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.
82.4K downloads
21 likesSpecifications
- 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
Get Started
HuggingFace
How Much VRAM Does MobileLLaMA 1.4B Chat Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 1.3 GB | — | 0.59 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 1.4 GB | — | 0.68 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 1.5 GB | — | 0.84 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 1.7 GB | — | 1.00 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 1.9 GB | — | 1.16 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 2.1 GB | — | 1.40 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 3.5 GB | — | 2.80 GB | Brain floating point 16 — preferred for training |
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 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. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run MobileLLaMA 1.4B Chat?
Q4_K_M · 1.5 GB33 devices with unified memory can run MobileLLaMA 1.4B Chat, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
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)
VRAM usage by quantization
Q4_K_M1.5 GB- 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
Q2_K1.3 GBQ4_K_M ★1.5 GBQ5_K_M1.7 GBQ6_K1.9 GBQ8_02.1 GBBF163.5 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run MobileLLaMA 1.4B Chat on a Mac?
MobileLLaMA 1.4B Chat requires at least 1.3 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 MobileLLaMA 1.4B Chat locally?
Yes — MobileLLaMA 1.4B Chat can run locally on consumer hardware. At Q4_K_M quantization it needs 1.5 GB of VRAM. 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 ~1893 tok/s on AMD Instinct MI300X. 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: AMD Instinct MI300X → 5300 ÷ 1.5 × 0.55 = ~1893 tok/s
Estimated speed at Q4_K_M (1.5 GB)
~1893 tok/s~426 tok/s~1415 tok/s~1170 tok/sReal-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?
35 consumer GPUs can run MobileLLaMA 1.4B Chat at Q4_K_M (1.5 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 35 GPUs have plenty of headroom for comfortable inference.
- Which devices can run MobileLLaMA 1.4B Chat?
33 devices with unified memory can run MobileLLaMA 1.4B Chat at Q4_K_M (1.5 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.