OLMo 1B HF — Hardware Requirements & GPU Compatibility
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OLMo 1B HF is a 1.2B-parameter open language model from Allen AI in the OLMo family. It supports a context window of up to 2,048 tokens. At Q4_K_M it needs about 1.27 GB of VRAM — see which GPUs and Macs can run it below.
66.7K downloads
29 likesSpecifications
- Publisher
- Allen AI
- Family
- OLMo
- Parameters
- 1.2B
- Architecture
- OlmoForCausalLM
- Context Length
- 2,048 tokens
- Vocabulary Size
- 50,304
- Release Date
- 2024-04-12
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does OLMo 1B HF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 1.1 GB | — | 0.50 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 1.1 GB | — | 0.51 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 1.1 GB | — | 0.57 GB | 3-bit medium quantization |
| Q4_K_M | 4.80 | 1.3 GB | — | 0.71 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 1.4 GB | — | 0.84 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 1.5 GB | — | 0.97 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 1.8 GB | — | 1.18 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 OLMo 1B HF?
Q4_K_M · 1.3 GBOLMo 1B HF (Q4_K_M) requires 1.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 2+ 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 OLMo 1B HF?
Q4_K_M · 1.3 GB59 devices with unified memory can run OLMo 1B HF, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomWhere to Download OLMo 1B HF
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
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Frequently Asked Questions
- How much VRAM does OLMo 1B HF need?
OLMo 1B HF requires 1.3 GB of VRAM at Q4_K_M, or 2.9 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 1.2B × 4.8 bits ÷ 8 = 0.7 GB
KV Cache + Overhead ≈ 0.6 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M1.3 GB- What's the best quantization for OLMo 1B HF?
For OLMo 1B HF, Q4_K_M (1.3 GB) offers the best balance of quality and VRAM usage. Q5_K_S (1.4 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 1.1 GB.
VRAM requirement by quantization
Q2_K1.1 GBQ3_K_L1.2 GBQ4_K_M ★1.3 GBQ5_K_S1.4 GBQ5_K_M1.4 GBBF162.9 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run OLMo 1B HF on a Mac?
OLMo 1B HF requires at least 1.1 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 OLMo 1B HF locally?
Yes — OLMo 1B HF can run locally on consumer hardware. At Q4_K_M quantization it needs 1.3 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is OLMo 1B HF?
At Q4_K_M, OLMo 1B HF can reach ~3780 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~516 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 ÷ 1.3 × 0.65 = ~4095 tok/s
Estimated speed at Q4_K_M (1.3 GB)
~4095 tok/s~516 tok/s~4095 tok/s~3780 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of OLMo 1B HF?
At Q4_K_M, the download is about 0.71 GB. The full-precision BF16 version is 2.35 GB. The smallest option (Q2_K) is 0.50 GB.
- Which GPUs can run OLMo 1B HF?
52 consumer GPUs can run OLMo 1B HF at Q4_K_M (1.3 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 OLMo 1B HF?
59 devices with unified memory can run OLMo 1B HF at Q4_K_M (1.3 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.