What GPU do I need to run RedHatAI/Llama-3.1-8B-Instruct-speculator.eagle3?
950M parameters, published in BF16. View on Hugging Face
Set up Llama-3.1-8…peculator.eagle3Llama-3.1-8B-Instruct-speculator.eagle3 is published by RedHatAI on Hugging Face, with 12,550 downloads and 2 likes to date. It's a Eagle3Speculator model built for text-generation, published natively in BF16.
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) |
|---|---|---|---|---|---|
| BF16 | 1.8 GB | 2.1 GB | RTX 3070 | 1 | $0.143/hr |
| FP8 (quantized) | 0.9 GB | 1.1 GB | RTX 5060 Ti | 1 | $0.197/hr |
| INT4 (quantized) | 0.4 GB | 0.5 GB | RTX 3070 | 1 | $0.143/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 Llama-3.1-8B-Instruct-speculator.eagle3 at its published (BF16) precision: 1× RTX 3070, at $0.143/hr per GPU ($0.143/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
Llama-3.1-8B-Instruct-speculator.eagle3: 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.
KV-cache size for Llama-3.1-8B-Instruct-speculator.eagle3 is not published for this architecture, so no figure is shown.
Llama-3.1-8B-Instruct-speculator.eagle3: common questions
How much VRAM does Llama-3.1-8B-Instruct-speculator.eagle3 need?
2.1 GB at BF16, 1.1 GB at FP8 (quantized), 0.5 GB at INT4 (quantized). All of those sit under 6 GB, the smallest capacity this page reasons about, so VRAM is not what limits where this model runs. The 1.8 GB of weights plus inference overhead is the whole requirement.
How many copies of Llama-3.1-8B-Instruct-speculator.eagle3 fit on one RTX 3070?
3, by VRAM alone. That card carries 8.0 GB and one copy needs 2.1 GB at BF16, on a live rate of $0.143/hr for the whole card. Throughput is not modelled here, so 3 copies is not 3 times the requests served.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More Llama 3.1 models
- LLaMA3.1-8B-Instruct-DFlash-UltraChat (1.0B, BF16)
- Llama-3.1-8B-Instruct (8.0B, BF16)
- Llama-3.1-8B (8.0B, BF16)
- Meta-Llama-3.1-8B-Instruct (8.0B, BF16)
- Meta-Llama-3.1-8B-Instruct (8.0B, BF16)
Lineage
Based on Llama-3.1-8B-Instruct.
Alternatives at this size
Other models for text-generation within about a third of Llama-3.1-8B-Instruct-speculator.eagle3's 950M parameters, from other model lines.
- Qwen3-0.6B (752M, BF16)
- gemma-3-1b-it (1000M, BF16)
- OpenELM-1_1B-Instruct (1.1B, BF16)
- gpt2-large (812M, F32)
- MiniCPM5-1B (1.1B, BF16)
More on Llama-3.1-8B-Instruct-speculator.eagle3
Fits on an 8 GB GPU at BF16: every model that fits in 8 GB.
Related reading: RTX 3070 pricing and specs, How much VRAM you need for LLMs, Serving LLMs with vLLM, vLLM vs TensorRT-LLM vs SGLang, Best GPU for LLM inference, and What AI model fine-tuning is.
