|
--- |
|
license: mit |
|
tags: |
|
- deepseek |
|
- int8 |
|
- vllm |
|
- llmcompressor |
|
base_model: deepseek-ai/DeepSeek-R1-Distill-Llama-70B |
|
library_name: transformers |
|
--- |
|
|
|
# DeepSeek-R1-Distill-Llama-70B-quantized.w8a8 |
|
|
|
## Model Overview |
|
- **Model Architecture:** LlamaForCausalLM |
|
- **Input:** Text |
|
- **Output:** Text |
|
- **Model Optimizations:** |
|
- **Weight quantization:** INT8 |
|
- **Activation quantization:** INT8 |
|
- **Release Date:** 2/3/2025 |
|
- **Version:** 1.0 |
|
- **Model Developers:** Neural Magic |
|
|
|
Quantized version of [DeepSeek-R1-Distill-Llama-70B](https://huggingface.co./deepseek-ai/DeepSeek-R1-Distill-Llama-70B). |
|
|
|
|
|
### Model Optimizations |
|
|
|
This model was obtained by quantizing the weights and activations of [DeepSeek-R1-Distill-Llama-70B](https://huggingface.co./deepseek-ai/DeepSeek-R1-Distill-Llama-70B) to INT8 data type. |
|
This optimization reduces the number of bits used to represent weights and activations from 16 to 8, reducing GPU memory requirements (by approximately 50%) and increasing matrix-multiply compute throughput (by approximately 2x). |
|
Weight quantization also reduces disk size requirements by approximately 50%. |
|
|
|
Only the weights and activations of the linear operators within transformers blocks are quantized. |
|
Weights are quantized using a symmetric per-channel scheme, whereas quantizations are quantized using a symmetric per-token scheme. |
|
The [GPTQ](https://arxiv.org/abs/2210.17323) algorithm is applied for quantization, as implemented in the [llm-compressor](https://github.com/vllm-project/llm-compressor) library. |
|
|
|
|
|
## Use with vLLM |
|
|
|
This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/latest/) backend, as shown in the example below. |
|
|
|
```python |
|
from transformers import AutoTokenizer |
|
from vllm import LLM, SamplingParams |
|
|
|
number_gpus = 2 |
|
model_name = "neuralmagic/DeepSeek-R1-Distill-Llama-70B-quantized.w8a8" |
|
|
|
tokenizer = AutoTokenizer.from_pretrained(model_name) |
|
sampling_params = SamplingParams(temperature=0.6, max_tokens=256, stop_token_ids=[tokenizer.eos_token_id]) |
|
llm = LLM(model=model_name, tensor_parallel_size=number_gpus, trust_remote_code=True) |
|
|
|
messages_list = [ |
|
[{"role": "user", "content": "Who are you? Please respond in pirate speak!"}], |
|
] |
|
|
|
prompt_token_ids = [tokenizer.apply_chat_template(messages, add_generation_prompt=True) for messages in messages_list] |
|
|
|
outputs = llm.generate(prompt_token_ids=prompt_token_ids, sampling_params=sampling_params) |
|
|
|
generated_text = [output.outputs[0].text for output in outputs] |
|
print(generated_text) |
|
``` |
|
|
|
vLLM also supports OpenAI-compatible serving. See the [documentation](https://docs.vllm.ai/en/latest/) for more details. |
|
|
|
## Creation |
|
|
|
This model was created with [llm-compressor](https://github.com/vllm-project/llm-compressor) by running the code snippet below. |
|
|
|
|
|
```python |
|
from transformers import AutoModelForCausalLM, AutoTokenizer |
|
from llmcompressor.modifiers.quantization import QuantizationModifier |
|
from llmcompressor.modifiers.smoothquant import SmoothQuantModifier |
|
from llmcompressor.transformers import oneshot |
|
from llmcompressor.transformers.compression.helpers import calculate_offload_device_map |
|
|
|
# Load model |
|
