OpenELM 1 1B Instruct — Hardware Requirements & GPU Compatibility
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OpenELM 1 1B Instruct is a 1.1B-parameter open language model from Apple. At Q4_K_M it needs about 0.71 GB of VRAM — see which GPUs and Macs can run it below.
1.5M downloads
75 likesSpecifications
- Publisher
- Apple
- Parameters
- 1.1B
- Architecture
- OpenELMForCausalLM
- Vocabulary Size
- 32,000
- Release Date
- 2024-04-12
- License
- apple-amlr
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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. 50 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 ~6197 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~6197 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?
50 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. 50 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.