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.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 32K)Best on 24 GB (Q4_K_M · ~923 tok/s · 32K)Macs from 8 GB (Q4_K_M · ~101 tok/s)
apple-amlr
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run OpenELM 1 1B Instruct
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
ollama run hf.co/mradermacher/OpenELM-1_1B-Instruct-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 21.8 GB free · 16K context
Download: OpenELM-1_1B-Instruct.Q4_K_M.gguf0.67 GBmradermacher
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on RTX 3080 Ti (12 GB) (9 GB headroom) · Q4_K_M
- Generation speed
- ~835 tok/s
- generation speed
- Cost per 1M output tokens
- $0.041
- per 1M output tokens
How Much VRAM Does OpenELM 1 1B Instruct Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 0.5 GB | 3.6 GB | 3-bit · i-quant extra small — Importance-weighted 3-bit, extra small | Fits: 32K context · ~1337 tok/s | |
| 0.5 GB | 3.6 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 32K context · ~1310 tok/s | |
| 0.5 GB | 3.7 GB | 3-bit small — 3-bit small quantization | Fits: 32K context · ~1260 tok/s | |
| 0.6 GB | 3.7 GB | 3-bit medium — 3-bit medium quantization | Fits: 32K context · ~1130 tok/s | |
| 0.7 GB | 3.9 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 32K context · ~923 tok/s | |
| 0.8 GB | 4 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 32K context · ~771 tok/s | |
| 1.0 GB | 4.1 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 32K context · ~669 tok/s | |
| 1.2 GB | 4.3 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 32K context · ~551 tok/s |
Selected Q4_K_M
4-bit medium quantization2.2GBat 16K context
- Weights
- 0.2 GB
- KV cache
- 1.7 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 21.8 GB free · ~923 tok/s
≥50 tok/s needs ≥55 GB/s · RTX 5090 ~1641, RTX 3090 Ti ~923
Which Hardware 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. 163 devices with unified memory can run OpenELM 1 1B Instruct, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 5.8 GB free · up to 32K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~696Simulate output on RTX 3080RTX 2080 Ti~564Simulate output on RTX 2080 TiRTX 3070 Ti~557Simulate output on RTX 3070 TiM3*8 GB~101Simulate output on iMac 24" M3 (8 GB)Orin Nano~93Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~67Simulate output on iPhone 1720 GB
fits · 17.8 GB free · up to 32K ctx
1 GPU
RX 7900 XT~676Simulate output on RX 7900 XT32–40 GB
fits · 29.8 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~1641Simulate output on RTX 5090M5 Max36 GB~454Simulate output on Mac Studio M5 Max (36 GB)M532 GB~151Simulate output on MacBook Air 13" M5 (32 GB)Orin~188Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~124Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| MiniCPM5 1BOpenBMB | — | 1.3 GB | ~662 | same size |
| TinyLlama 1.1B Chat v1.0TinyLlama | — | 1 GB | ~649 | same size |
| LLaMA3.1 8B Instruct DFlash UltraChatz-lab | — | 2.7 GB | ~269 | same size |
| Vaultgemma 1BGoogle | — | 3.7 GB | ~286 | same size |
| Qwen3 8B Speculator.eagle3RedHatAI | — | 2.1 GB | ~978 | same size |
| TinyDolphin 2.8 1.1BQuixiAI | — | 1.1 GB | ~649 | same size |
Specifications
- Publisher
- Apple
- Parameters
- 1.1B
- Architecture
- OpenELMForCausalLM
- Vocabulary Size
- 32,000
- Release Date
- 2024-04-12
- License
- apple-amlr
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)
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 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
★ Recommended — best balance of quality and VRAM usage.
Can I run OpenELM 1 1B Instruct on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run OpenELM 1 1B Instruct. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 0.7 GB of usable unified memory (RAM minus macOS overhead).
Can I run OpenELM 1 1B Instruct locally?
Yes — OpenELM 1 1B Instruct can run locally on consumer hardware. 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 ÷ 0.71 × 0.65 = ~7324 tok/s
Estimated speed at Q4_K_M (0.7 GB)
Real-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. 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?
163 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, 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.