FlexOlmo 7x7B 1T — Hardware Requirements & GPU Compatibility
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FlexOlmo 7x7B 1T is a 33.3B-parameter open language model from Allen AI. It supports a context window of up to 4,096 tokens. At BF16 it needs about 67.92 GB of VRAM — see which GPUs and Macs can run it below.
335 downloads
40 likesSpecifications
- Publisher
- Allen AI
- Parameters
- 33.3B
- Architecture
- FlexOlmoForCausalLM
- Context Length
- 4,096 tokens
- Vocabulary Size
- 100,352
- Release Date
- 2026-03-02
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does FlexOlmo 7x7B 1T Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 67.9 GB | 69.0 GB | 66.54 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run FlexOlmo 7x7B 1T?
BF16 · 67.9 GBFlexOlmo 7x7B 1T (BF16) requires 67.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 89+ GB is recommended. Using the full 4K context window can add up to 1.1 GB, bringing total usage to 69.0 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run FlexOlmo 7x7B 1T?
BF16 · 67.9 GB5 devices with unified memory can run FlexOlmo 7x7B 1T, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
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Frequently Asked Questions
- How much VRAM does FlexOlmo 7x7B 1T need?
FlexOlmo 7x7B 1T requires 67.9 GB of VRAM at BF16. Full 4K context adds up to 1.1 GB (69.0 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 33.3B × 16 bits ÷ 8 = 66.5 GB
KV Cache + Overhead ≈ 1.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 2.5 GB (at full 4K context)
VRAM usage by quantization
BF1667.9 GBBF16 + full context69.0 GB- Can NVIDIA GeForce RTX 5090 run FlexOlmo 7x7B 1T?
No — FlexOlmo 7x7B 1T requires at least 67.9 GB at BF16, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- Can I run FlexOlmo 7x7B 1T on a Mac?
FlexOlmo 7x7B 1T requires at least 67.9 GB at BF16, 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 FlexOlmo 7x7B 1T locally?
Yes — FlexOlmo 7x7B 1T can run locally on consumer hardware. At BF16 quantization it needs 67.9 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is FlexOlmo 7x7B 1T?
At BF16, FlexOlmo 7x7B 1T can reach ~43 tok/s on AMD Instinct MI300X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 67.9 × 0.55 = ~43 tok/s
Estimated speed at BF16 (67.9 GB)
~43 tok/s~32 tok/s~27 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of FlexOlmo 7x7B 1T?
At BF16, the download is about 66.54 GB.
- Which GPUs can run FlexOlmo 7x7B 1T?
No single consumer GPU has enough VRAM to run FlexOlmo 7x7B 1T at BF16 (67.9 GB). Multi-GPU or professional hardware is required.
- Which devices can run FlexOlmo 7x7B 1T?
5 devices with unified memory can run FlexOlmo 7x7B 1T at BF16 (67.9 GB), including Mac Pro M2 Ultra (192 GB), Mac Studio M2 Ultra (192 GB), Mac Studio M4 Max (128 GB), NVIDIA DGX A100 640GB. Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.