MobileLLaMA 1.4B Chat — Hardware Requirements & GPU Compatibility
Chat
MobileLLaMA 1.4B Chat is a 1.4B-parameter open language model from mtgv in the Llama family. It supports a context window of up to 2,048 tokens. At Q4_K_M it needs about 1.54 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 · 2K)Best on 24 GB (Q4_K_M · ~426 tok/s · 2K)Macs from 8 GB (Q4_K_M · ~47 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run MobileLLaMA 1.4B Chat
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
vllm serve mtgv/MobileLLaMA-1.4B-Chat --max-model-len 2048vLLM repo with its own quantisation
Download: Weights on Hugging Face
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on RTX 3080 Ti (12 GB) (10 GB headroom) · Q4_K_M
- Generation speed
- ~385 tok/s
- generation speed
- Cost per 1M output tokens
- $0.088
- per 1M output tokens
How Much VRAM Does MobileLLaMA 1.4B Chat Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 1.3 GB | — | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 2K context · ~504 tok/s | |
| 1.4 GB | — | 3-bit medium — 3-bit medium quantization | Fits: 2K context · ~471 tok/s | |
| 1.5 GB | — | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 2K context · ~426 tok/s | |
| 1.7 GB | — | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 2K context · ~385 tok/s | |
| 1.9 GB | — | 6-bit, very good — 6-bit quantization, very good quality | Fits: 2K context · ~352 tok/s | |
| 2.1 GB | — | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 2K context · ~312 tok/s | |
| 3.5 GB | — | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 2K context · ~187 tok/s |
Selected Q4_K_M
4-bit medium quantization1.5GBat 2K context
- Weights
- 0.8 GB
- KV cache
- 0.4 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 22.5 GB free · ~426 tok/s
≥50 tok/s needs ≥118 GB/s · RTX 5090 ~756, RTX 3090 Ti ~426
Which Hardware Can Run MobileLLaMA 1.4B Chat?
Q4_K_M · 1.5 GBMobileLLaMA 1.4B Chat (Q4_K_M) requires 1.5 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 3+ GB is recommended. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run MobileLLaMA 1.4B Chat, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 6.5 GB free · up to 2K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~321Simulate output on RTX 3080RTX 2080 Ti~260Simulate output on RTX 2080 TiRTX 3070 Ti~257Simulate output on RTX 3070 TiM3*8 GB~47Simulate output on iMac 24" M3 (8 GB)Orin Nano~43Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~31Simulate output on iPhone 1720 GB
fits · 18.5 GB free · up to 2K ctx
1 GPU
RX 7900 XT~312Simulate output on RX 7900 XT32–40 GB
fits · 30.5 GB free · up to 2K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~756Simulate output on RTX 5090M5 Max36 GB~209Simulate output on Mac Studio M5 Max (36 GB)M532 GB~70Simulate output on MacBook Air 13" M5 (32 GB)Orin~86Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~57Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to MobileLLaMA 1.4B Chat
Llama family · 20 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| MiniCPM V 4.6 ThinkingOpenBMB | — | 1.5 GB | ~580 | same size |
| Inkling Small DSpark PreviewRadixArk | — | 1.5 GB | ~555 | same size |
| Sweep Next Edit 1.5BXenova | — | 1.6 GB | ~551 | same size |
| Ouro 1.4BByteDance | — | 6.4 GB | ~184 | same size |
| OLMo 2 0425 1B InstructAllen AI | — | 1.7 GB | ~449 | same size |
| Granite 3.0 1B A400m InstructIBM | — | 1.3 GB | ~524 | same size |
Specifications
- Publisher
- mtgv
- Family
- Llama
- Parameters
- 1.4B
- Architecture
- LlamaForCausalLM
- Context Length
- 2,048 tokens
- Vocabulary Size
- 32,000
- Release Date
- 2023-12-29
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does MobileLLaMA 1.4B Chat need?
MobileLLaMA 1.4B Chat requires 1.5 GB of VRAM at Q4_K_M, or 3.5 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 1.4B × 4.8 bits ÷ 8 = 0.8 GB
KV Cache + Overhead ≈ 0.7 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.
VRAM usage by quantization
What's the best quantization for MobileLLaMA 1.4B Chat?
For MobileLLaMA 1.4B Chat, Q4_K_M (1.5 GB) offers the best balance of quality and VRAM usage. Q5_K_M (1.7 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 1.3 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run MobileLLaMA 1.4B Chat on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run MobileLLaMA 1.4B Chat. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 1.5 GB of usable unified memory (RAM minus macOS overhead).
Can I run MobileLLaMA 1.4B Chat locally?
Yes — MobileLLaMA 1.4B Chat can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is MobileLLaMA 1.4B Chat?
At Q4_K_M, MobileLLaMA 1.4B Chat can reach ~3117 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~426 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 ÷ 1.54 × 0.65 = ~3377 tok/s
Estimated speed at Q4_K_M (1.5 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of MobileLLaMA 1.4B Chat?
At Q4_K_M, the download is about 0.84 GB. The full-precision BF16 version is 2.80 GB. The smallest option (Q2_K) is 0.59 GB.
Which GPUs can run MobileLLaMA 1.4B Chat?
52 consumer GPUs can run MobileLLaMA 1.4B Chat 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 MobileLLaMA 1.4B Chat?
163 devices with unified memory can run MobileLLaMA 1.4B Chat at Q4_K_M (1.5 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.