Cheapest way to run Llama 3.1 8B Instruct
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Llama 3.1 8B Instruct needs 6.0 GB at 4-bit — cheapest rental $0.06/hr.
Quick answer
At 4-bit with an 8K-token conversation, Llama 3.1 8B Instruct needs about 6.0 GB. It fits a RTX 4060 (8 GB), so if you already own one it costs nothing per token; the cheapest rental big enough is RTX 3060 on Vast.ai at $0.06 an hour; Novita AI hosts it for $0.02 per 1M input tokens.
| Option | What you need | Cost | Notes |
|---|---|---|---|
| Your own GPU | RTX 4060 (8 GB) at 4-bit (Q4_K_M), needs 6.0 GB | No GPU purchase prices in our data. Check price on Amazon Amazon affiliate link | $0 per token or hour once you own the card (power not counted) |
| Mac (unified memory) | Mac, 16 GB at 8-bit (Q8_0), needs 9.6 GB | No Mac prices in our data | $0 per token once you own it |
| Rent a GPU | RTX 3060 (12 GB total) on Vast.ai Referral link, Marketplace, verified on-demand | $0.06 per hour | Pay only while it runs. Price checked 2026-11-09 |
| Hosted API | Novita AI: Llama 3.1 8B Instruct Referral link | $0.02 in / $0.05 out per 1M tokens | Pay per token, nothing to set up. Price checked Nov 3 2026 |
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 3.1 8B 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.
Links marked Referral link, Affiliate partner or Amazon affiliate link may earn us credit or a commission at no cost to you. Disclosure.
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) | 17.1 GB | RTX 3090 (24 GB) | Mac, 24 GB at 16-bit (FP16/BF16) | RTX A5000 (24 GB) on Runpod, $0.16/hr | RTX A5000 (24 GB) on Runpod, $0.16/hr |
| 8-bit (Q8_0) | 9.6 GB | RTX 3060 (12 GB) | Mac, 16 GB at 8-bit (Q8_0) | RTX 3060 (12 GB) on Vast.ai, $0.06/hr | RTX 3060 (12 GB) on Vast.ai, $0.06/hr |
| 4-bit (Q4_K_M) | 6.0 GB | RTX 4060 (8 GB) | Mac, 16 GB at 4-bit (Q4_K_M) | RTX 3060 (12 GB) on Vast.ai, $0.06/hr | RTX 3060 (12 GB) on Vast.ai, $0.06/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 | 6.0 GB | RTX 3060 (12 GB) on Vast.ai, $0.06/hr |
| 32K tokens | 9.2 GB | RTX 3060 (12 GB) on Vast.ai, $0.06/hr |
| 128K tokens | 22.1 GB | RTX A6000 (48 GB) on Runpod, $0.33/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 (RTX 3060 (12 GB) on Vast.ai, $0.06/hr), $100 of rental buys about 1667 hours of running Llama 3.1 8B Instruct at 4-bit with 8K context. Electricity, storage and setup time are not counted.
Fit quirks for this model
- 32 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 2026-10-23; rental prices checked 2026-11-09. For file sizes, sources and the full fit table see the Llama 3.1 8B Instruct VRAM requirements page; this page is only about cost.
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