LFM2.5 230M Base — Hardware Requirements & GPU Compatibility
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LFM2.5 230M Base is a 230M-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 0.50 GB of VRAM — see which GPUs and Macs can run it below.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 125K)Best on 24 GB (Q4_K_M · ~1310 tok/s · 125K)Macs from 8 GB (Q4_K_M · ~143 tok/s)
Other
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run LFM2.5 230M Base
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve LiquidAI/LFM2.5-230M-Base --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Other ways
each opens step-by-stepRun in cloud
Fits on RTX 3060 12GB (11 GB headroom) · Q4_K_M
- Generation speed
- ~468 tok/s
- generation speed
- Cost per 1M output tokens
- $0.042
- per 1M output tokens
How Much VRAM Does LFM2.5 230M Base Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 0.5 GB | 0.8 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 125K context · ~1424 tok/s | |
| 0.5 GB | 0.8 GB | 3-bit medium — 3-bit medium quantization | Fits: 125K context · ~1394 tok/s | |
| 0.5 GB | 0.8 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 125K context · ~1310 tok/s | |
| 0.5 GB | 0.9 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 125K context · ~1260 tok/s | |
| 0.6 GB | 0.9 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 125K context · ~1191 tok/s | |
| 0.6 GB | 0.9 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 125K context · ~1111 tok/s | |
| 0.8 GB | 1.2 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 125K context · ~799 tok/s |
Selected Q4_K_M
4-bit medium quantization0.6GBat 16K context
- Weights
- 0.1 GB
- KV cache
- 0.2 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 23.4 GB free · ~1310 tok/s
≥50 tok/s needs ≥38 GB/s · RTX 5090 ~2330, RTX 3090 Ti ~1310
Which Hardware Can Run LFM2.5 230M Base?
Q4_K_M · 0.5 GBLFM2.5 230M Base (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 1.5 GB, bringing total usage to 2.0 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run LFM2.5 230M Base, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 7.4 GB free · up to 125K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~988Simulate output on RTX 3080RTX 2080 Ti~801Simulate output on RTX 2080 TiRTX 3070 Ti~791Simulate output on RTX 3070 TiM3*8 GB~143Simulate output on iMac 24" M3 (8 GB)Orin Nano~133Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~96Simulate output on iPhone 1720 GB
fits · 19.4 GB free · up to 125K ctx
1 GPU
RX 7900 XT~960Simulate output on RX 7900 XT32–40 GB
fits · 31.4 GB free · up to 125K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~2330Simulate output on RTX 5090M5 Max36 GB~644Simulate output on Mac Studio M5 Max (36 GB)M532 GB~215Simulate output on MacBook Air 13" M5 (32 GB)Orin~266Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~176Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to LFM2.5 230M Base
LFM2.5 family · 15 models →Similar models
2| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Tiny MixtralTitanML | — | 0.5 GB | ~2329 | same size |
| T5 Paraphrase PawsVamsi | — | 0.2 GB | ~4368 | same size |
Fine-tunes & variants(1)
Specifications
- Publisher
- Liquid AI
- Family
- LFM2.5
- Parameters
- 230M
- Architecture
- Lfm2ForCausalLM
- Context Length
- 128,000 tokens
- Vocabulary Size
- 65,536
- Release Date
- 2026-06-16
- License
- Other
Frequently Asked Questions
How much VRAM does LFM2.5 230M Base need?
LFM2.5 230M Base requires 0.5 GB of VRAM at Q4_K_M, or 0.8 GB at BF16. Full 128K context adds up to 1.5 GB (2.0 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 230M × 4.8 bits ÷ 8 = 0.1 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 1.9 GB (at full 128K context)
VRAM usage by quantization
What's the best quantization for LFM2.5 230M Base?
For LFM2.5 230M Base, Q4_K_M (0.5 GB) offers the best balance of quality and VRAM usage. Q5_K_M (0.5 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 0.5 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run LFM2.5 230M Base on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run LFM2.5 230M Base. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 0.5 GB of usable unified memory (RAM minus macOS overhead).
Can I run LFM2.5 230M Base locally?
Yes — LFM2.5 230M Base can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is LFM2.5 230M Base?
At Q4_K_M, LFM2.5 230M Base can reach ~9600 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~1310 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 B200 → 8000 ÷ 0.5 × 0.65 = ~10400 tok/s
Estimated speed at Q4_K_M (0.5 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of LFM2.5 230M Base?
At Q4_K_M, the download is about 0.14 GB. The full-precision BF16 version is 0.46 GB. The smallest option (Q2_K) is 0.10 GB.
Which GPUs can run LFM2.5 230M Base?
52 consumer GPUs can run LFM2.5 230M Base at Q4_K_M. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 52 GPUs have plenty of headroom for comfortable inference.
Which devices can run LFM2.5 230M Base?
163 devices with unified memory can run LFM2.5 230M Base at Q4_K_M (0.5 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (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.