Olmo 3 7B Instruct — Hardware Requirements & GPU Compatibility
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OLMo 3 7B Instruct is an instruction-tuned language model from the Allen Institute for AI, built as part of their Open Language Model initiative. Like all OLMo releases, it comes with fully open training data, code, and intermediate checkpoints, setting a high standard for reproducibility and scientific transparency in the LLM space. At roughly 7 billion parameters, this model delivers competitive performance on instruction following, reasoning, and general knowledge tasks while remaining runnable on consumer GPUs with 8 GB or more of VRAM. It is an excellent choice for users who value open science and want a capable, well-documented model for local chat and assistant applications.
152.1K downloads
132 likesSpecifications
- Publisher
- Allen AI
- Family
- OLMo
- Parameters
- 7.3B
- Architecture
- Olmo3ForCausalLM
- Context Length
- 65,536 tokens
- Vocabulary Size
- 100,278
- Release Date
- 2025-11-19
- License
- Apache 2.0
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HuggingFace
How Much VRAM Does Olmo 3 7B Instruct Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 4.5 GB | 37.8 GB | 3.10 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 4.6 GB | 37.9 GB | 3.19 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 4.9 GB | 38.2 GB | 3.56 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 5.0 GB | 38.3 GB | 3.65 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 5.8 GB | 39.0 GB | 4.38 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 6.6 GB | 39.9 GB | 5.20 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 7.4 GB | 40.7 GB | 6.02 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 8.7 GB | 42.0 GB | 7.30 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Olmo 3 7B Instruct?
Q4_K_M · 5.8 GBOlmo 3 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 66K context window can add up to 33.3 GB, bringing total usage to 39.0 GB. 50 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Olmo 3 7B Instruct?
Q4_K_M · 5.8 GB58 devices with unified memory can run Olmo 3 7B Instruct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Air 13" M3 (8 GB).
Runs great
— Plenty of headroomWhere to Download Olmo 3 7B 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 Olmo 3 7B Instruct need?
Olmo 3 7B Instruct requires 5.8 GB of VRAM at Q4_K_M, or 16.0 GB at BF16. Full 66K context adds up to 33.3 GB (39.0 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)
KV Cache + Overhead ≈ 34.6 GB (at full 66K context)
VRAM usage by quantization
Q4_K_M5.8 GBQ4_K_M + full context39.0 GB- What's the best quantization for Olmo 3 7B Instruct?
For Olmo 3 7B Instruct, Q4_K_M (5.8 GB) offers the best balance of quality and VRAM usage. Q5_K_S (6.4 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 3.4 GB.
VRAM requirement by quantization
IQ2_XXS3.4 GBQ3_K_S4.6 GBQ4_15.5 GBQ4_K_M ★5.8 GBQ5_K_S6.4 GBBF1616.0 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Olmo 3 7B Instruct on a Mac?
Olmo 3 7B Instruct requires at least 3.4 GB at IQ2_XXS, 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 3 7B Instruct locally?
Yes — Olmo 3 7B Instruct can run locally on consumer hardware. At Q4_K_M quantization it needs 5.8 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Olmo 3 7B Instruct?
At Q4_K_M, Olmo 3 7B Instruct can reach ~765 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 ÷ 5.8 × 0.65 = ~904 tok/s
Estimated speed at Q4_K_M (5.8 GB)
~904 tok/s~114 tok/s~904 tok/s~765 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Olmo 3 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 (IQ2_XXS) is 2.01 GB.
- Which GPUs can run Olmo 3 7B Instruct?
50 consumer GPUs can run Olmo 3 7B Instruct at Q4_K_M (5.8 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 39 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Olmo 3 7B Instruct?
59 devices with unified memory can run Olmo 3 7B Instruct at Q4_K_M (5.8 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.