How much VRAM does OLMo 3 7B Instruct need?

About 6.8 GB at Q4_K_M with 4K of context and batch 1, rising to roughly 10.9 GB at 32K. That figure is weights plus KV cache plus a 10% activation buffer, calculated from the model's architecture rather than measured on a card.

Will OLMo 3 7B Instruct run on a 8GB GPU?

Yes — at Q4_K_M and 4K context it needs about 6.8 GB, which leaves 1.2 GB spare on a RTX 4060, or on any of the other 8 GB cards here (9 in all). That is the smallest size in this index that clears it comfortably; 72 of 74 do.

Which quantization of OLMo 3 7B Instruct should I use?

No published quality comparison exists for this model, so pick by footprint: Q4_K_M is the level most people run, and a higher bits-per-weight level is more faithful. All 2 levels in the index are Q4_K_M, Q8_0.

OLMo 3 7B Instruct

Allen AI OLMo

Allen AI's fully open 7B model, released with its training data, code and intermediate checkpoints. Its predecessor stopped at a 4K context; OLMo 3 reaches 64K, extended from an 8K base by YaRN. Three of every four layers use a 4K sliding window, which is what makes that affordable. It still uses full multi-head attention with no shared KV heads, so long context costs more here than on most 7B–8B models. At Q4_K_M it needs about 6.8 GB at 4K context and 10.9 GB at 32K. Without the sliding window, 32K would need about 22 GB. Sizes use the calculator's generic rates, since no GGUF file size could be checked, and no per-level quality loss has been published.

⬇ 479.4K HF downloads♥ 153 likesallenai/Olmo-3-7B-Instruct· stats from 10/5/2026

Consumer GPUMac / Apple SiliconCPU / VPS

66K

Max Context

Quant Variants

GGUF Q8_0

Best Quality

—

Accuracy Retained

Calculate VRAM Hugging Face Compare

Quantization Variants

Per-quant VRAM, quality loss, and inference speed on RTX 4090

Measured = run on this site · Estimated = not run here: calculated, or a figure this site has not verified · Community = public reports

Format	Level	BPW	VRAM	PPL Loss	Speed	Source	Actions
GGUF	Q4_K_M	4.85	6.8 GB	—	—	Estimated	Calc HF
GGUF	Q8_0	8.5	10.3 GB	—	—	Estimated	Calc HF

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Phi-3.5 Mini Instruct

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Running OLMo 3 7B Instruct locally

At Q4_K_M and 4K of context, OLMo 3 7B Instruct needs about 6.8 GB — 4.2 GB of weights, 2.00 GB of KV cache and a 0.6 GB activation buffer. The smallest cards in this index that clear that comfortably are 8 GB ones — 9 of them, from the RTX 4060 up — and 72 of the 74 cards here do. These are calculated figures, not measurements: the estimate stops counting a card as comfortable at 88% of its VRAM, which is roughly the room a desktop session needs.

What longer context costs

Going from 4K to 32K adds about 3.69 GB, taking the total to 10.9 GB. That is gentler than the parameter count suggests: only 8 of 32 layers keep a KV cache that grows with context, and 24 more cap theirs at a sliding window, so the cache scales at a fraction of the usual rate. The model's native window is 64K; holding all of it at Q4_K_M would need about 15 GB.

Which build to download

This index only tracks OLMo 3 7B Instruct in GGUF, across 2 levels (Q4_K_M, Q8_0). No per-level perplexity sweep has been published for this model, so the quality column is empty rather than estimated — within one model, more bits per weight is the only ordering the data supports. No throughput has been measured for this model on this site's hardware. GGUF runs on llama.cpp and Ollama across NVIDIA, AMD and Apple silicon; AWQ and GPTQ target vLLM on CUDA and ROCm; EXL2 is ExLlamaV2 — CUDA only, and now archived, so local use rather than serving.

Common questions

How much VRAM does OLMo 3 7B Instruct need?: About 6.8 GB at Q4_K_M with 4K of context and batch 1, rising to roughly 10.9 GB at 32K. That figure is weights plus KV cache plus a 10% activation buffer, calculated from the model's architecture rather than measured on a card.
Will OLMo 3 7B Instruct run on a 8GB GPU?: Yes — at Q4_K_M and 4K context it needs about 6.8 GB, which leaves 1.2 GB spare on a RTX 4060, or on any of the other 8 GB cards here (9 in all). That is the smallest size in this index that clears it comfortably; 72 of 74 do.
Which quantization of OLMo 3 7B Instruct should I use?: No published quality comparison exists for this model, so pick by footprint: Q4_K_M is the level most people run, and a higher bits-per-weight level is more faithful. All 2 levels in the index are Q4_K_M, Q8_0.

Where this model fits

Sized at Q4_K_M with a 4K context window, smallest card first. Comfortable means the estimate uses at most 88% of the memory.

Runs comfortably on: RTX 40608GB · +1.2 RTX 4060 Ti 8G8GB · +1.2 Radeon RX 9060 XT 8G8GB · +1.2 RTX 5060 Ti 8G8GB · +1.2 RTX 50608GB · +1.2
Just misses: Mac M1 8G+0.8GB over Mac M3 8G+0.8GB over
Ships in: GGUF
Try it yourself: Size it in the calculator Generate the run command

How to actually run this

Deployment guides for this model and this class of hardware.

This page's figures change when the model or the runtime does.Last updated 2026-10-05 RSS → /feed.xml