For Llama 3 8B, the RTX 4060 Ti 16GB at $400 handles it easily at Q8 quantization. For Llama 3 70B, you need the RTX 5090 32GB at minimum — or dual RTX 4090s for better quality. The 405B variant is cloud-only territory.
NVIDIA GeForce RTX 4060 Ti 16GB
16GB GDDR616GB VRAM runs Llama 3 8B at Q8 quantization with room for 8K+ context. Best price-to-performance ratio for the everyday Llama 3 user.
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Llama 3 model family overview
Meta’s Llama 3 lineup spans three sizes with very different hardware demands:
| Model | Parameters | FP16 Size | Q4_K_M Size | Q8 Size | Minimum VRAM |
|---|---|---|---|---|---|
| Llama 3 8B | 8B | ~16GB | ~4.5GB | ~8.5GB | 8GB (tight) |
| Llama 3 70B | 70B | 141GB | 43GB | 75GB | 32GB (q3_K_S or q2_K only) |
| Llama 3 405B | 405B | ~810GB | ~230GB | ~430GB | Multi-GPU only |
The 8B model is the everyday workhorse — fast, capable, and fits on nearly any modern GPU. The 70B model delivers substantially better reasoning and instruction-following but demands serious hardware. The 405B is primarily a research and enterprise tool that is out of reach for single-GPU setups.
VRAM capacity memory bandwidth Specs are manufacturer figures. Bar lengths are scaled independently per metric.
Llama 3 vs Llama 2: hardware differences
Llama 3 introduces Grouped Query Attention (GQA) and a larger vocabulary (128K tokens vs 32K). Both changes have hardware implications:
- GQA reduces KV cache size — Llama 3 8B uses less VRAM for its attention cache than Llama 2 13B, despite similar inference quality
- Larger vocabulary adds a small VRAM overhead (~0.5GB at FP16) that most calculators undercount
- Context length — Llama 3 supports 8K context by default vs 4K for Llama 2; longer context fills the KV cache faster
Net result: Llama 3 8B is roughly as demanding as Llama 2 7B on VRAM but more capable on output quality. Llama 3 70B is more demanding than Llama 2 70B due to its 8K context default.
Best GPUs for Llama 3 8B
The 8B model is lightweight enough to run on almost any modern GPU at Q4 quantization. The question is how fast you want it and what quantization quality you want.
| GPU | VRAM | Llama 3 8B Q4_K_M | Llama 3 8B Q8 | Price |
|---|---|---|---|---|
| RTX 5090 | 32GB | ~95 tok/s | ~85 tok/s | ~$4,900 |
| RTX 4090 | 24GB | ~65 tok/s | ~60 tok/s | ~$2,200 |
| RTX 5080 | 16GB | ~55 tok/s | ~50 tok/s | ~$1,400 |
| RTX 5070 Ti | 16GB | ~45 tok/s | ~40 tok/s | ~$1,050 |
| RTX 4070 Ti Super | 16GB | ~40 tok/s | ~35 tok/s | ~$800 |
| RTX 4060 Ti 16GB | 16GB | ~35 tok/s | ~28 tok/s | ~$425 |
| RTX 3060 12GB (used) | 12GB | ~30 tok/s | ~18 tok/s | ~$250 |
At Q4_K_M, the 8B model uses about 4.5GB of VRAM plus context overhead. Even the RTX 3060 12GB handles it comfortably with room for 8K+ context. The RTX 4060 Ti 16GB lets you run Q8 — noticeably better output quality with only a modest speed drop. For an exact breakdown of Llama 3 8B VRAM needs across every quantization level, see how much VRAM for Llama 3 8B.
Optimal quantization per GPU tier for Llama 3 8B
| GPU Tier | Recommended Quant | Why |
|---|---|---|
| 8GB VRAM | Q4_K_M | Fits model + moderate context; Q8 is too tight |
| 12GB VRAM | Q6_K or Q8 | 12GB gives Q6_K with long context or Q8 with short context |
| 16GB VRAM | Q8 | Comfortable fit with 8K context headroom |
| 24GB+ VRAM | FP16 | Full precision, maximum output quality |
Q4_K_M is the minimum for good output quality. Q8 is the sweet spot for quality without using FP16’s full VRAM cost.
