Best GPU for Llama Inference (2026 Edition)
How to pick the right GPU for serving Llama 3.x models — by parameter count, batch size, and context length, with cost-per-million-tokens math.
The honest answer to "what GPU should I use for Llama inference?" is: it depends on the model size, your latency target, and whether you'll quantize. Here's the decision tree.
Step 1: Model size dictates VRAM floor
Rough VRAM needed for inference (FP16, no quantization, no KV cache headroom):
| Model | Params | VRAM (FP16) | VRAM (INT8) | VRAM (INT4) |
|---|---|---|---|---|
| Llama 3.2 1B | 1 B | 2 GB | 1 GB | 0.5 GB |
| Llama 3.1 8B | 8 B | 16 GB | 8 GB | 4 GB |
| Llama 3.1 70B | 70 B | 140 GB | 70 GB | 35 GB |
| Llama 3.1 405B | 405 B | 810 GB | 405 GB | 200 GB |
Add 20–40% headroom for KV cache at production batch sizes and context lengths.
Step 2: Match GPU to model
- Llama 8B — A single RTX 4090 (24 GB) is the cost-per-token king. L4 (24 GB) works for higher concurrency at lower power.
- Llama 70B (FP16) — Needs 2× A100 80GB or 2× H100 80GB with NVLink.
- Llama 70B (INT4 / AWQ) — Fits on a single A100 80GB or H100 80GB.
- Llama 405B — 8× H100 80GB minimum, or 8× B200 if you can get them.
Step 3: Batching and throughput
If you're serving lots of users, batching dominates. Tools like vLLM or TensorRT-LLM pay for themselves immediately.
Approximate throughput for Llama 3.1 8B at INT8, batch 32:
- 4090 (24 GB): ~3,000 tok/s
- L40S (48 GB): ~3,800 tok/s
- A100 80GB: ~5,500 tok/s
- H100 80GB: ~11,000 tok/s
Recommendation
- Hobby / low concurrency: RTX 4090 on RunPod Community.
- Production inference for 8B: H100 80GB — best tokens-per-dollar at scale.
- Production inference for 70B: 2× H100 80GB with NVLink, or single A100 with INT4.
Quantize before you scale up the hardware. AWQ and GPTQ are basically free wins on modern Llamas.