Can the H100 SXM 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.
At Q4_K_M quantization with 131,072 context, the Llama 3.3 70B requires approximately 89.55GB of VRAM. The H100 SXM has 80GB available (9.55GB shortfall).
VRAM Breakdown
| Component | Size (GB) | Source |
|---|---|---|
| Model weights (Q4_K_M) | 42.36 | calculated |
| Loading overhead (10%) | 4.24 | calculated |
| KV cache @ 131,072 context | 42.95 | calculated |
| Total required | 89.55 | |
| GPU memory available | 80 | vendor_spec |
| Headroom / shortfall | -9.55 |
Quantization Options
| Quant | Weights (GB) | Total @ 4K (GB) | Total @ 32K (GB) | Fits H100 SXM? |
|---|---|---|---|---|
| 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 | No |
| FP16 | 141.2 | 156.66 | 166.06 | No |
Measured Speed
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If It Doesn't Fit: Alternatives
Multi-GPU Config
Two of these cards together (~90% parallel efficiency) cover the 89.55GB requirement. See our multi-GPU guide for setup.
Same GPU, Other Models
Frequently Asked Questions
Can the Llama 3.3 70B run on 80GB VRAM?
Yes. At Q4_K_M quantization with 4K context, the Llama 3.3 70B needs approximately 89.55GB. Your 80GB of usable memory has 9.55GB shortfall.
What is the maximum context length I can use on the H100 SXM?
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 ~89.55GB; at 32K it needs more.
Which quantization should I use with the H100 SXM?
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 · H100 SXM 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.