Best GPUs for Llama 3 70B
This is where GPU selection matters most. At Q4_K_M, the 70B model requires roughly 40GB, which exceeds every single consumer GPU.
| Setup | Quantization | Download size | Fits? | Speed |
|---|---|---|---|---|
| RTX 5090 (32GB) | q2_K | 26GB | Yes | ~22 tok/s |
| RTX 5090 (32GB) | q3_K_S | 31GB | Tight | ~18 tok/s |
| RTX 5090 (32GB) | q3_K_M | 34GB | No — 2GB over | — |
| RTX 5090 (32GB) | q4_K_M | 43GB | No | — |
| 2x RTX 4090 (48GB) | q4_K_M | 43GB | Yes, ~5GB spare | ~15 tok/s |
| 2x RTX 4090 (48GB) | q5_K_M | 50GB | No — 2GB over | — |
| RTX 4090 + CPU offload | q4_K_M | Partial | Slow | ~4 tok/s |
Sizes are the published downloads for llama3:70b, so you can check them before buying anything. Two rows are worth reading twice, because both were wrong here until 2026-09-11 and both would have cost someone a purchase: q3_K_M does not fit a 32GB RTX 5090 (34GB), and q5_K_M does not fit two 4090s (50GB against 48GB, before any KV cache). The step that does fit each is listed above it.
For serious 70B use, dual RTX 4090s running via llama.cpp tensor splitting give you 48GB of fast VRAM and solid throughput at q4_K_M — with about 5GB left for context, which is enough for a short session and not much more. The RTX 5090 handles q2_K or q3_K_S on a single card, but quality degrades noticeably below q4.
For a deeper look at VRAM planning, see our VRAM requirements guide.
Ollama setup tips for Llama 3
Getting Llama 3 running well with Ollama takes a few minutes:
# Pull and run Llama 3 8B
ollama run llama3
# Pull a specific quantization (Q8 for 16GB cards)
ollama pull llama3:8b-instruct-q8_0
# For 70B on dual GPUs, set tensor split
CUDA_VISIBLE_DEVICES=0,1 ollama run llama3:70bOllama automatically selects Q4_K_M by default for the base llama3 tag. If you have 16GB VRAM, pulling the Q8 variant gives measurably better output quality for only ~30% more VRAM usage.
For dual-GPU setups, Ollama handles tensor splitting automatically when both GPUs are visible. You do not need to configure anything manually — it detects the available VRAM and distributes layers accordingly.
Llama 3 405B: not a single-GPU model
The 405B model needs 230GB+ even at Q4, making it multi-node or cloud-only. If you need 405B capability, look at cloud GPU providers or build a dedicated inference cluster. For most users, the 70B model provides excellent quality at a fraction of the hardware cost.
Run Llama 3 405B on RunPod — A100 instances available→Which GPU should you buy for Llama 3?
Running Llama 3 8B for daily chat and coding? → RTX 4060 Ti 16GB ($425). Runs Q8 quantization with room for 8K context. Best price for the most common Llama 3 use case.
Running Llama 3 8B at maximum speed? → RTX 4090 (~$2,200). Hits ~65 tok/s at Q4_K_M, FP16 fits comfortably. The 5090 adds ~30% more speed for $400 more.
Running Llama 3 70B on a single card? → RTX 5090 ($4,900). The only consumer GPU that fits 70B at any usable quantization. Q3_K_M on 32GB, borderline Q2_K.
Running Llama 3 70B at full Q4 quality? → 2x RTX 4090 ($4,400). 48GB combined VRAM, Q4_K_M fits cleanly with context headroom. The power-user choice for 70B.
Need 405B capability? → RunPod cloud. No consumer GPU handles this. A100 80GB instances on RunPod can run 405B at Q4 without the multi-GPU setup complexity.
Llama 3 8B vs 70B: which size should you run?
The 8B model is not just a stepping stone to 70B. For many tasks, 8B at Q8 on a 16GB card outperforms 70B at Q2 on a 32GB card — because quantization quality matters. If you are choosing between Llama 3 8B and Mistral 7B for your workload, our best GPU for Mistral guide offers a side-by-side perspective on where Mistral’s architecture behaves differently under the same hardware.
