Can I Run Llama 3.2 Family on NVIDIA GeForce RTX 3080 (10GB)?
Written by Jakub Rusinowski · Last updated August 15, 2026
Yes, comfortably — Llama 3.2 11B Vision Instruct at Q4_K_M needs 8.5 GB at 8K context (6.4 GB weights + 2.1 GB KV cache/overhead), leaving ~1.5 GB of the NVIDIA GeForce RTX 3080 (10GB)'s 10 GB, at ~58 tok/s (est.), with room for up to 16K context.
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NVIDIA GeForce RTX 3080 (10GB) Specs
| VRAM | 10 GB |
| Memory Bandwidth | 760 GB/s |
Llama 3.2 11B Vision Instruct on the NVIDIA GeForce RTX 3080 (10GB): VRAM by quantization
| Quant | VRAM needed | Fits 10 GB? | Max context | Whole PC |
|---|---|---|---|---|
| F16 | 23.3 GB | ✗ No | — | — |
| Q8_0 | 13.4 GB | ✗ No | — | — |
| Q6_K | 10.8 GB | ✗ No | — | — |
| Q5_K_M | 9.7 GB | ✓ Yes | 8K | — |
| Q4_K_M | 8.5 GB | ✓ Yes | 16K | — |
| Q3_K_M | 6.7 GB | ✓ Yes | 16K | — |
| Q2_K | 5.6 GB | ✓ Yes | 32K | — |
Assumes an 8K-token context with an f16 KV cache. A longer window needs more; a quantized KV cache needs less. “Max context” is the largest window that still fits in 10 GB. Figures are estimates from parameter count, quantization and memory bandwidth — the analyzer lets you tune KV-cache quant and context.
Context costs VRAM too. At Q4_K_M on the NVIDIA GeForce RTX 3080 (10GB): 8K 8.5 GB · 32K 12.6 GB ✗ · 125K 28.2 GB ✗. Past 32K it no longer fits 10 GB — a q8_0 KV cache buys roughly half of that back, which the calculator will price for you.
No compatible GPU? Llama 3.2 11B Vision Instruct on 16 GB of system RAM, CPU only: runs.
Llama 3.2 Family Sizes That Fit the NVIDIA GeForce RTX 3080 (10GB) (at 8K context)
| Llama 3.2 11B Vision Instruct | Q4_K_M · 8.5 GB at 8K context · ~58 tok/s (est.) |
| Llama 3.2 3B Instruct | Q4_K_M · 3.7 GB at 8K context · ~136 tok/s (est.) |
| Llama 3.2 1B Instruct | Q4_K_M · 1.8 GB at 8K context · ~243 tok/s (est.) |
At 2 hrs/day, buying (~$699) beats renting at $0.34/hr after about 2.9 years.
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Cloud rates verified 2026-07 — estimates, and marketplace prices vary. Buying price is GPU MSRP only, not a full PC.
FAQ
Does the NVIDIA GeForce RTX 3080 (10GB) have enough VRAM for Llama 3.2 Family?
Yes, comfortably — Llama 3.2 11B Vision Instruct at Q4_K_M needs 8.5 GB at 8K context (6.4 GB weights + 2.1 GB KV cache/overhead), leaving ~1.5 GB of the NVIDIA GeForce RTX 3080 (10GB)'s 10 GB, at ~58 tok/s (est.), with room for up to 16K context.
Which quantization of Llama 3.2 Family should I use on the NVIDIA GeForce RTX 3080 (10GB)?
Llama 3.2 11B Vision Instruct at Q4_K_M quantization needs 8.5 GB at 8K context (6.4 GB weights + 2.1 GB KV cache/overhead), estimated ~58 tokens/sec, up to 16K context.
Every Llama 3.2 Family size on the NVIDIA GeForce RTX 3080 (10GB)
| Size | VRAM at 8K | Verdict | Speed |
|---|---|---|---|
| Llama 3.2 90B Vision Instruct | 57.8 GB | Does not fit | — |
| Llama 3.2 11B Vision Instruct | 8.5 GB | Runs | ~58 tok/s |
| Llama 3.2 3B Instruct | 3.7 GB | Runs | ~136 tok/s |
| Llama 3.2 1B Instruct | 1.8 GB | Runs | ~243 tok/s |
Llama 3.2 Family on Other GPUs
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- Llama 3.2 Family on NVIDIA GeForce RTX 5060
- Llama 3.2 Family on NVIDIA GeForce RTX 4070 Ti
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VRAM Tier
Troubleshooting
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