Finance Llama3 8B — Hardware Requirements & GPU Compatibility
Chat
Finance Llama3 8B is a 8.0B-parameter open language model from instruction-pretrain in the Llama 3 family. It supports a context window of up to 8,192 tokens. At FP16 it needs about 16.63 GB of VRAM — see which GPUs and Macs can run it below.
1.9K downloads
76 likesSpecifications
- Publisher
- instruction-pretrain
- Family
- Llama 3
- Parameters
- 8.0B
- Architecture
- LlamaForCausalLM
- Context Length
- 8,192 tokens
- Vocabulary Size
- 128,256
- Release Date
- 2026-03-02
- License
- Llama 3 Community
Get Started
HuggingFace
How Much VRAM Does Finance Llama3 8B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| FP16 | 16.00 | 16.6 GB | 17.4 GB | 16.06 GB | Full half-precision — baseline for inference |
Which GPUs Can Run Finance Llama3 8B?
FP16 · 16.6 GBFinance Llama3 8B (FP16) requires 16.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 22+ GB is recommended. Using the full 8K context window can add up to 0.8 GB, bringing total usage to 17.4 GB. 6 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroom
Which Devices Can Run Finance Llama3 8B?
FP16 · 16.6 GB21 devices with unified memory can run Finance Llama3 8B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Finance Llama3 8B need?
Finance Llama3 8B requires 16.6 GB of VRAM at FP16. Full 8K context adds up to 0.8 GB (17.4 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 8.0B × 16 bits ÷ 8 = 16.1 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 1.3 GB (at full 8K context)
VRAM usage by quantization
FP1616.6 GBFP16 + full context17.4 GB- Can I run Finance Llama3 8B on a Mac?
Finance Llama3 8B requires at least 16.6 GB at FP16, 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 Finance Llama3 8B locally?
Yes — Finance Llama3 8B can run locally on consumer hardware. At FP16 quantization it needs 16.6 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Finance Llama3 8B?
At FP16, Finance Llama3 8B can reach ~175 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~39 tok/s. 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 ÷ 16.6 × 0.55 = ~175 tok/s
Estimated speed at FP16 (16.6 GB)
~175 tok/s~39 tok/s~131 tok/s~108 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Finance Llama3 8B?
At FP16, the download is about 16.06 GB.
- Which GPUs can run Finance Llama3 8B?
6 consumer GPUs can run Finance Llama3 8B at FP16 (16.6 GB). Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XT, AMD Radeon RX 7900 XTX. 1 GPU have plenty of headroom for comfortable inference.
- Which devices can run Finance Llama3 8B?
21 devices with unified memory can run Finance Llama3 8B at FP16 (16.6 GB), including Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB), Mac Pro M2 Ultra (192 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.