OpenELM 1 1B Instruct — Hardware Requirements & GPU Compatibility
Chat
OpenELM-1.1B-Instruct is Apple's 1.08-billion-parameter instruction-tuned language model, using a layer-wise scaling strategy that varies parameter allocation across layers for better accuracy per parameter. It's a small, general-purpose chat model for on-device and research use. Apple released it alongside 270M, 450M, and 3B siblings and, unusually for the company, published the full pretraining and fine-tuning pipeline for reproducibility. Its size makes it easy to run on a single consumer GPU or a modern laptop CPU. Context length is limited to 2,048 tokens, reflecting its 2024-era pretraining. It is released under Apple's Machine Learning Research license, permitting use and modification for research rather than the broad commercial rights of Apache or MIT. Published in April 2024, it trained on roughly 1.8 trillion tokens from RefinedWeb, deduplicated PILE, RedPajama, and Dolma.
1.3M downloads
76 likesSpecifications
- Publisher
- Apple
- Parameters
- 1.1B
- Architecture
- OpenELMForCausalLM
- Vocabulary Size
- 32,000
- Release Date
- 2024-04-12
- License
- apple-amlr
Get Started
HuggingFace
How Much VRAM Does OpenELM 1 1B Instruct Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 0.5 GB | — | 0.46 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 0.5 GB | — | 0.47 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 0.6 GB | — | 0.53 GB | 3-bit medium quantization |
| Q4_K_M | 4.80 | 0.7 GB | — | 0.65 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 0.8 GB | — | 0.77 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 1.0 GB | — | 0.89 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 1.2 GB | — | 1.08 GB | 8-bit quantization, near-lossless |
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 OpenELM 1 1B Instruct?
Q4_K_M · 0.7 GBOpenELM 1 1B Instruct (Q4_K_M) requires 0.7 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 1+ GB is recommended. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run OpenELM 1 1B Instruct?
Q4_K_M · 0.7 GB59 devices with unified memory can run OpenELM 1 1B Instruct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomWhere to Download OpenELM 1 1B Instruct
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Related Models
Frequently Asked Questions
- How much VRAM does OpenELM 1 1B Instruct need?
OpenELM 1 1B Instruct requires 0.7 GB of VRAM at Q4_K_M, or 2.4 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 1.1B × 4.8 bits ÷ 8 = 0.6 GB
KV Cache + Overhead ≈ 0.1 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M0.7 GB- What's the best quantization for OpenELM 1 1B Instruct?
For OpenELM 1 1B Instruct, Q4_K_M (0.7 GB) offers the best balance of quality and VRAM usage. Q5_K_S (0.8 GB) provides better quality if you have the VRAM. The smallest option is IQ3_XS at 0.5 GB.
VRAM requirement by quantization
IQ3_XS0.5 GBIQ3_M0.5 GBIQ4_XS0.6 GBQ4_K_M ★0.7 GBQ5_K_M0.8 GBBF162.4 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run OpenELM 1 1B Instruct on a Mac?
OpenELM 1 1B Instruct requires at least 0.5 GB at IQ3_XS, 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 OpenELM 1 1B Instruct locally?
Yes — OpenELM 1 1B Instruct can run locally on consumer hardware. At Q4_K_M quantization it needs 0.7 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is OpenELM 1 1B Instruct?
At Q4_K_M, OpenELM 1 1B Instruct can reach ~6761 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~923 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 ÷ 0.7 × 0.65 = ~7324 tok/s
Estimated speed at Q4_K_M (0.7 GB)
~7324 tok/s~923 tok/s~7324 tok/s~6761 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of OpenELM 1 1B Instruct?
At Q4_K_M, the download is about 0.65 GB. The full-precision BF16 version is 2.16 GB. The smallest option (IQ3_XS) is 0.45 GB.
- Which GPUs can run OpenELM 1 1B Instruct?
52 consumer GPUs can run OpenELM 1 1B Instruct at Q4_K_M (0.7 GB). 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 OpenELM 1 1B Instruct?
59 devices with unified memory can run OpenELM 1 1B Instruct at Q4_K_M (0.7 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, 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.