Liquid AI·LFM2.5·Lfm2DSparkDraftModel

LFM2.5 2.6B DSpark — Hardware Requirements & GPU Compatibility

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LFM2.5 2.6B DSpark is a 328M-parameter open language model from Liquid AI in the LFM2.5 family. It supports a context window of up to 128,000 tokens. At Q4_K_M it needs about 0.52 GB of VRAM — see which GPUs and Macs can run it below.

3.2K downloads 35 likes 77.9K quant downloads128K context
Based on LFM2.5 2.6B

Specifications

Publisher
Liquid AI
Family
LFM2.5
Parameters
328M
Architecture
Lfm2DSparkDraftModel
Context Length
128,000 tokens
Vocabulary Size
128,000
Release Date
2026-08-10
License
Other

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How Much VRAM Does LFM2.5 2.6B DSpark Need?

Select a quantization to see compatible GPUs below.

QuantizationBitsVRAM
Q2_Kest.3.400.5 GB
Q3_K_Mest.3.900.5 GB
Q4_K_M4.800.5 GB
Q5_K_Mest.5.700.6 GB
Q6_Kest.6.600.6 GB
Q8_08.000.7 GB
BF16est.16.001.0 GB

est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.

Which GPUs Can Run LFM2.5 2.6B DSpark?

Q4_K_M · 0.5 GB

LFM2.5 2.6B DSpark (Q4_K_M) requires 0.5 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 1+ GB is recommended. Using the full 128K context window can add up to 1.3 GB, bringing total usage to 1.8 GB. 50 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.

Runs great

Plenty of headroom
NVIDIA GeForce RTX 5090~2240 tok/sNVIDIA GeForce RTX 3090 Ti~1260 tok/sNVIDIA GeForce RTX 4090~1260 tok/sNVIDIA GeForce RTX 5080~1200 tok/sNVIDIA GeForce RTX 3090~1170 tok/sNVIDIA GeForce RTX 3080 Ti~1141 tok/sNVIDIA GeForce RTX 5070 Ti~1120 tok/sNVIDIA GeForce RTX 5090 Laptop GPU~1120 tok/sAMD Radeon RX 7900 XTX~1108 tok/sNVIDIA GeForce RTX 3080~950 tok/sAMD Radeon RX 7900 XT~923 tok/sNVIDIA GeForce RTX 4080 SUPER~920 tok/sNVIDIA GeForce RTX 4080~896 tok/sNVIDIA GeForce RTX 4070 Ti SUPER~840 tok/sNVIDIA GeForce RTX 5070~840 tok/sNVIDIA TITAN RTX~840 tok/sNVIDIA GeForce RTX 2080 Ti~770 tok/sNVIDIA GeForce RTX 3070 Ti~760 tok/sAMD Radeon RX 9070~739 tok/sAMD Radeon RX 9070 XT~739 tok/sAMD Radeon RX 7800 XT~720 tok/sNVIDIA GeForce RTX 4090 Laptop GPU~720 tok/sAMD Radeon RX 7900 GRE~665 tok/sNVIDIA GeForce RTX 4070~630 tok/sNVIDIA GeForce RTX 4070 SUPER~630 tok/sNVIDIA GeForce RTX 4070 Ti~630 tok/sNVIDIA GeForce GTX 1080 Ti~606 tok/sAMD Radeon RX 6800~591 tok/sAMD Radeon RX 6800 XT~591 tok/sAMD Radeon RX 6900 XT~591 tok/sNVIDIA GeForce RTX 3060 Ti~560 tok/sNVIDIA GeForce RTX 3070~560 tok/sNVIDIA GeForce RTX 5060~560 tok/sNVIDIA GeForce RTX 5060 Ti 16GB~560 tok/sNVIDIA GeForce RTX 5060 Ti 8GB~560 tok/sIntel Arc A770 16GB~539 tok/sAMD Radeon RX 7700 XT~499 tok/sIntel Arc A750~492 tok/sNVIDIA GeForce RTX 3060 12GB~450 tok/sAMD Radeon RX 6700 XT~443 tok/sIntel Arc B580~439 tok/sAMD Radeon RX 9060 XT 16GB~369 tok/sIntel Arc B570~365 tok/sNVIDIA GeForce RTX 4060 Ti 16GB~360 tok/sNVIDIA GeForce RTX 4060 Ti 8GB~360 tok/sNVIDIA GeForce RTX 4060~340 tok/sAMD Radeon RX 7600~332 tok/sAMD Radeon RX 7600 XT~332 tok/sNVIDIA GeForce RTX 3060 8GB~300 tok/sNVIDIA GeForce RTX 3050 8GB~280 tok/s

Which Devices Can Run LFM2.5 2.6B DSpark?

Q4_K_M · 0.5 GB

59 devices with unified memory can run LFM2.5 2.6B DSpark, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.

