Cheapest way to run Llama 3.3 70B Instruct
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Llama 3.3 70B Instruct needs 45.2 GB at 4-bit — cheapest rental $0.40/hr.
Quick answer
At 4-bit with an 8K-token conversation, Llama 3.3 70B Instruct needs about 45.2 GB. The cheapest rental big enough is A100 PCIe on Vast.ai at $0.40 an hour.
| Option | What you need | Cost | Notes |
|---|---|---|---|
| Your own GPU | Doesn't fit any single consumer card in our list (largest 32 GB) at 4-bit with 8K context | – | – |
| Mac (unified memory) | Mac, 64 GB at 4-bit (Q4_K_M), needs 45.2 GB | No Mac prices in our data | $0 per token once you own it |
| Rent a GPU | A100 PCIe (80 GB total) on Vast.ai Referral link, Marketplace, verified on-demand | $0.40 per hour | Pay only while it runs. Price checked 2026-11-09 |
Fit rule: the Q4_K_M file (or the highest precision that fits) plus an 8K-token FP16 KV cache under 90% of memory, same as the calculator. Sizes and sources: Llama 3.3 70B Instruct VRAM page. Rental prices: GPU prices (checked 2026-11-09). Hourly and per-token prices measure different things, so which is cheaper depends on how much you use it.
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Precision ladder: what each quant costs
| Precision | Needs at 8K | Smallest consumer GPU | Smallest Mac | Cheapest single-GPU rental | Cheapest rental overall (any GPU count) |
|---|---|---|---|---|---|
| 16-bit (FP16/BF16) | 143.8 GB | none (max 32 GB) | Mac Studio, 256 GB at 16-bit (FP16/BF16) | B200 (180 GB) on Runpod, $5.98/hr | 4× RTX A6000 (192 GB) on Lambda, $4.36/hr |
| 8-bit (Q8_0) | 77.7 GB | none (max 32 GB) | Mac, 96 GB at 8-bit (Q8_0) | RTX Pro 6000 (96 GB) on Runpod, $1.69/hr | RTX Pro 6000 (96 GB) on Runpod, $1.69/hr |
| 4-bit (Q4_K_M) | 45.2 GB | none (max 32 GB) | Mac, 64 GB at 4-bit (Q4_K_M) | A100 PCIe (80 GB) on Vast.ai, $0.40/hr | A100 PCIe (80 GB) on Vast.ai, $0.40/hr |
Every row names its precision, Mac rows included (Macs share unified memory with the system, so the same 90% rule applies). Single-GPU and multi-GPU rentals are listed separately: a multi-GPU rental needs software that splits the model across cards (llama.cpp, vLLM tensor parallel). Prices checked 2026-11-09.
How context length changes the bill (4-bit)
| Context | Needs | Cheapest rental overall |
|---|---|---|
| 8K tokens | 45.2 GB | A100 PCIe (80 GB) on Vast.ai, $0.40/hr |
| 32K tokens | 53.3 GB | A100 PCIe (80 GB) on Vast.ai, $0.40/hr |
| 128K tokens | 85.5 GB | RTX Pro 6000 (96 GB) on Runpod, $1.69/hr |
What $100 of rental buys you
We list no GPU purchase prices (they change daily and we cannot check them), so this page does not compare renting with buying. At the cheapest rental that fits (A100 PCIe (80 GB) on Vast.ai, $0.40/hr), $100 of rental buys about 250 hours of running Llama 3.3 70B Instruct at 4-bit with 8K context. Electricity, storage and setup time are not counted.
Fit quirks for this model
- 80 layers × 8 KV heads × 128 head dim × 2 (K and V) × 2 bytes (FP16).
- Maximum context is 131,072 tokens; longer settings are capped.
Model data checked 2026-10-31; rental prices checked 2026-11-09. For file sizes, sources and the full fit table see the Llama 3.3 70B Instruct VRAM requirements page; this page is only about cost.
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