Choose 8B when:
- You need fast responses (35+ tok/s vs 15-22 tok/s)
- Your tasks are chat, Q&A, summarization, or simple coding
- Budget is a priority
Choose 70B when:
- You need better reasoning and multi-step problem solving
- Code quality at the complex-task level matters
- You have the hardware to run it at Q4 or better
The 70B quality advantage is real but only shows clearly on hard benchmarks. For everyday chat, the 8B model is often “good enough” that users cannot tell the difference.
Common mistakes to avoid
- Buying 8GB VRAM for Llama 3 8B — it does fit at Q4: 4.9GB of weights, half a gigabyte of KV cache at 4K context, and about 1.5GB of runtime, so roughly 6.9GB. What you lose is room to grow. Push the context to 32K and the cache alone is 4GB, and there is nothing left.
- Expecting to run 70B at good quality on a single consumer GPU — even the RTX 5090 limits you to q3_K_S — its 32GB cannot take q3_K_M’s 34GB — and reasoning quality degrades there. Plan for dual GPUs or accept the quality compromise.
- Ignoring bandwidth when comparing GPUs — the RTX 3060 12GB (360 GB/s) produces faster inference than the RTX 4060 8GB (272 GB/s) for the same 8B model. Bandwidth per dollar matters for inference.
- Not accounting for KV cache VRAM — Llama 3 8B at its default 8K context adds about 1GB of cache on top of the weights. It stays small because Llama 3 uses grouped-query attention, 8 key/value heads against 32 query heads; older multi-head models of the same size cost four times as much per token.
Our recommendation
| Your goal | Best GPU | Price |
|---|---|---|
| Llama 3 8B daily driver | RTX 4060 Ti 16GB | ~$425 |
| Llama 3 8B maximum speed | RTX 4090 | ~$2,200 |
| Llama 3 70B (single GPU, Q3) | RTX 5090 | ~$4,900 |
| Llama 3 70B (Q4 quality) | 2x RTX 4090 | ~$4,400 |
| Llama 3 405B | RunPod cloud | Pay per hour |
NVIDIA GeForce RTX 4060 Ti 16GB
16GB GDDR6The most practical Llama 3 GPU for most users. Runs Q8 at 28+ tok/s with full 8K context support.
Affiliate links — we may earn a commission at no extra cost to you. Amazon ships globally; Shopee SG covers Singapore & ASEAN.
If you are running models through Ollama, the same GPU picks apply — Ollama uses llama.cpp under the hood with automatic quantization selection. Want to fine-tune Llama 3 on your own data? The LLM fine-tuning GPU guide covers the additional VRAM overhead LoRA and full fine-tuning require. For the broader local LLM landscape, see our budget GPU guide and VRAM requirements.
Frequently asked questions
How much VRAM do I need for Llama 3 8B?
Llama 3 8B at Q4_K_M is a 4.9GB download for the model weights, plus 1–2GB for an 8K context window. A total of 8GB is the technical minimum, but 12–16GB is recommended so you can use higher quantization (Q8) and longer context without running out of memory.
Can I run Llama 3 70B on one GPU?
Yes, but with limitations. The RTX 5090 with 32GB VRAM can run Llama 3 70B at Q2_K (~25GB) or a very tight Q3_K_M (~32GB). Below Q4_K_M, output quality degrades noticeably — especially on multi-step reasoning tasks. For full Q4_K_M quality, you need dual RTX 4090s (48GB combined).
What’s the best budget GPU for Llama 3?
The RTX 4060 Ti 16GB (~$400) is the best budget option for Llama 3. It runs Llama 3 8B at Q8 quantization (28 tok/s) with 8K context headroom. For ultra-budget, a used RTX 3060 12GB ($250) runs Llama 3 8B at Q4_K_M around 30 tok/s.
How does Llama 3 8B compare to Llama 3 70B in quality?
Llama 3 70B significantly outperforms 8B on complex reasoning, coding, and instruction-following benchmarks. However, for everyday chat, Q&A, and simple tasks, the 8B model at high quantization (Q8) is often indistinguishable. The 70B advantage shows most clearly on multi-step problems and hard coding tasks.
Does Ollama automatically choose the right quantization for my GPU?
Yes. Ollama selects quantization based on your available VRAM when you pull a model. For 16GB GPUs, it typically selects Q4_K_M or Q5_K_M by default. You can override this by explicitly pulling a quantized variant, like ‘ollama pull llama3:8b-instruct-q8_0’, to get Q8 quality on a 16GB card.