OLMo 2 1124 7B Instruct — Hardware Requirements & GPU Compatibility
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OLMo 2 1124 7B Instruct is a 7.3B-parameter open language model from Allen AI in the OLMo family. It supports a context window of up to 4,096 tokens. At Q4_K_M it needs about 5.75 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 · 4K)Best on 24 GB (Q4_K_M · ~114 tok/s · 4K)Macs from 16 GB (Q4_K_M · ~24 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run OLMo 2 1124 7B 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 olmo2:7bQ4_K_M fits fully on the RTX 4090 · 17.2 GB free · 4K context
Download: Weights on Hugging Face
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on RTX 3080 Ti (12 GB) (5 GB headroom) · Q4_K_M
- Generation speed
- ~103 tok/s
- generation speed
- Cost per 1M output tokens
- $0.33
- per 1M output tokens
How Much VRAM Does OLMo 2 1124 7B Instruct Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 4.5 GB | — | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 4K context · ~146 tok/s | |
| 4.9 GB | — | 3-bit medium — 3-bit medium quantization | Fits: 4K context · ~133 tok/s | |
| 5.8 GB | — | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 4K context · ~114 tok/s | |
| 6.6 GB | — | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 4K context · ~100 tok/s | |
| 7.4 GB | — | 6-bit, very good — 6-bit quantization, very good quality | Fits: 4K context · ~89 tok/s | |
| 8.7 GB | — | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 4K context · ~76 tok/s | |
| 16.0 GB | — | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 4K context · ~41 tok/s |
Selected Q4_K_M
4-bit medium quantization6.8GBat 4K context
- Weights
- 4.4 GB
- KV cache
- 2.1 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 17.2 GB free · ~114 tok/s
≥50 tok/s needs ≥442 GB/s · RTX 5090 ~203, RTX 3090 Ti ~114
Which Hardware Can Run OLMo 2 1124 7B Instruct?
Q4_K_M · 5.8 GBOLMo 2 1124 7B Instruct (Q4_K_M) requires 5.8 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 8+ GB is recommended. Using the full 4K context window can add up to 1.1 GB, bringing total usage to 6.8 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti. 151 devices with unified memory can run OLMo 2 1124 7B Instruct, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), NVIDIA Jetson Orin Nano 8GB (Super).
≤ 11 GB
≤ 11 GB tier →fits · 1.2 GB free · up to 4K ctx
16 GPUs · 1 device family
RTX 3080~86Simulate output on RTX 3080RTX 2080 Ti~70Simulate output on RTX 2080 TiRTX 3070 Ti~69Simulate output on RTX 3070 TiOrin Nano~12Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)20 GB
fits · 13.2 GB free · up to 4K ctx
1 GPU
RX 7900 XT~84Simulate output on RX 7900 XT32–40 GB
fits · 25.2 GB free · up to 4K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~203Simulate output on RTX 5090M5 Max36 GB~56Simulate output on Mac Studio M5 Max (36 GB)M532 GB~19Simulate output on MacBook Air 13" M5 (32 GB)Orin~23Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~15Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to OLMo 2 1124 7B Instruct
OLMo family · 20 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Qwen2.5 7B InstructAlibaba | 65.1 | 5.8 GB | ~131 | same size |
| Phi Mini MoE InstructMicrosoft | — | 5.4 GB | ~239 | same size |
| Gemma 3n E4B ITGoogle | — | 7.7 GB | ~127 | same size |
| Falcon H1 7B InstructTII UAE | — | 6.3 GB | ~130 | same size |
| DeepSeek R1 Distill Qwen 7BDeepSeek | — | 5.8 GB | ~131 | same size |
| Eurus 2 7B PRIMEPRIME-RL | — | 5.1 GB | ~131 | same size |
Specifications
- Publisher
- Allen AI
- Family
- OLMo
- Parameters
- 7.3B
- Architecture
- Olmo2ForCausalLM
- Context Length
- 4,096 tokens
- Vocabulary Size
- 100,352
- Release Date
- 2024-12-18
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does OLMo 2 1124 7B Instruct need?
OLMo 2 1124 7B Instruct requires 5.8 GB of VRAM at Q4_K_M, or 16.0 GB at BF16. Full 4K context adds up to 1.1 GB (6.8 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 7.3B × 4.8 bits ÷ 8 = 4.4 GB
KV Cache + Overhead ≈ 1.3 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.
KV Cache + Overhead ≈ 2.4 GB (at full 4K context)
VRAM usage by quantization
What's the best quantization for OLMo 2 1124 7B Instruct?
For OLMo 2 1124 7B Instruct, Q4_K_M (5.8 GB) offers the best balance of quality and VRAM usage. Q5_K_M (6.6 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 4.5 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run OLMo 2 1124 7B Instruct on a Mac?
Yes — Mac Mini M2 Pro (16 GB) and 133 other Macs can run OLMo 2 1124 7B Instruct. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 5.8 GB of usable unified memory (RAM minus macOS overhead).
Can I run OLMo 2 1124 7B Instruct locally?
Yes — OLMo 2 1124 7B Instruct can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is OLMo 2 1124 7B Instruct?
At Q4_K_M, OLMo 2 1124 7B Instruct can reach ~835 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~114 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 ÷ 5.75 × 0.65 = ~904 tok/s
Estimated speed at Q4_K_M (5.8 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of OLMo 2 1124 7B Instruct?
At Q4_K_M, the download is about 4.38 GB. The full-precision BF16 version is 14.60 GB. The smallest option (Q2_K) is 3.10 GB.
Which GPUs can run OLMo 2 1124 7B Instruct?
52 consumer GPUs can run OLMo 2 1124 7B Instruct at Q4_K_M. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 40 GPUs have plenty of headroom for comfortable inference.
Which devices can run OLMo 2 1124 7B Instruct?
151 devices with unified memory can run OLMo 2 1124 7B Instruct at Q4_K_M (5.8 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.