reaperdoesntknow·Lfm2MoeForCausalLM

LFM2.5 8B A1B Opus Distil — Hardware Requirements & GPU Compatibility

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LFM2.5 8B A1B Opus Distil is a 8.5B-parameter open language model from reaperdoesntknow. It supports a context window of up to 128,000 tokens. At Q4_K_M it needs about 5.48 GB of VRAM — see which GPUs and Macs can run it below.

229 downloads 4 likes128K context
Based on LFM2.5 8B A1B

Specifications

Publisher
reaperdoesntknow
Parameters
8.5B
Architecture
Lfm2MoeForCausalLM
Context Length
128,000 tokens
Vocabulary Size
128,000
Release Date
2026-06-06
License
Apache 2.0

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How Much VRAM Does LFM2.5 8B A1B Opus Distil Need?

Select a quantization to see compatible GPUs below.

QuantizationBitsVRAM
Q2_K3.404 GB
Q3_K_M3.904.5 GB
Q4_04.004.6 GB
Q4_K_M4.805.5 GB
Q5_K_M5.706.4 GB
Q6_K6.607.4 GB
Q8_08.008.9 GB

Which GPUs Can Run LFM2.5 8B A1B Opus Distil?

Q4_K_M · 5.5 GB

LFM2.5 8B A1B Opus Distil (Q4_K_M) requires 5.5 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 8+ GB is recommended. Using the full 128K context window can add up to 6.2 GB, bringing total usage to 11.7 GB. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti.

Which Devices Can Run LFM2.5 8B A1B Opus Distil?

Q4_K_M · 5.5 GB

33 devices with unified memory can run LFM2.5 8B A1B Opus Distil, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Air 13" M3 (8 GB).

Related Models

Frequently Asked Questions

How much VRAM does LFM2.5 8B A1B Opus Distil need?

LFM2.5 8B A1B Opus Distil requires 5.5 GB of VRAM at Q4_K_M, or 8.9 GB at Q8_0. Full 128K context adds up to 6.2 GB (11.7 GB total).

VRAM = Weights + KV Cache + Overhead

Weights = 8.5B × 4.8 bits ÷ 8 = 5.1 GB

KV Cache + Overhead 0.4 GB (at 2K context + ~0.3 GB framework)

KV Cache + Overhead 6.6 GB (at full 128K context)

VRAM usage by quantization

5.5 GB
11.7 GB

Learn more about VRAM estimation →

What's the best quantization for LFM2.5 8B A1B Opus Distil?

For LFM2.5 8B A1B Opus Distil, Q4_K_M (5.5 GB) offers the best balance of quality and VRAM usage. Q5_K_S (6.2 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 2.7 GB.

VRAM requirement by quantization

IQ2_XXS
2.7 GB
IQ3_S
4.0 GB
IQ4_XS
5.0 GB
Q4_K_M
5.5 GB
Q5_K_S
6.2 GB
Q8_0
8.9 GB

★ Recommended — best balance of quality and VRAM usage.

Learn more about quantization →

Can I run LFM2.5 8B A1B Opus Distil on a Mac?

LFM2.5 8B A1B Opus Distil requires at least 2.7 GB at IQ2_XXS, 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 8B A1B Opus Distil locally?

Yes — LFM2.5 8B A1B Opus Distil can run locally on consumer hardware. At Q4_K_M quantization it needs 5.5 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.

How fast is LFM2.5 8B A1B Opus Distil?

At Q4_K_M, LFM2.5 8B A1B Opus Distil can reach ~532 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~120 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.

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

Example: AMD Instinct MI300X5300 ÷ 5.5 × 0.55 = ~532 tok/s

Estimated speed at Q4_K_M (5.5 GB)

~532 tok/s
~120 tok/s
~398 tok/s
~329 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 8B A1B Opus Distil?

At Q4_K_M, the download is about 5.08 GB. The full-precision Q8_0 version is 8.47 GB. The smallest option (IQ2_XXS) is 2.33 GB.

Which GPUs can run LFM2.5 8B A1B Opus Distil?

35 consumer GPUs can run LFM2.5 8B A1B Opus Distil at Q4_K_M (5.5 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 28 GPUs have plenty of headroom for comfortable inference.

Which devices can run LFM2.5 8B A1B Opus Distil?

33 devices with unified memory can run LFM2.5 8B A1B Opus Distil at Q4_K_M (5.5 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.