# Llama 3.1 8B GPU requirements: VRAM, GPUs and servers

> Llama 3.1 8B needs about 16 GB of VRAM in BF16, 8.0 GB in FP8 and 4.0 GB in 4-bit, plus KV cache. GPUs and servers that run it, with prices.

Source: https://overgpu.com/models/llama-3-1-8b  
Updated: 2026-11-03  
Publisher: OverGPU (https://overgpu.com/)

**In short:** Yes. In BF16, a single NVIDIA GeForce RTX 5090 (32 GB, $449 a month at OverGPU) runs Llama 3.1 8B with an 8k context and 8 concurrent requests.

## Model facts

- **Developer:** Meta
- **License:** Llama 3.1 Community License
- **Type:** Dense
- **Parameters:** 8B
- **Layers:** 32
- **Attention:** 32 query heads, 8 KV heads, head dimension 128
- **Context window:** 128k tokens

## How much VRAM Llama 3.1 8B needs

Weights take parameters × bytes per parameter; serving adds the KV cache and about 10% runtime overhead. Totals assume an 8k context with 8 concurrent requests and a BF16 KV cache.

| Precision | Weights | Total to serve | Cheapest OverGPU server that fits |
| --- | --- | --- | --- |
| BF16 | 16 GB | 27 GB | NVIDIA GeForce RTX 5090 ($449/month) |
| FP8 | 8.0 GB | 18 GB | NVIDIA GeForce RTX 5090 ($449/month) |
| 4-bit | 4.0 GB | 14 GB | NVIDIA GeForce RTX 5090 ($449/month) |

## Which GPUs can run Llama 3.1 8B?

Yes = 8 concurrent requests with an 8k context each; 1 request = a single 8k request at a time; No = does not fit.

| Configuration | BF16 | FP8 | 4-bit |
| --- | --- | --- | --- |
| NVIDIA GeForce RTX 5090 (32 GB, $449/month) | Yes | Yes | Yes |
| NVIDIA L40S (48 GB, $890/month) | Yes | Yes | Yes |
| NVIDIA H100 SXM (80 GB, $2,190/month) | Yes | Yes | Yes |
| NVIDIA RTX PRO 6000 Blackwell (96 GB, $1,390/month) | Yes | Yes | Yes |
| NVIDIA H200 (141 GB, $3,090/month) | Yes | Yes | Yes |
| NVIDIA B200 (180 GB, $4,590/month) | Yes | Yes | Yes |
| NVIDIA B300 (288 GB, $5,190/month) | Yes | Yes | Yes |
| 8× NVIDIA HGX H100 (8 × 80 GB, $16,900/month) | Yes | Yes | Yes |
| 8× NVIDIA RTX PRO 6000 Blackwell (8 × 96 GB, $10,490/month) | Yes | Yes | Yes |
| 8× NVIDIA HGX H200 (8 × 141 GB, $23,900/month) | Yes | Yes | Yes |
| 8× NVIDIA HGX B200 (8 × 180 GB, $34,900/month) | Yes | Yes | Yes |
| 8× NVIDIA HGX B300 (8 × 288 GB, $39,900/month) | Yes | Yes | Yes |
| 8× AMD Instinct MI355X (8 × 288 GB, $19,900/month) | Yes | Yes | Yes |

## Llama 3.1 8B KV cache by context length

About 131 MB per 1,000 tokens in flight with a BF16 cache.

| Context per request | 1 request | 8 requests | 32 requests |
| --- | --- | --- | --- |
| 4k tokens | 537 MB | 4.3 GB | 17 GB |
| 32k tokens | 4.3 GB | 34 GB | 137 GB |
| 128k tokens | 17 GB | 137 GB | 550 GB |

## Memory to fine-tune Llama 3.1 8B

| Method | Approximate memory | Cheapest OverGPU server that fits |
| --- | --- | --- |
| QLoRA (4-bit base) | 6.1 GB | NVIDIA GeForce RTX 5090 ($449/month) |
| LoRA (BF16 base) | 20 GB | NVIDIA GeForce RTX 5090 ($449/month) |
| Full fine-tune (AdamW, mixed precision) | 154 GB | NVIDIA B200 ($4,590/month) |

## About Llama 3.1 8B

Meta’s Llama 3.1 8B is the small dense model many teams start with: 32 layers, grouped-query attention with 8 KV heads and a 128k-token context window. It is small enough to serve in BF16 on a single card, so the real sizing question is how much KV cache you need for your context length and concurrency.

Every figure comes from the model's published architecture and the formulas of the VRAM calculator (https://overgpu.com/tools/vram-calculator).

## Frequently asked questions

### How much VRAM does Llama 3.1 8B need?

About 16 GB for the weights in BF16, 8.0 GB in FP8 and 4.0 GB in 4-bit, plus the KV cache: about 131 MB per 1,000 tokens in flight with a BF16 cache. Serving it in BF16 with an 8k context and 8 concurrent requests takes about 27 GB in total.

### Can Llama 3.1 8B run on a single GPU?

Yes. In BF16, a single NVIDIA GeForce RTX 5090 (32 GB) serves it with an 8k context and 8 concurrent requests. Longer contexts or more users need more memory for the KV cache, so check your case in the VRAM calculator.

### What is the cheapest OverGPU server for Llama 3.1 8B?

For BF16 serving with an 8k context and 8 concurrent requests, the NVIDIA GeForce RTX 5090 at $449 per month is the lowest-priced configuration that fits. For other contexts and concurrency, size it in the VRAM calculator.

### How much memory do I need to fine-tune Llama 3.1 8B?

Roughly 6.1 GB with QLoRA, 20 GB with LoRA on BF16 weights, and 154 GB for a full fine-tune with AdamW in mixed precision, before tuning batch size and sequence length. Full fine-tunes of large models are sharded across GPUs with FSDP or ZeRO-3.

## Related pages

- [LLM VRAM calculator: will your model fit on a GPU?](https://overgpu.com/tools/vram-calculator): Pick Llama, Qwen, Mistral or gpt-oss, set the precision and context length, and see weights, KV cache and which GPUs fit.
- [How much VRAM do I need for an LLM?](https://overgpu.com/blog/vram-for-llms): Quick answers for 7B, 14B, 32B, 70B and 405B models at BF16, FP8 and 4-bit, plus KV cache by context and the GPU that fits.
- [Best GPU for LLM inference in 2026: a sizing guide by model size](https://overgpu.com/blog/best-gpu-for-llm-inference): Use our 2026 sizing table, from RTX 5090 to 8× B300, to choose.
- [LLM GPU requirements](https://overgpu.com/models): VRAM, KV cache and the cheapest servers for Llama, Qwen3, Mistral and gpt-oss models.