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Liquid AI · MODEL RECORD

d1-2B

d1-2B has 1.918 billion total parameters. The selected Q4_K_M GGUF file is 1.1 GB.

Data snapshot: . 1 published test in this catalog.

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Computers for d1-2B

These configurations pass a weight-size check for the 1.10 GB file. Compare the platforms, then check the exact Amazon listing.

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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
1,918,181,888
Exact file size
1,099,629,888 bytes
Quantization / precision
Q4_K_M
Weight repository
LiquidAI/d1-2B-GGUF
Original model repository
LiquidAI/d1-2B
Weight revision
e968f6575f0d4fad928312d7af15cad90fd365c0
Model revision
cfe5760ff5d5998f91d9ac56bd4b2dbd96996d41
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.

d1-2B-Q4_K_M.gguf

GGUF file and download on Hugging Face · Original model card

Published benchmark results

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.

Decision Index 0.3 · LFM3 public suite

51.1 / 100

Liquid AI LFM3 release evaluation of the Decision Index v0.3 public suite, rescored on common items. Separate from prior release scores with different item/scoring details.

Benchmark efficiency: 46.47 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 Decision Index public suite v0.3. The release card rescores comparison models on the same items with the same scoring and warns that older card results can differ. Exact evaluator commit, request count and precision for this score are not supplied. Not the mixed benchmark Average row.

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 203,031,584 bytes; language plus projector is 1,302,661,472 bytes before runtime/context overhead. Projector: https://huggingface.co/LiquidAI/d1-2B-GGUF/blob/e968f6575f0d4fad928312d7af15cad90fd365c0/mmproj-d1-2B-Q8_0.gguf. Decision/classification model: returns typed answers in one forward pass without generating chat tokens. 131,072-token source limit. llama.cpp b11960/v0.8.0 supports /v1/systemone noul, choice and score questions; extraction, estimates, boxes and multiple:true are not yet supported there. Batch settings must hold the full state and media. Images and video are supported; video requires ffmpeg.

Read the Decision Index 0.3 · LFM3 public suite source

Memory and comparison limits

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.

Use the model ranking chart to compare one test at a time. Use the 8, 16 and 32 GB memory guide. Check hardware memory and bandwidth before choosing a model.

RAM and VRAM planning for d1-2B

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.

System RAM (CPU inference)
4 GB planning tier · 2 GB initial weight budget
Dedicated VRAM
4 GB planning tier · 3 GB initial weight budget
Unified memory
4 GB planning tier · 2 GB initial weight budget

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.

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