What GPU do I need to run allenai/OLMoE-1B-7B-0125?
6.9B parameters, published in F32. View on Hugging Face
Set up OLMoE-1B-7B-0125OLMoE-1B-7B-0125 is published by allenai on Hugging Face, with 14,901 downloads and 38 likes to date. It's a OlmoeForCausalLM model built for text-generation, published natively in F32.
VRAM required & cheapest live GPU fit
Required VRAM = weight size at each precision times a flat 1.2 overhead for activations and fragmentation. The KV-cache grows with context and is not in that factor; it is listed per model below where the architecture is published. Full formula and assumptions: methodology.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| FP32 | 25.8 GB | 30.9 GB | V100 | 1 | $0.187/hr |
| FP8 (quantized) | 6.4 GB | 7.7 GB | RTX 4000 SFF Ada | 1 | $0.198/hr |
| INT4 (quantized) | 3.2 GB | 3.9 GB | RTX 3060 | 1 | $0.110/hr |
A GPU is only matched to a row if its hardware supports that precision, and the primary recommendation is always a single-GPU fit when one exists.
INT4 requires a quantized checkpoint actually published for this model, check its Hugging Face page before relying on this row.
Cheapest way to run OLMoE-1B-7B-0125 at its published (F32) precision: 1× V100, at $0.187/hr per GPU ($0.187/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
OLMoE-1B-7B-0125: KV cache by context length
The KV cache is the memory the attention layers hold for every token of context, on top of the weights. It is not part of the flat 1.2x overhead in the table above, grows with context length and with every concurrent request, and is why a long-context deployment needs more VRAM than the table shows.
Multi-head attention: every layer caches keys and values for every head. Cached per token: 131,072 bytes at 16-bit.
| Context | KV cache, one sequence |
|---|---|
| 4K | 0.50 GB |
Computed from the layer, head and window counts in the model's own configuration (allenai/OLMoE-1B-7B-0125), at 16 bits per cached value. Each concurrent sequence needs its own cache, on top of the weights.
OLMoE-1B-7B-0125: common questions
Does OLMoE-1B-7B-0125 fit on a 32 GB GPU?
Yes. At FP32 it needs 30.9 GB of VRAM, so a 32 GB card holds it with 1.1 GB to spare. A 24 GB card is not enough for it at FP32.
Can OLMoE-1B-7B-0125 run in 16-bit instead of FP32?
Yes. Its published weights are FP32, 25.8 GB, or 30.9 GB once inference overhead is added. Loading the same weights in 16 bits halves that to 12.9 GB, or 15.5 GB with overhead. That moves it onto a 16 GB card instead of a 32 GB one. How much accuracy the cast costs is model-specific and is not measured here.
What is the least VRAM OLMoE-1B-7B-0125 can run in?
3.9 GB, at INT4 (quantized), which fits a 6 GB card, against 30.9 GB at FP32. That row assumes an INT4 checkpoint has actually been published for this model, so check its Hugging Face page before planning around it.
Does quantizing OLMoE-1B-7B-0125 lower the GPU bill?
Yes. At FP32 the cheapest live fit is one V100 at $0.187/hr. At INT4 (quantized) it drops to one RTX 3060 at $0.110/hr, provided a quantized checkpoint exists for it.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More OLMoE models
- OLMoE-1B-7B-0125-Instruct (6.9B, BF16)
- OLMoE-1B-7B-0924 (6.9B, BF16)
- OLMoE-1B-7B-0924-Instruct (6.9B, BF16)
Alternatives at this size
Other models for text-generation within about a third of OLMoE-1B-7B-0125's 6.9B parameters, from other model lines.
- Qwen3-8B (8.2B, BF16)
- Llama-3.1-8B-Instruct (8.0B, BF16)
- Mistral-7B-Instruct-v0.2 (7.2B, BF16)
- granite-4.1-8b (8.8B, BF16)
- DeepSeek-R1-0528-Qwen3-8B (8.2B, BF16)
More on OLMoE-1B-7B-0125
Fits on a 32 GB GPU at FP32: every model that fits in 32 GB.
Fits on an 8 GB GPU at FP8: every model that fits in 8 GB.
Best chat and assistants models: how OLMoE-1B-7B-0125 ranks against the rest.
Related reading: V100 pricing and specs, How much VRAM you need for LLMs, Serving LLMs with vLLM, vLLM vs TensorRT-LLM vs SGLang, and Best GPU for LLM inference.
