OLMo 1B HF — Hardware Requirements & GPU Compatibility
Chat
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.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 2K)Best on 24 GB (Q4_K_M · ~516 tok/s · 2K)Macs from 8 GB (Q4_K_M · ~56 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run OLMo 1B HF
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/OLMo-1B-hf-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 22.7 GB free · 2K context
Download: OLMo-1B-hf.Q4_K_M.gguf0.73 GBmradermacher
Ways to runOther ways
each opens step-by-stepRun in cloud
Your own private copy · Q4_K_M · 24 GB GPU
- The easiest way to try this model
- Running in a few minutes, easy setup
- Chat right here or connect any app
$5+ bonus for new users
Runs on RTX 3080 Ti (12 GB) (10 GB headroom) · Q4_K_M
- Generation speed
- ~467 tok/s
- generation speed
- Cost per 1M output tokens
- $0.073
- per 1M output tokens
How Much VRAM Does OLMo 1B HF Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 1.1 GB | — | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 2K context · ~612 tok/s | |
| 1.1 GB | — | 3-bit small — 3-bit small quantization | Fits: 2K context · ~607 tok/s | |
| 1.1 GB | — | 3-bit medium — 3-bit medium quantization | Fits: 2K context · ~575 tok/s | |
| 1.2 GB | — | 3-bit large — 3-bit large quantization | Fits: 2K context · ~560 tok/s | |
| 1.3 GB | — | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 2K context · ~516 tok/s | |
| 1.4 GB | — | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 2K context · ~465 tok/s | |
| 1.5 GB | — | 6-bit, very good — 6-bit quantization, very good quality | Fits: 2K context · ~426 tok/s | |
| 1.8 GB | — | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 2K context · ~374 tok/s |
Selected Q4_K_M
4-bit medium quantization1.3GBat 2K context
- Weights
- 0.7 GB
- KV cache
- 0.3 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 22.7 GB free · ~516 tok/s
≥50 tok/s needs ≥98 GB/s · RTX 5090 ~917, RTX 3090 Ti ~516
Which Hardware 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. 163 devices with unified memory can run OLMo 1B HF, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 6.7 GB free · up to 2K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~389Simulate output on RTX 3080RTX 2080 Ti~315Simulate output on RTX 2080 TiRTX 3070 Ti~311Simulate output on RTX 3070 TiM3*8 GB~56Simulate output on iMac 24" M3 (8 GB)Orin Nano~52Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~38Simulate output on iPhone 1720 GB
fits · 18.7 GB free · up to 2K ctx
1 GPU
RX 7900 XT~378Simulate output on RX 7900 XT32–40 GB
fits · 30.7 GB free · up to 2K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~917Simulate output on RTX 5090M5 Max36 GB~254Simulate output on Mac Studio M5 Max (36 GB)M532 GB~85Simulate output on MacBook Air 13" M5 (32 GB)Orin~105Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~69Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to OLMo 1B HF
OLMo family · 20 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| MinerU2.5 Pro 2605 1.2Bopendatalab | — | 1.1 GB | ~642 | same size |
| MinerU2.5 Pro 2604 1.2Bopendatalab | — | 1.1 GB | ~642 | same size |
| Llama 3.2 1B InstructMeta | — | 2.3 GB | ~799 | same size |
| TinyLlama 1.1B Chat v1.0TinyLlama | — | 1 GB | ~649 | same size |
| HRM Text 1Bsapientinc | — | 3.1 GB | ~228 | same size |
| LFM2.5 1.2B ThinkingLiquid AI | — | 1.2 GB | ~612 | same size |
Specifications
- 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
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)
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 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
★ Recommended — best balance of quality and VRAM usage.
Can I run OLMo 1B HF on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run OLMo 1B HF. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 1.3 GB of usable unified memory (RAM minus macOS overhead).
Can I run OLMo 1B HF locally?
Yes — OLMo 1B HF can run locally on consumer hardware. 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 ÷ 1.2700 × 0.65 = ~4094 tok/s
Estimated speed at Q4_K_M (1.3 GB)
Real-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. 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?
163 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, 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.