Can I Run Llama 3.1 Family on NVIDIA GeForce RTX 3070?

Autor: Jakub Rusinowski · Ostatnia aktualizacja: 12 lipca 2026

Yes, comfortably — Llama 3.1 8B Instruct at Q4_K_M needs 6.7 GB at 8K context (4.8 GB weights + 1.9 GB KV cache/overhead), leaving ~1.3 GB of the NVIDIA GeForce RTX 3070's 8 GB, at ~47 tok/s (est.), with room for up to 16K context.

See what else this hardware can run →

Kup ten sprzęt NVIDIA GeForce RTX 3070 8GB — 8 GB VRAM · 220 W board powerUruchom w chmurze RTX 4090 w RunPod — od $0.34/godz. · stawka sprawdzona 2026-07

lub porównaj w Vast.ai od $0.35/godz. (typical low · varies)

Jako partner Amazon zarabiamy na kwalifikujących się zakupach. Linki do chmurowych GPU są linkami polecającymi — możemy otrzymać prowizję bez dodatkowych kosztów dla Ciebie.

Ujawnienie afiliacyjne: Niektóre odnośniki na tej stronie to linki afiliacyjne — jeśli dokonasz zakupu za ich pośrednictwem, LLM Configurator może otrzymać prowizję bez dodatkowych kosztów dla Ciebie. Jako uczestnik programu Amazon Associates, LLM Configurator zarabia na kwalifikujących się zakupach.

NVIDIA GeForce RTX 3070 8GB
8 GB VRAM · 220 W board power
Ceny w 2026 są niestabilne — sprawdź aktualną ofertę.
Sprawdź cenę na Amazon

NVIDIA GeForce RTX 3070 Specs

VRAM8 GB
Memory Bandwidth448 GB/s

Llama 3.1 8B Instruct on the NVIDIA GeForce RTX 3070: VRAM by quantization

QuantVRAM neededFits 8 GB?Max contextWhole PC
F1617.9 GB✗ No——
Q8_010.4 GB✗ No——
Q6_K8.4 GB✗ No——
Q5_K_M7.5 GB✓ Yes8K—
Q4_K_M6.7 GB✓ Yes16K—
Q3_K_M5.3 GB✓ Yes16K—
Q2_K4.5 GB✓ Yes32K—

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 8 GB. Figures are estimates from parameter count, quantization and memory bandwidth — the analyzer lets you tune KV-cache quant and context.

Superseded model. Llama 3.1 Family has been superseded by Llama 4. This page is kept for reference; the newer family is a better starting point. Llama 4 needs at least 68.2 GB at Q4_K_M with an 8K context, so it does not fit the NVIDIA GeForce RTX 3070.

Context costs VRAM too. At Q4_K_M on the NVIDIA GeForce RTX 3070: 8K 6.7 GB · 32K 9.9 GB ✗ · 128K 22.8 GB ✗. Past 32K it no longer fits 8 GB — a q8_0 KV cache buys roughly half of that back, which the calculator will price for you.

No compatible GPU? Llama 3.1 8B Instruct on 8 GB of system RAM, CPU only: runs.

Llama 3.1 Family Sizes That Fit the NVIDIA GeForce RTX 3070 (at 8K context)

Llama 3.1 8B InstructQ4_K_M · 6.7 GB at 8K context · ~47 tok/s (est.)
Buy vs. rent Llama 3.1 Family
Buy the GPU
~$499
NVIDIA GeForce RTX 3070 · MSRP
Rent by the hour
from $0.34/hr
RTX 4090 (24 GB) class

At 2 hrs/day, buying (~$499) beats renting at $0.34/hr after about 2.0 years.

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RunPod $0.34/hr
Rent on RunPod →
Vast.ai $0.35/hr · typical low · varies
Rent on Vast.ai →

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 3070 have enough VRAM for Llama 3.1 Family?

Yes, comfortably — Llama 3.1 8B Instruct at Q4_K_M needs 6.7 GB at 8K context (4.8 GB weights + 1.9 GB KV cache/overhead), leaving ~1.3 GB of the NVIDIA GeForce RTX 3070's 8 GB, at ~47 tok/s (est.), with room for up to 16K context.

Which quantization of Llama 3.1 Family should I use on the NVIDIA GeForce RTX 3070?

Llama 3.1 8B Instruct at Q4_K_M quantization needs 6.7 GB at 8K context (4.8 GB weights + 1.9 GB KV cache/overhead), estimated ~47 tokens/sec, up to 16K context.

Llama 3.1 Family on Other GPUs

Popular Models on the NVIDIA GeForce RTX 3070

VRAM Tier

Troubleshooting

Buying Guide

← Can I Run It? | Llama 3.1 Family Model Page | NVIDIA GeForce RTX 3070 GPU Page | VRAM calculator | Check Your Hardware