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Can the Apple M1 (8 GB) run LFM2 700M?

18Basic quality (D) · 18 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 56/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs great

Runs great. At Q8_0 it needs about 1.9 GB of 5.4 GB usable, with an estimated speed of 49.6 tok/s.

Best quant
Q8_0
Generation speed
49.6 tok/s
Prompt processing
2,178 tok/s
First token (1K prompt)
0.5 s
Largest safe context
125K

Best app to run it on the Apple M1 (8 GB)

On macOS

LM Studio

Desktop app · runs on llama.cpp, MLX · use the MLX build

  • Runs the MLX build, typically 35–90% faster than GGUF for a model with 0.7B active parameters on Apple silicon.
  • Point-and-click: search, download and chat in one window.

Open LFM2 700M in LM Studio

  • MLX LM Runs the MLX build, typically 35–90% faster than GGUF for a model with 0.7B active parameters on Apple silicon.
  • Unsloth Desktop Runs the MLX build, typically 35–90% faster than GGUF for a model with 0.7B active parameters on Apple silicon.

How we choose: the files published for this model, your operating system and GPU, whether it fits fully in memory, and published speed tests (MLX vs llama.cpp on Apple silicon, vLLM vs llama.cpp vs Ollama on NVIDIA). Details.

Download LFM2 700M

Recommended for the Apple M1 (8 GB): Q8_0. Every other quantization has its own download link in the table below.

Quantized files · GGUF

Works in LM Studio, llama.cpp, Jan and Ollama. No sign-in needed.

Download the Q8_0 file (opens in a new tab)

huggingface.co/LiquidAI/LFM2-700M-GGUF (opens in a new tab)

ollama run hf.co/LiquidAI/LFM2-700M-GGUF:Q8_0

Original weights · Hugging Face

Full-precision files from Liquid AI, for fine-tuning, vLLM, Transformers.

huggingface.co/LiquidAI/LFM2-700M (opens in a new tab)

No sign-in needed.

Links checked 2 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 18Basic quality (D) · 18 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 56/100 (quality, fit, speed and memory use) sets the ranking.
How capable the model itself is at Q8_0, from its editorial quality tier scaled by the quantization or precision. It’s the same on any hardware that runs this variant. Scale: A+ 85+ · A 70+ · B 55+ · C 40+ · D under 40.
Runs Runs great
How well it runs on the Apple M1 (8 GB): speed, capped by how well it fits in memory. Runs great: 30+ tokens/s (or near the image, video or decision speed target) · Runs well: 10+ tokens/s · Slow: 3+ tokens/s · Very slow: under 3 · Won’t run: doesn’t fit even with offload. A tight fit or RAM offload caps it at Runs well; heavy offload caps it at Slow.

Match score

To order lists, each result also gets a match score out of 100 from four parts. It favors the most capable model that still runs well.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.8.1 / 45
Memory fitFull marks when the model fits in the share of unified memory the GPU can use; fewer when it goes past that limit.25.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.15.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.8.2 / 15
Match score56.3 / 100

The match score orders results; it is not shown as a grade. Quality and Runs are the two ratings in the tables.

Every quantization on the Apple M1 (8 GB)

Quant Memory needed Available Speed Runs Quality Download
Q2_K 1.4 GB 5.4 GB 160 tok/s Runs great 12Basic quality (D) · 12 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 51/100 (quality, fit, speed and memory use) sets the ranking. GGUF files (opens in a new tab)
Q3_K_M 1.5 GB 5.4 GB 126 tok/s Runs great 15Basic quality (D) · 15 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 53/100 (quality, fit, speed and memory use) sets the ranking. GGUF files (opens in a new tab)
Q4_K_M 1.6 GB 5.4 GB 97.5 tok/s Runs great 17Basic quality (D) · 17 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 54/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q5_K_M 1.7 GB 5.4 GB 80.5 tok/s Runs great 17Basic quality (D) · 17 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 54/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q6_K 1.7 GB 5.4 GB 67.6 tok/s Runs great 17Basic quality (D) · 17 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 55/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q8_0 1.9 GB 5.4 GB 49.6 tok/s Runs great 18Basic quality (D) · 18 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 56/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
F16 2.6 GB 5.4 GB 22.9 tok/s Runs well 18Basic quality (D) · 18 of 100: how capable the model is at F16, whatever it runs on. Match score 55/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab) Original weights (opens in a new tab)

Memory figures assume 8K of context. Longer conversations or documents grow the KV cache.

Other hardware that runs it

Quick answers

How much memory does LFM2 700M need on the M1 8GB?

At Q8_0 it needs about 1.9 GB. The M1 8GB has 5.4 GB usable.

How fast is LFM2 700M on the M1 8GB?

About 49.6 tok/s at Q8_0, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of LFM2 700M should I use on the M1 8GB?

Q8_0. Of the versions that fit, it is the one that runs best; when two run equally well, the check prefers the higher-rated one.

Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.

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