# Qwen3 14B GPU requirements: VRAM, GPUs and servers

> Qwen3 14B needs about 30 GB of VRAM in BF16, 15 GB in FP8 and 7.4 GB in 4-bit, plus KV cache. GPUs and servers that run it, with prices.

Source: https://overgpu.com/models/qwen3-14b  
Updated: 2026-11-03  
Publisher: OverGPU (https://overgpu.com/)

**In short:** Yes. In BF16, a single NVIDIA H100 SXM (80 GB, $2,190 a month at OverGPU) runs Qwen3 14B with an 8k context and 8 concurrent requests; one NVIDIA L40S (48 GB) holds a single 8k request.

## Model facts

- **Developer:** Alibaba Qwen
- **License:** Apache 2.0
- **Type:** Dense
- **Parameters:** 14.8B
- **Layers:** 40
- **Attention:** 40 query heads, 8 KV heads, head dimension 128
- **Context window:** 32k tokens native, about 128k with YaRN

## How much VRAM Qwen3 14B 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 | 30 GB | 44 GB | NVIDIA RTX PRO 6000 Blackwell ($1,390/month) |
| FP8 | 15 GB | 28 GB | NVIDIA GeForce RTX 5090 ($449/month) |
| 4-bit | 7.4 GB | 20 GB | NVIDIA GeForce RTX 5090 ($449/month) |

## Which GPUs can run Qwen3 14B?

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) | No | Yes | Yes |
| NVIDIA L40S (48 GB, $890/month) | 1 request | 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 |

## Qwen3 14B KV cache by context length

About 164 MB per 1,000 tokens in flight with a BF16 cache.

| Context per request | 1 request | 8 requests | 32 requests |
| --- | --- | --- | --- |
| 4k tokens | 671 MB | 5.4 GB | 21 GB |
| 32k tokens | 5.4 GB | 43 GB | 172 GB |
| 128k tokens | 21 GB | 172 GB | 687 GB |

## Memory to fine-tune Qwen3 14B

| Method | Approximate memory | Cheapest OverGPU server that fits |
| --- | --- | --- |
| QLoRA (4-bit base) | 11 GB | NVIDIA GeForce RTX 5090 ($449/month) |
| LoRA (BF16 base) | 37 GB | NVIDIA L40S ($890/month) |
| Full fine-tune (AdamW, mixed precision) | 284 GB | 8× NVIDIA RTX PRO 6000 Blackwell ($10,490/month) |

## About Qwen3 14B

Qwen3 14B is a dense model with 40 layers and grouped-query attention (8 KV heads), released under Apache 2.0. Its native context is 32k tokens and can be extended to about 128k with YaRN scaling, which multiplies the KV cache accordingly.

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 Qwen3 14B need?

About 30 GB for the weights in BF16, 15 GB in FP8 and 7.4 GB in 4-bit, plus the KV cache: about 164 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 44 GB in total.

### Can Qwen3 14B run on a single GPU?

Yes. In BF16, a single NVIDIA RTX PRO 6000 Blackwell (96 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 Qwen3 14B?

For BF16 serving with an 8k context and 8 concurrent requests, the NVIDIA RTX PRO 6000 Blackwell at $1,390 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 Qwen3 14B?

Roughly 11 GB with QLoRA, 37 GB with LoRA on BF16 weights, and 284 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.