Runs great

Plenty of headroom
NVIDIA DGX H100~33500 tok/sNVIDIA DGX A100 640GB~20390 tok/sMac Studio (M3 Ultra, 256GB)~1103 tok/sMac Studio (M3 Ultra, 512GB)~1103 tok/sMac Studio (M3 Ultra, 96GB)~1103 tok/sMac Pro M2 Ultra (192 GB)~1077 tok/sMac Studio M2 Ultra (192 GB)~1077 tok/sMacBook Pro 16" M5 Max (128 GB)~827 tok/sMac Studio M4 Max (128 GB)~735 tok/sMac Studio M4 Max (64 GB)~735 tok/sMacBook Pro 16" M4 Max (48 GB)~735 tok/sMacBook Pro 16" M4 Max (64 GB)~735 tok/sMac Studio M4 Max (36 GB)~551 tok/sMacBook Pro 14" M4 Max (36 GB)~551 tok/sMacBook Pro 16" M3 Max (48 GB)~551 tok/sMacBook Pro 14-inch (M5 Pro)~413 tok/sMac Mini M4 Pro (24 GB)~368 tok/sMac Mini M4 Pro (48 GB)~368 tok/sMacBook Pro 14" M4 Pro (24 GB)~368 tok/sMacBook Pro 16" M4 Pro (24 GB)~368 tok/sASUS Ascent GX10~341 tok/sNVIDIA DGX Spark~341 tok/sNVIDIA Jetson AGX Thor Developer Kit~341 tok/sAsus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB)~320 tok/sBeelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB)~320 tok/sFramework Desktop (Ryzen AI Max+ 395, 128 GB)~320 tok/sGMKtec EVO-X2 (Ryzen AI Max+ 395, 128 GB)~320 tok/sHP Z2 Mini G1a (Ryzen AI Max+ PRO 395, 128 GB)~320 tok/sHP ZBook Ultra G1a 14 (Ryzen AI Max+ PRO 395, 128 GB)~320 tok/sMinisforum MS-S1 MAX (Ryzen AI Max+ 395, 128 GB)~320 tok/sSnapdragon X2 Elite Extreme Copilot+ PC~285 tok/sNVIDIA Jetson AGX Orin 32GB~256 tok/sNVIDIA Jetson AGX Orin 64GB~256 tok/sMacBook Pro 14-inch (M5)~207 tok/siPad Pro M5 13" (16 GB)~206 tok/sSnapdragon X Elite Copilot+ PC~169 tok/sMac Mini M4 (16 GB)~162 tok/sMac Mini M4 (32 GB)~162 tok/sMacBook Air 13" M4 (16 GB)~162 tok/sMacBook Air 13" M4 (24 GB)~162 tok/sMacBook Air 15" M4 (16 GB)~162 tok/sMacBook Air 15" M4 (24 GB)~162 tok/sMacBook Pro 14" M4 (16 GB)~162 tok/siPad Pro M4 13" (16 GB)~162 tok/sAMD Ryzen AI 9 HX 370 (Strix Point) Laptop~139 tok/sMacBook Air 13" M3 (16 GB)~138 tok/sMacBook Air 13" M3 (24 GB)~138 tok/sMacBook Air 13" M3 (8 GB)~138 tok/sIntel Core Ultra 9 288V (Lunar Lake) Laptop~131 tok/sNVIDIA Jetson Orin NX 16GB~128 tok/sNVIDIA Jetson Orin Nano 8GB (Super)~128 tok/sApple iPhone 17 Pro~103 tok/siPhone 17 Pro Max~103 tok/siPhone 17~92 tok/siPhone Air~92 tok/siPhone 15 ProiPhone 15 Pro MaxiPhone 16 ProiPhone 16 Pro Max

Where to Download LFM2.5 2.6B DSpark

Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.

Related Models

Frequently Asked Questions

How much VRAM does LFM2.5 2.6B DSpark need?

LFM2.5 2.6B DSpark requires 0.5 GB of VRAM at Q4_K_M, or 1.0 GB at BF16. Full 128K context adds up to 1.3 GB (1.8 GB total).

VRAM = Weights + KV Cache + Overhead

Weights = 328M × 4.8 bits ÷ 8 = 0.2 GB

KV Cache + Overhead 0.3 GB (at 2K context + ~0.3 GB framework)

KV Cache + Overhead 1.6 GB (at full 128K context)

VRAM usage by quantization

0.5 GB
1.8 GB

Learn more about VRAM estimation →

What's the best quantization for LFM2.5 2.6B DSpark?

For LFM2.5 2.6B DSpark, Q4_K_M (0.5 GB) offers the best balance of quality and VRAM usage. Q5_K_M (0.6 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 0.5 GB.

VRAM requirement by quantization

Q2_K
0.5 GB
Q4_K_M
0.5 GB
Q5_K_M
0.6 GB
Q6_K
0.6 GB
Q8_0
0.7 GB
BF16
1.0 GB

★ Recommended — best balance of quality and VRAM usage.

Learn more about quantization →

Can I run LFM2.5 2.6B DSpark on a Mac?

LFM2.5 2.6B DSpark requires at least 0.5 GB at Q2_K, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.

Can I run LFM2.5 2.6B DSpark locally?

Yes — LFM2.5 2.6B DSpark can run locally on consumer hardware. At Q4_K_M quantization it needs 0.5 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.

How fast is LFM2.5 2.6B DSpark?

At Q4_K_M, LFM2.5 2.6B DSpark can reach ~9231 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~1260 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.

tok/s = (bandwidth GB/s ÷ model GB) × efficiency

Example: NVIDIA B2008000 ÷ 0.5 × 0.65 = ~10000 tok/s

Estimated speed at Q4_K_M (0.5 GB)

~10000 tok/s
~1260 tok/s
~10000 tok/s
~9231 tok/s

Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.

Learn more about tok/s estimation →

What's the download size of LFM2.5 2.6B DSpark?

At Q4_K_M, the download is about 0.20 GB. The full-precision BF16 version is 0.66 GB. The smallest option (Q2_K) is 0.14 GB.

Which GPUs can run LFM2.5 2.6B DSpark?

50 consumer GPUs can run LFM2.5 2.6B DSpark at Q4_K_M (0.5 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 50 GPUs have plenty of headroom for comfortable inference.

Which devices can run LFM2.5 2.6B DSpark?

59 devices with unified memory can run LFM2.5 2.6B DSpark at Q4_K_M (0.5 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.