What GPU do I need to run allenai/Molmo-7B-D-0924?
8.0B parameters, published in F32. View on Hugging Face
Set up Molmo-7B-D-0924Molmo-7B-D-0924 is published by allenai on Hugging Face, with 23,630 downloads and 566 likes to date. It's a MolmoForCausalLM model built for image-text-to-text, 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 | 29.9 GB | 35.9 GB | RTX A6000 | 1 | $0.363/hr |
| cheaper alt. | V100 | 3 | $0.264/hr | ||
| FP8 (quantized) | 7.5 GB | 9.0 GB | RTX 4070 Super | 1 | $0.121/hr |
| INT4 (quantized) | 3.7 GB | 4.5 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 Molmo-7B-D-0924 at its published (F32) precision: 1× RTX A6000, at $0.363/hr per GPU ($0.363/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
Molmo-7B-D-0924: 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.
Grouped-query attention: every layer caches keys and values for a few shared KV heads. Cached per token: 57,344 bytes at 16-bit.
| Context | KV cache, one sequence |
|---|---|
| 4K | 0.22 GB |
Computed from the layer, head and window counts in the model's own configuration (allenai/Molmo-7B-D-0924), at 16 bits per cached value. Each concurrent sequence needs its own cache, on top of the weights.
Molmo-7B-D-0924: common questions
Can Molmo-7B-D-0924 run on a single GPU?
Yes, but not on a desktop card. At FP32 it needs 35.9 GB of VRAM, more than a single 32 GB desktop card holds. The cheapest live single-card fit is a 48.0 GB RTX A6000 at $0.363/hr.
Can Molmo-7B-D-0924 run in 16-bit instead of FP32?
Yes. Its published weights are FP32, 29.9 GB, or 35.9 GB once inference overhead is added. Loading the same weights in 16 bits halves that to 14.9 GB, or 17.9 GB with overhead. That moves it onto a 24 GB card, which the FP32 weights do not fit. How much accuracy the cast costs is model-specific and is not measured here.
What is the least VRAM Molmo-7B-D-0924 can run in?
4.5 GB, at INT4 (quantized), which fits a 6 GB card, against 35.9 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 Molmo-7B-D-0924 lower the GPU bill?
Yes. At FP32 the cheapest live fit is one RTX A6000 at $0.363/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 Molmo models
Alternatives at this size
Other models for image-text-to-text within about a third of Molmo-7B-D-0924's 8.0B parameters, from other model lines.
- Qwen3.5-9B (9.7B, BF16)
- llava-1.5-7b-hf (7.1B, F16)
- UI-TARS-1.5-7B (8.3B, F32)
- Cosmos-Reason2-8B (8.8B, BF16)
- Ovis2.5-9B (9.2B, BF16)
More on Molmo-7B-D-0924
Fits on a 48 GB GPU at FP32: every model that fits in 48 GB.
Fits on a 12 GB GPU at FP8: every model that fits in 12 GB.
Fits on an 8 GB GPU at INT4: every model that fits in 8 GB.
Best vision-language models: how Molmo-7B-D-0924 ranks against the rest.
Related reading: RTX A6000 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.
