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. 35 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 GB33 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 ~4106 tok/s on AMD Instinct MI300X. 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: AMD Instinct MI300X → 5300 ÷ 0.7 × 0.55 = ~4106 tok/s
Estimated speed at Q4_K_M (0.7 GB)
~4106 tok/s~923 tok/s~3069 tok/s~2538 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?
35 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. 35 GPUs have plenty of headroom for comfortable inference.
- Which devices can run OpenELM 1 1B Instruct?
33 devices with unified memory can run OpenELM 1 1B Instruct at Q4_K_M (0.7 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.