This article was originally published on Best GPU for LLM. The full version with interactive tools, FAQ, and live pricing is on the original site.

Quick answer: You need at least 48GB of VRAM to run Llama 70B at usable quality. A single RTX 5090 (32GB) can run it at aggressive Q3/Q4 quantization, but for good quality you'll need dual GPUs or a workstation card like the A6000.

See the recommended pick on the original guide

The VRAM problem with 70B models

Llama 70B is one of the most capable open-source language models available, but it's demanding. Here's how much VRAM it actually needs:

VRAM chart available at the original article

The VRAM column includes overhead for context window and KV cache. Actual usage varies with context length.

GPU options for Llama 70B

Single GPU options

Dual GPU options

See the recommended pick on the original guide

See the recommended pick on the original guide

Best approaches by budget

Budget: Under $2,000 — Dual RTX 3090

The cheapest way to run Llama 70B at decent quality:

• 48GB combined VRAM handles Q4_K_M quantization
• RTX 3090s are widely available used for $800-900 each — see our dual RTX 3090 setup guide for the full build walkthrough
• Ollama and llama.cpp support multi-GPU splitting natively
• Inference speed is slower due to inter-GPU communication

Downsides: Needs a motherboard with two x16 PCIe slots, a beefy PSU (1200W+), and good case airflow. Two cards at 350W each generate serious heat.

Mid-range: $2,000-4,000 — RTX 5090 or dual 4090

Single RTX 5090: Simplest setup. Can run 70B at Q3_K_M, which is usable but you'll notice quality loss on reasoning-heavy tasks. Best if you also use the GPU for smaller models where it excels. For tips on making the most of a single-card 70B setup, see how to run 70B on a single GPU, and for a broader look at the $2,000 tier our best GPU for LLM under $2,000 guide ranks the alternatives.

Dual RTX 4090: 48GB total VRAM for Q4_K_M+ quality. Better output quality than a single 5090, but more complex setup and higher power draw.

High-end: $3,500+ — NVIDIA A6000

The NVIDIA A6000 with 48GB VRAM on a single card is the cleanest solution:

• Runs Q4_K_M and Q5_K_M on one card
• No multi-GPU complexity
• Professional-grade reliability
• ECC memory for consistent results

The downside is price and availability. The A6000 is a professional card with professional pricing.

Ollama setup for multi-GPU

If you go the dual-GPU route, Ollama handles GPU splitting automatically:

OLLAMA_NUM_GPU=999 ollama run llama3:70b-q4_K_M

For llama.cpp, specify the split:

--tensor-split 24,24

Both tools will distribute model layers across available GPUs. Inference speed scales roughly 60-70% of linear with two cards due to communication overhead.

Inference speed expectations

These are approximate for 2048 context length. Longer contexts reduce speed.

Should you even run 70B locally?

Before investing in hardware, consider:

• Is 70B actually better for your use case? For many tasks, a well-prompted 13B or fine-tuned 34B model performs nearly as well.
• Would cloud be cheaper? If you only need 70B occasionally, cloud GPU rental (RunPod, Vast.ai) at $1-2/hour may be more cost-effective than a $3,000+ hardware investment. See RunPod vs Vast.ai for LLM to understand which platform offers better pricing and reliability for this workload, and our cloud GPU TCO vs self-hosted LLM breakdown for the exact monthly break-even math.
• Do you need the privacy? Local inference means your data never leaves your machine. If that matters, the hardware cost is justified.

Which GPU should YOU buy for Llama 70B?

• Running 70B as your primary model? Get 2x RTX 4090 ($3,200). 48GB combined VRAM handles Q4_K_M with good quality and decent speed.
• Running 70B occasionally alongside smaller models? Get an RTX 5090 ($2,000). Handles Q3_K_M for 70B and excels at 7B-34B models the rest of the time.
• Need the best single-card 70B experience? Get an NVIDIA A6000 ($3,500). 48GB on one card means Q4_K_M+ without multi-GPU complexity.
• Only need 70B sometimes? Use cloud GPUs instead. $1-2/hour beats a $3,000+ hardware investment for occasional use.

Common mistakes to avoid

• Buying a single 24GB GPU expecting to run 70B — the RTX 4090 at 24GB can only fit Q2_K quantization, where output quality is significantly degraded. You need 32GB minimum, and realistically 48GB for good results.
• Ignoring memory bandwidth in dual-GPU setups — inter-GPU communication adds latency. Two RTX 3090s (936 GB/s each) outperform two RTX 4060 Tis even if total VRAM is similar, because bandwidth determines token generation speed.
• Not accounting for context length VRAM overhead — at Q4_K_M, Llama 70B uses ~40GB for weights alone. A 4K context window adds 3-5GB for the KV cache. Plan your VRAM budget accordingly. For a full breakdown of exactly how much VRAM each 70B quantization level needs, see how much VRAM for a 70B model.
• Skipping the "do I actually need 70B" question — a well-quantized 34B model on a single RTX 4090 often matches 70B at Q2_K in output quality, at 3x the inference speed and half the hardware cost. Llama 4 Scout is another alternative worth considering — it beats Llama 3 70B on benchmarks and fits on a single RTX 5090; see our Llama 4 Scout GPU guide for details. DeepSeek's reasoning-tuned 32B is another single-card alternative — see our DeepSeek GPU guide for VRAM needs and tok/s on 24GB cards. If you are wondering whether a budget card like the 4060 Ti can even attempt 70B, see can the RTX 4060 Ti run Llama 70B?

Final verdict

See the recommended pick on the original guide

For most people, Llama 70B is not a single-GPU workload at consumer prices. Accept that and plan for either dual GPUs, a workstation card, or cloud.

The best GPU for Llama 70B is the one that gives you enough VRAM to avoid aggressive quantization. Quality degrades fast below Q4 — don't sacrifice output quality to save on hardware.

Related guides on Best GPU for LLM

• Best Budget GPU for Local LLM in 2026 (Under $350)
• Best GPU for 13B Parameter Models in 2026 (Ranked)
• Best GPU for 34B Models: Yi, CodeLlama & Qwen

Continue on Best GPU for LLM for the complete guide with interactive calculators and current GPU prices.