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- DeepSeek 670B BF16 Training Performance - GB200 NVL72 vs H10

DeepSeek 670B BF16 Training Performance - GB200 NVL72 vs H100
The numbers behind a chart SemiAnalysis published, redrawn natively. Check the original, study the data, then make your own.
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Drawn by us from the numbers below. Hover, tap or use the arrow keys.A simplified redraw: it shows the numbers in our own look, not the look of the original. One accent marks what the chart is about. Values marked with ~ in the table are estimates.
The numbers
First 10 of 120 points are free| Group | Series | Value | Source | Conf. |
|---|---|---|---|---|
| GPU SKU | Units | ~0 | Estimate | 0.80 |
| GPU SKU | H100 | ~0 | Estimate | 0.30 |
| GPU SKU | GB200 NVL72 | ~0 | Estimate | 0.30 |
| GPU SKU | GB200 NVL72 | ~0 | Estimate | 0.30 |
| GPU SKU | GB200 NVL72 | ~0 | Estimate | 0.30 |
| GPU SKU | H100 | ~0 | Estimate | 0.30 |
| GPU SKU | GB200 NVL72 | ~0 | Estimate | 0.30 |
| GPU SKU | GB200 NVL72 | ~0 | Estimate | 0.30 |
| Global Batch Size | Units | ~0 | Estimate | 0.30 |
| Global Batch Size | H100 | ~8192 | Estimate | 0.30 |

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| Global Batch Size | GB200 NVL72 | ~1024 | Estimate | 0.30 |
|---|---|---|---|---|
| Global Batch Size | GB200 NVL72 | ~2048 | Estimate | 0.30 |
| Global Batch Size | GB200 NVL72 | ~2048 | Estimate | 0.30 |
| Global Batch Size | H100 | ~8192 | Estimate | 0.30 |
| Global Batch Size | GB200 NVL72 | ~4096 | Estimate | 0.30 |
| Global Batch Size | GB200 NVL72 | ~8192 | Estimate | 0.30 |
| Token/s/GPU | Units | ~0 | Estimate | 0.30 |
| Token/s/GPU | H100 | ~630 | Estimate | 0.30 |
| Token/s/GPU | GB200 NVL72 | ~956 | Estimate | 0.30 |
| Token/s/GPU | GB200 NVL72 | ~958 | Estimate | 0.30 |
| Token/s/GPU | GB200 NVL72 | ~959 | Estimate | 0.30 |
| Token/s/GPU | H100 | ~842 | Estimate | 0.30 |
| Token/s/GPU | GB200 NVL72 | ~2138 | Estimate | 0.30 |
| Token/s/GPU | GB200 NVL72 | ~3957 | Estimate | 0.30 |
| BF16 Model Flops Utilization (MFU) | Units | ~0 | Estimate | 0.30 |
| BF16 Model Flops Utilization (MFU) | H100 | ~16.6 | Estimate | 0.30 |
| BF16 Model Flops Utilization (MFU) | GB200 NVL72 | ~10.1 | Estimate | 0.30 |
| BF16 Model Flops Utilization (MFU) | GB200 NVL72 | ~10.17 | Estimate | 0.30 |
| BF16 Model Flops Utilization (MFU) | GB200 NVL72 | ~10.18 | Estimate | 0.30 |
| BF16 Model Flops Utilization (MFU) | H100 | ~22 | Estimate | 0.30 |
| BF16 Model Flops Utilization (MFU) | GB200 NVL72 | ~22.7 | Estimate | 0.30 |
| BF16 Model Flops Utilization (MFU) | GB200 NVL72 | ~42 | Estimate | 0.30 |
| BF16 Cost Per Million Tokens | Units | ~0 | Estimate | 0.30 |
| BF16 Cost Per Million Tokens | H100 | ~0.468 | Estimate | 0.30 |
| BF16 Cost Per Million Tokens | GB200 NVL72 | ~0.166 | Estimate | 0.30 |
| BF16 Cost Per Million Tokens | GB200 NVL72 | ~0.307 | Estimate | 0.30 |
| BF16 Cost Per Million Tokens | GB200 NVL72 | ~0.684 | Estimate | 0.30 |
| BF16 Cost Per Million Tokens | H100 | ~0.626 | Estimate | 0.30 |
| BF16 Cost Per Million Tokens | GB200 NVL72 | ~0.684 | Estimate | 0.30 |
| BF16 Cost Per Million Tokens | GB200 NVL72 | ~0.686 | Estimate | 0.30 |
| BF16 All-In Utility Joules per Token | Units | ~0 | Estimate | 0.30 |
