Can I Run Meta Llama 3.1 8B Instruct on NVIDIA GeForce RTX 4080 Super (16GB GDDR6X)?
Full VRAM footprint analysis, tokens/sec generation benchmark, and copy-paste Ollama & vLLM runtime commands for running Meta Llama 3.1 8B Instruct (8B) on NVIDIA GeForce RTX 4080 Super (16GB GDDR6X) (16 GB).
✓ Fits Comfortably in VRAM
OLLAMA_FLASH_ATTENTION=1 OLLAMA_KV_CACHE_TYPE=q8_0 ollama run llama3.1:8bLocal Hardware Configurations & Pricing
NVIDIA GeForce RTX 5090 (32GB GDDR7)
Maximum tokens/sec on 27B–35B models & Blackwell FP4 quantization
NVIDIA GeForce RTX 4090 (24GB GDDR6X)
The 24GB CUDA enthusiast standard for 14B–32B Q4_K_M models
2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM)
Best price-to-VRAM homelab rig for 70B Q4_K_M & Qwen 3.8 27B Q8
NVIDIA GeForce RTX 4080 Super (16GB GDDR6X)
736 GB/s memory bandwidth for fast 14B and MoE 35B models
Cloud Instance Options for Meta Llama 3.1 8B Instruct
On-DemandDeploy this model on an on-demand cloud GPU without local hardware constraints or power usage:
Quantization Precision Matrix
32K ctx • KV: Q8_0| Quantization | Weights | KV Cache | Total VRAM | Est. Speed | Fit Status |
|---|---|---|---|---|---|
| FP1616 bpw | 16 GB | 2.15 GB | 19.46 GB (122%) | ~7 t/s | PARTIAL |
| Q8_08.5 bpw | 8.48 GB | 2.15 GB | 11.68 GB (73%) | ~56 t/s | FITS |
| Q6_K6.56 bpw | 6.56 GB | 2.15 GB | 9.69 GB (61%) | ~71 t/s | FITS |
| Q5_K_M5.68 bpw | 5.68 GB | 2.15 GB | 8.78 GB (55%) | ~80 t/s | FITS |
| Q4_K_M4.8 bpw | 4.8 GB | 2.15 GB | 7.87 GB (49%) | ~94 t/s | FITS |
| FP44.25 bpw | 4.24 GB | 2.15 GB | 7.29 GB (46%) | ~105 t/s | FITS |
| IQ3_XS3.3 bpw | 3.28 GB | 2.15 GB | 6.29 GB (39%) | ~131 t/s | FITS |
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Frequently Asked Questions: Meta Llama 3.1 8B Instruct on NVIDIA GeForce RTX 4080 Super (16GB GDDR6X)
How much VRAM does Meta Llama 3.1 8B Instruct need on NVIDIA GeForce RTX 4080 Super (16GB GDDR6X)?
At standard 4-bit quantization (Q4_K_M) with 32K context, Meta Llama 3.1 8B Instruct requires 7.87 GB of total VRAM (weights + KV cache + CUDA runtime overhead).
What inference speed will Meta Llama 3.1 8B Instruct achieve on NVIDIA GeForce RTX 4080 Super (16GB GDDR6X)?
Based on NVIDIA GeForce RTX 4080 Super (16GB GDDR6X)'s 736 GB/s memory bandwidth, expected decode speed is approximately 94 tokens per second.
Can I expand context beyond 32K tokens?
Each additional 32K tokens of context requires additional KV cache memory. For longer context windows, consider 8-bit or 4-bit KV cache quantization.