplied screenshot reviewed 25 Sep 2026; capture time not supplied. Dated reference, not a live quote. Excludes card signup offers, coupons, tax and shipping.
Fixed 24GB memory and internal storage limit model size; no CUDA support; Amazon main-offer price and stock were not verified in this run.
Listing evidence: 2026-10-03. Apple US launch price announced 22 September 2026: USD 1,699. Dated manufacturer reference, not an Amazon offer; tax and shipping may differ.
Memory is soldered and shared with the operating system.
Listing evidence: 2026-09-24. GMKtec, EVO-X2 / 64GB / 1TB, recorded 22 Sep 2026; exact ASIN capacity identified 24 Sep. This is a dated reference, not a current Amazon price; tax, shipping and bundle contents may differ.
Weight size is not tested compatibility or speed. Check runtime support and memory for context before buying. Prices are dated references. As an Amazon Associate I earn from qualifying purchases.
How these options are selected
Up to three platforms, with the lowest dated reference price on each. Unknown prices follow known prices. Each memory pool keeps a reserve: 30% (minimum 2 GB) for system or unified memory; 20% (minimum 1 GB) for dedicated VRAM. System RAM is for CPU inference. This check does not establish GPU allocation, software support or context capacity.
Model file and details
Provider
Liquid AI
Total parameters
2,166,928,384
Exact file size
1,099,630,080 bytes
Quantization / precision
Q4_K_M
Weight repository
LiquidAI/LFM3-Omni-2B-GGUF
Original model repository
LiquidAI/LFM3-Omni-2B
Weight revision
2918dd47cfceb4f260cd1734cd1aac1b22bbe4b6
Model revision
ce2458cbce4ad970f92032ed94e3d18d30b04f9c
Runtime notes
Confirm model architecture and quantization support in your runner.
Recorded release date
2026-10-27
Date evidence
Liquid AI LFM3 family announcement dated 27 October 2026; official source and GGUF model cards reviewed on 28 October. Exact source parameter count includes modality encoders; the language GGUF metadata count excludes separate encoders.
These scores were compiled from the linked sources. These published scores were not independently tested here. Different prompts, precision, thinking budgets and tools can change results.
Daily-Omni · reported
70.09 / 100
Publisher-reported multiple-choice accuracy for videos with audio. This is an audiovisual understanding task, not general chat quality.
Benchmark efficiency: 63.74 published score points per GB of selected weights. This ratio is not an inference-speed measure; check the test settings to see whether the score covers the selected quantization.
Test settings: Liquid AI Daily-Omni video-with-soundtrack multiple-choice accuracy. Each model uses its own chat template, prompts and media preprocessing; text output uses greedy decoding. Not a text-only or silent-video score.
Publisher source-checkpoint result, not an independent evaluation or a measurement of the selected quantized file. No repeated-run confidence interval is supplied. File size covers language weights only; it is not runtime RAM. Allow additional memory for context, runtime and media. LFM Open License v1.0 applies; commercial use at or above the defined USD 10 million annual-revenue threshold needs a separate license. The separate Q8_0 projector is 540,892,768 bytes; language plus projector is 1,640,522,848 bytes before runtime/context overhead. Projector: https://huggingface.co/LiquidAI/LFM3-Omni-2B-GGUF/blob/2918dd47cfceb4f260cd1734cd1aac1b22bbe4b6/mmproj-LFM3-Omni-2B-Q8_0.gguf. Text, images, video and audio input; text/speech output. Non-reasoning; 32,768-token source limit. Current official GGUF card supports video in llama.cpp b11960/v0.8.0 with ffmpeg; LM Studio is image-only and Ollama support differs. Use --reasoning off in llama serve.
Context and version evidence: The repository and file name identify the selected model. Revision hashes are shown above when recorded. Tested context length, full hardware configuration and run date are not consistently recorded in this snapshot. Only settings explicitly stated in each benchmark record are known.
The 1.1 GB figure covers model weights in this file. It excludes the context cache, runtime, operating system and any separate multimodal files. It is not a measured RAM requirement or a token-generation speed.
Confirm model architecture and quantization support in your runner. The selected weights use 1.1 GB; this excludes context cache, runtime buffers and other programs.
These are the first listed tiers that pass our reserve policy, not measured minimum requirements. System RAM and VRAM remain separate. Use the memory tool to change the reserve for your context and runner.