| BF16 All-In Utility Joules per Token | H100 | ~2.12 | Estimate | 0.30 |
| BF16 All-In Utility Joules per Token | GB200 NVL72 | ~0.53 | Estimate | 0.30 |
| BF16 All-In Utility Joules per Token | GB200 NVL72 | ~0.97 | Estimate | 0.30 |
| BF16 All-In Utility Joules per Token | GB200 NVL72 | ~2.17 | Estimate | 0.30 |
| BF16 All-In Utility Joules per Token | H100 | ~2.84 | Estimate | 0.30 |
| BF16 All-In Utility Joules per Token | GB200 NVL72 | ~2.17 | Estimate | 0.30 |
| BF16 All-In Utility Joules per Token | GB200 NVL72 | ~2.17 | Estimate | 0.30 |
| BF16 Tokens per annual US HH energy use | Units | ~0 | Estimate | 0.30 |
| BF16 Tokens per annual US HH energy use¹ | H100 | ~13.68 | Estimate | 0.30 |
| BF16 Tokens per annual US HH energy use¹ | GB200 NVL72 | ~17.88 | Estimate | 0.30 |
| BF16 Tokens per annual US HH energy use¹ | GB200 NVL72 | ~17.92 | Estimate | 0.30 |
| BF16 Tokens per annual US HH energy use¹ | GB200 NVL72 | ~17.93 | Estimate | 0.30 |
| BF16 Tokens per annual US HH energy use¹ | H100 | ~18.3 | Estimate | 0.30 |
| BF16 Tokens per annual US HH energy use¹ | GB200 NVL72 | ~39.97 | Estimate | 0.30 |
| BF16 Tokens per annual US HH energy use¹ | GB200 NVL72 | ~73.99 | Estimate | 0.30 |
| GPU SKU | Units | ~0 | Estimate | 0.50 |
| GPU SKU | H100 | ~0 | Estimate | 0.50 |
| GPU SKU | GB200 NVL72 | ~0 | Estimate | 0.50 |
| GPU SKU | GB200 NVL72 | ~0 | Estimate | 0.50 |
| GPU SKU | GB200 NVL72 | ~0 | Estimate | 0.50 |
| GPU SKU | H100 | ~0 | Estimate | 0.50 |
| GPU SKU | GB200 NVL72 | ~0 | Estimate | 0.50 |
| GPU SKU | GB200 NVL72 | ~0 | Estimate | 0.50 |
| Global Batch Size | Units | ~0 | Estimate | 0.50 |
| Global Batch Size | H100 | ~0 | Estimate | 0.50 |
| Global Batch Size | GB200 NVL72 | ~0 | Estimate | 0.50 |
| Global Batch Size | GB200 NVL72 | ~0 | Estimate | 0.50 |
| Global Batch Size | GB200 NVL72 | ~0 | Estimate | 0.50 |
| Global Batch Size | H100 | ~0 | Estimate | 0.50 |
| Global Batch Size | GB200 NVL72 | ~0 | Estimate | 0.50 |
| Global Batch Size | GB200 NVL72 | ~0 | Estimate | 0.50 |
| Token/s/GPU | Units | ~0 | Estimate | 0.50 |
| Token/s/GPU | H100 | ~0 | Estimate | 0.50 |
| Token/s/GPU | GB200 NVL72 | ~0 | Estimate | 0.50 |
| Token/s/GPU | GB200 NVL72 | ~0 | Estimate | 0.50 |
| Token/s/GPU | GB200 NVL72 | ~0 | Estimate | 0.50 |
| Token/s/GPU | H100 | ~0 | Estimate | 0.50 |
| Token/s/GPU | GB200 NVL72 | ~0 | Estimate | 0.50 |
| Token/s/GPU | GB200 NVL72 | ~0 | Estimate | 0.50 |
| BF16 Model Flops Utilization (MFU) | Units | ~0 | Estimate | 0.50 |
| BF16 Model Flops Utilization (MFU) | H100 | ~0 | Estimate | 0.50 |
| BF16 Model Flops Utilization (MFU) | GB200 NVL72 | ~0 | Estimate | 0.50 |
| BF16 Model Flops Utilization (MFU) | GB200 NVL72 | ~0 | Estimate | 0.50 |
| BF16 Model Flops Utilization (MFU) | GB200 NVL72 | ~0 | Estimate | 0.50 |
| BF16 Model Flops Utilization (MFU) | H100 | ~0 | Estimate | 0.50 |
| BF16 Model Flops Utilization (MFU) | GB200 NVL72 | ~0 | Estimate | 0.50 |
| BF16 Model Flops Utilization (MFU) | GB200 NVL72 | ~0 | Estimate | 0.50 |
| BF16 Cost Per Million Tokens | Units | ~0 | Estimate | 0.50 |
| BF16 Cost Per Million Tokens | H100 | ~0 | Estimate | 0.50 |
| BF16 Cost Per Million Tokens | GB200 NVL72 | ~0 | Estimate | 0.50 |
| BF16 Cost Per Million Tokens | GB200 NVL72 | ~0 | Estimate | 0.50 |
