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Can the NVIDIA RTX PRO 6000 Blackwell 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.

Fits comfortably

At Q4_K_M quantization with 32,768 context, the Llama 3.3 70B requires approximately 57.34GB of VRAM. The NVIDIA RTX PRO 6000 Blackwell has 96GB available (38.66GB headroom).

VRAM Breakdown

ComponentSize (GB)Source
Model weights (Q4_K_M)42.36calculated
Loading overhead (10%)4.24calculated
KV cache @ 32,768 context10.74calculated
Total required57.34
GPU memory available96vendor_spec
Headroom / shortfall+38.66

Quantization Options

QuantWeights (GB)Total @ 4K (GB)Total @ 32K (GB)Fits NVIDIA RTX PRO 6000 Blackwell?
Q4_K_M 42.36 47.94 57.34 Yes
Q5_K_M 50.3 56.67 66.07 Yes
Q6_K 58.25 65.42 74.82 Yes
Q8_0 75.01 83.85 93.25 Yes
FP16 141.2 156.66 166.06 No

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If It Doesn't Fit: Alternatives

H100 SXM 80GB VRAM
H200 SXM 141GB VRAM
NVIDIA B200 192GB VRAM
AMD Instinct MI300X 192GB VRAM

Same GPU, Other Models

Frequently Asked Questions

Can the Llama 3.3 70B run on 96GB VRAM?

Yes. At Q4_K_M quantization with 4K context, the Llama 3.3 70B needs approximately 57.34GB. Your 96GB of usable memory has 38.66GB headroom.

What is the maximum context length I can use on the NVIDIA RTX PRO 6000 Blackwell?

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 ~57.34GB; at 32K it needs more.

Which quantization should I use with the NVIDIA RTX PRO 6000 Blackwell?

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.

Related Pages

Full Llama 3.3 70B fit page · All AI models · NVIDIA RTX PRO 6000 Blackwell 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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