Best GPU for Llama 3 Hosting
Meta’s Llama 3 family ranges from a laptop-friendly 8B to the 405B research-tier giant. The right GPU depends entirely on the variant — 8B is comfortable on a single 24 GB card, 70B needs serious hardware, 405B is multi-node territory.
The short answer: the RTX 5090 is the best GPU for self-hosting Llama 3 (8B family) on a dedicated server. It has the right VRAM (32 GB) for the model, modern tensor cores, and the best cost-per-token in our catalogue for this workload.
Ranking — Best to Worst for This Workload
From best to worst for this specific workload, with the reason in plain English.
RTX 5090 Top Pick (8B)
32 GB fits Llama 3.1 8B FP16 with full 128K context. Best cost-per-token.
32 GB · Blackwell · from £399/mo
RTX 6000 Pro 96 GB Top Pick (70B)
96 GB serves Llama 3.3 70B FP8 single-card with comfortable context.
96 GB · Blackwell · from £899/mo
RTX 3090 Budget 8B
24 GB fits Llama 3.1 8B FP16. Cheapest practical Llama deployment.
24 GB · Ampere · from £159/mo
A100 80 GB 70B FP16
80 GB needed for full FP16 70B serving with NVLink. Production reference.
80 GB · Ampere · POA
RTX 5080 Latency 8B
16 GB at FP8 — best single-stream latency for Llama 3 8B.
16 GB · Blackwell · from £189/mo
Background & Sizing
Llama 3 is the most-deployed open-weight LLM family in the world. Meta has released 3.0, 3.1, 3.2 (multimodal) and 3.3 (text-only 70B refresh). For self-hosting purposes the practical options are 8B, 70B, and the multimodal 11B / 90B variants.
Pick by use case
- General chatbot — Llama 3.1 8B on a 5090 or 3090.
- Coding agent — Consider DeepSeek-Coder instead.
- Research / quality — Llama 3.3 70B on a 6000 Pro or multi-GPU cluster.
- Vision — Llama 3.2 11B Vision needs ~22 GB FP16. Fits a 24 GB card.
Frequently Asked Questions
The questions buyers actually ask before committing to a GPU server.
Llama 3 vs Llama 3.1 vs Llama 3.3 — which to pick?
Llama 3.1 has 128K context (vs 8K). Llama 3.3 70B is the latest text-only refresh with stronger reasoning. For 8B-class, Llama 3.1 is the default.
Can I run Llama 3 70B on a single consumer GPU?
Only at INT4 — fits a 5090 (32 GB) tightly. For FP8 / FP16 you need a 6000 Pro or multi-GPU cluster.
Llama 3 vs Mistral 7B?
Llama 3.1 8B has a longer context (128K vs 32K) and better multilingual. Mistral 7B has stronger function calling. Both fit similar hardware.
405B — is it host-able?
Only on multi-node H100 / H200 clusters with InfiniBand. POA build, 4-6 week lead time.
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