| BF16 Cost Per Million Tokens | GB200 NVL72 | ~0 | Estimate | 0.50 |
| BF16 Cost Per Million Tokens | H100 | ~0 | Estimate | 0.50 |
| BF16 Cost Per Million Tokens | GB200 NVL72 | ~0 | Estimate | 0.50 |
| BF16 Cost Per Million Tokens | GB200 NVL72 | ~0 | Estimate | 0.50 |
| BF16 All-In Utility Joules per Token | Units | ~0 | Estimate | 0.50 |
| BF16 All-In Utility Joules per Token | H100 | ~0 | Estimate | 0.50 |
| BF16 All-In Utility Joules per Token | GB200 NVL72 | ~0 | Estimate | 0.50 |
| BF16 All-In Utility Joules per Token | GB200 NVL72 | ~0 | Estimate | 0.50 |
| BF16 All-In Utility Joules per Token | GB200 NVL72 | ~0 | Estimate | 0.50 |
| BF16 All-In Utility Joules per Token | H100 | ~0 | Estimate | 0.50 |
| BF16 All-In Utility Joules per Token | GB200 NVL72 | ~0 | Estimate | 0.50 |
| BF16 All-In Utility Joules per Token | GB200 NVL72 | ~0 | Estimate | 0.50 |
| BF16 Tokens per annual US HH energy use | Units | ~0 | Estimate | 0.50 |
| BF16 Tokens per annual US HH energy use | H100 | ~0 | Estimate | 0.50 |
| BF16 Tokens per annual US HH energy use | GB200 NVL72 | ~0 | Estimate | 0.50 |
| BF16 Tokens per annual US HH energy use | GB200 NVL72 | ~0 | Estimate | 0.50 |
| BF16 Tokens per annual US HH energy use | GB200 NVL72 | ~0 | Estimate | 0.50 |
| BF16 Tokens per annual US HH energy use | H100 | ~0 | Estimate | 0.50 |
| BF16 Tokens per annual US HH energy use | GB200 NVL72 | ~0 | Estimate | 0.50 |
| BF16 Tokens per annual US HH energy use | GB200 NVL72 | ~0 | Estimate | 0.50 |
| GPU SKU | # | ~0 | Estimate | 0.50 |
| Number of GPUs | H100 | ~1024 | Estimate | 0.50 |
| Number of GPUs | GB200 NVL72 | ~128 | Estimate | 0.50 |
| Number of GPUs | GB200 NVL72 | ~256 | Estimate | 0.50 |
| Number of GPUs | GB200 NVL72 | ~512 | Estimate | 0.50 |
| Number of GPUs | H100 | ~1024 | Estimate | 0.50 |
| Number of GPUs | GB200 NVL72 | ~256 | Estimate | 0.50 |
| Number of GPUs | GB200 NVL72 | ~1024 | Estimate | 0.50 |
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On the Max plan you can pull this chart's numbers with the provenance of each one, as JSON or CSV, with its redraw spec, or call it from an agent over MCP. Chart id 65d06ddd5bcd.
Approximate values. Our two readings of this chart agreed only in part (confidence 0.41 of 1), so check the original before you quote a number. The API marks it lowConfidence, and ?min_confidence=0.5 leaves it out.
| Path | Returns | Plan | |
|---|---|---|---|
| GET | /api/v1/charts/65d06ddd5bcd | Title, publisher, date, form, point count; with a Max key, the data link | Open |
| GET | /api/v1/charts/65d06ddd5bcd/data | Every point with its provenance and confidence, plus the redraw spec. Add ?format=csv for CSV | Max |
| GET | /api/v1/charts/65d06ddd5bcd/render?view=1 | The draw-only view this page uses | Open |
| POST | /mcp | Tool get_chart_data with this id, for Claude, Cursor and other agents | Max |
curl -s https://viral-chart.com/api/v1/charts/65d06ddd5bcd/data \
-H "Authorization: Bearer $VIRALCHART_KEY"claude mcp add --transport http viralchart https://viral-chart.com/mcp \
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They are estimated from the image by two independent passes, with confidence 0.41 on a 0 to 1 scale. Printed numbers read well; values read off an axis can be a few percent off.
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