Can the GeForce RTX 5090 run Llama 3.3 70B?

Llama 3.3 70B needs a GPU with 48 GB of VRAM to run locally at Q4_K_M with a 4k-token context, requiring about 47.9 GB of VRAM. Llama 3.3 70B uses the same 70.6-billion-parameter architecture as Llama 3.1 70B, so every VRAM figure and GPU-fit result on this page applies to both models. An 80 GB GPU such as the NVIDIA H100 80GB runs Llama 3.3 70B comfortably.

Needs offload or 2nd GPU

At Q4_K_M quantization with 4,096 context, the Llama 3.3 70B requires approximately 47.94GB of VRAM. The GeForce RTX 5090 has 32GB available (15.94GB shortfall).

4,096 tokens 16,384 tokens 32,768 tokens 131,072 tokens

VRAM Breakdown

Component	Size (GB)	Source
Model weights (Q4_K_M)	42.36	calculated
Loading overhead (10%)	4.24	calculated
KV cache @ 4,096 context	1.34	calculated
Total required	47.94
GPU memory available	32	vendor_spec
Headroom / shortfall	-15.94

Quantization Options

Quant	Weights (GB)	Total @ 4K (GB)	Total @ 32K (GB)	Fits GeForce RTX 5090?
Q4_K_M	42.36	47.94	57.34	No
Q5_K_M	50.3	56.67	66.07	No
Q6_K	58.25	65.42	74.82	No
Q8_0	75.01	83.85	93.25	No
FP16	141.2	156.66	166.06	No

Measured Speed

42 tok/s

llama.cpp · Q4 · 32GB VRAM, tight fit — editorial estimate pending article-level source verification
Tested: 2025-06-01 · Source (Estimated, confidence 0.40)

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If It Doesn't Fit: Alternatives

H100 SXM 80GB VRAM

NVIDIA A100 80GB SXM 80GB VRAM

NVIDIA H100 PCIe 80GB 80GB VRAM

NVIDIA RTX PRO 6000 Blackwell 96GB VRAM

H200 SXM 141GB VRAM

NVIDIA B200 192GB VRAM

Multi-GPU Config

Two of these cards together (~90% parallel efficiency) cover the 47.94GB requirement. See our multi-GPU guide for setup.

Same GPU, Other Models

DeepSeek R1-Distill 32B FLUX.1 dev Gemma 3 27B Llama 3.1 8B Mistral Small 3.2 24B Qwen 3 32B Whisper large-v3

Frequently Asked Questions

Can the Llama 3.3 70B run on 32GB VRAM?

Yes. At Q4_K_M quantization with 4K context, the Llama 3.3 70B needs approximately 47.94GB. Your 32GB of usable memory has 15.94GB shortfall.

What is the maximum context length I can use on the GeForce RTX 5090?

The Llama 3.3 70B supports up to 131,072 tokens of context. Larger context increases KV-cache memory requirements. At 4K context the model needs ~47.94GB; at 32K it needs more.

Which quantization should I use with the GeForce RTX 5090?

Q4_K_M is the recommended starting point — it offers the best speed-to-quality tradeoff. If you have VRAM headroom, Q6_K or Q8_0 improves quality at the cost of speed. FP16 is only for inference servers with ample memory.

Does Llama 3.3 70B need the same VRAM as Llama 3.1 70B?

Yes. Llama 3.3 70B has the identical 70.6-billion-parameter architecture, layer count, and attention configuration as Llama 3.1 70B, so the VRAM requirement is the same: about 47.9 GB at Q4_K_M with a 4k context.

Can Llama 3.3 70B run on 24 GB of VRAM?

Not fully. At Q4_K_M the weights alone are 42.36 GB, so a 24 GB GPU such as the RTX 4090 or RTX 3090 must offload most layers to system RAM, which reduces speed to roughly 1–2 tokens per second. A 48 GB professional GPU is the practical minimum.

Full Llama 3.3 70B fit page · All AI models · GeForce RTX 5090 specs · VRAM calculator

VRAM figures are calculated from model architecture parameters using established formulas. Benchmark data is measured and cited per row. GPU memory is vendor_spec from manufacturer datasheets.

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