model_stub = "deepseek-ai/DeepSeek-R1-Distill-Llama-70B" |
|
model_name = model_stub.split("/")[-1] |
|
|
|
num_samples = 1024 |
|
max_seq_len = 8192 |
|
|
|
tokenizer = AutoTokenizer.from_pretrained(model_stub) |
|
|
|
device_map = calculate_offload_device_map( |
|
model_stub, |
|
reserve_for_hessians=True, |
|
num_gpus=2, |
|
torch_dtype="auto", |
|
) |
|
|
|
model = AutoModelForCausalLM.from_pretrained( |
|
model_stub, |
|
device_map=device_map, |
|
torch_dtype="auto", |
|
) |
|
|
|
def preprocess_fn(example): |
|
return {"text": tokenizer.apply_chat_template(example["messages"], add_generation_prompt=False, tokenize=False)} |
|
|
|
ds = load_dataset("neuralmagic/LLM_compression_calibration", split="train") |
|
ds = ds.map(preprocess_fn) |
|
|
|
# Configure the quantization algorithm and scheme |
|
recipe = [ |
|
SmoothQuantModifier(smoothing_strength=0.7), |
|
QuantizationModifier( |
|
targets="Linear", |
|
scheme="W8A8", |
|
ignore=["lm_head"], |
|
dampening_frac=0.1, |
|
), |
|
] |
|
|
|
# Apply quantization |
|
oneshot( |
|
model=model, |
|
dataset=ds, |
|
recipe=recipe, |
|
max_seq_length=max_seq_len, |
|
num_calibration_samples=num_samples, |
|
) |
|
|
|
# Save to disk in compressed-tensors format |
|
save_path = model_name + "-quantized.w8a8 |
|
model.save_pretrained(save_path) |
|
tokenizer.save_pretrained(save_path) |
|
print(f"Model and tokenizer saved to: {save_path}") |
|
``` |
|
|
|
## Evaluation |
|
|
|
The model was evaluated on OpenLLM Leaderboard [V1](https://huggingface.co./spaces/open-llm-leaderboard-old/open_llm_leaderboard) and [V2](https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard#/), using the following commands: |
|
|
|
OpenLLM Leaderboard V1: |
|
``` |
|
lm_eval \ |
|
--model vllm \ |
|
--model_args pretrained="neuralmagic/DeepSeek-R1-Distill-Llama-70B-quantized.w8a8",dtype=auto,max_model_len=4096,tensor_parallel_size=2,enable_chunked_prefill=True \ |
|
--tasks openllm \ |
|
--write_out \ |
|
--batch_size auto \ |
|
--output_path output_dir \ |
|
--show_config |
|
``` |
|
|
|
OpenLLM Leaderboard V2: |
|
``` |
|
lm_eval \ |
|
--model vllm \ |
|
--model_args pretrained="neuralmagic/DeepSeek-R1-Distill-Llama-70B-quantized.w8a8",dtype=auto,max_model_len=4096,tensor_parallel_size=2,enable_chunked_prefill=True \ |
|
--apply_chat_template \ |
|
--fewshot_as_multiturn \ |
|
--tasks leaderboard \ |
|
--write_out \ |
|
--batch_size auto \ |
|
--output_path output_dir \ |
|
--show_config |
|
``` |
|
|
|
### Accuracy |
|
|
|
<table> |
|
<thead> |
|
<tr> |
|
<th>Category</th> |
|
<th>Metric</th> |
|
<th>deepseek-ai/DeepSeek-R1-Distill-Llama-70B</th> |
|
<th>neuralmagic/DeepSeek-R1-Distill-Llama-70B-quantized.w8a8</th> |
|
<th>Recovery</th> |
|
</tr> |
|
</thead> |
|
<tbody> |
|
<tr> |
|
<td rowspan="4"><b>Reasoning</b></td> |
|
<td>AIME 2024 (pass@1)</td> |
|
<td>67.83</td> |
|
<td>67.78</td> |
|
<td>99.93%</td> |
|
</tr> |
|
<tr> |
|
<td>MATH-500 (pass@1)</td> |
|
<td>95.29</td> |
|
<td>95.27</td> |
|
<td>99.98%</td> |
|
</tr> |
|
<tr> |
|
<td>GPQA Diamond (pass@1)</td> |
|
<td>65.57</td> |
|
<td>65.01</td> |
|
<td>99.15%</td> |
|
</tr> |
|
<tr> |
|
<td><b>Average Score</b></td> |
|
<td><b>76.23</b></td> |
|
<td><b>76.02</b></td> |
|
<td><b>99.72%</b></td> |
|
</tr> |
|
<tr> |
|
<td rowspan="7"><b>OpenLLM V1</b></td> |
|
<td>ARC-Challenge (Acc-Norm, 25-shot)</td> |
|
<td>63.65</td> |
|
<td>63.57</td> |
|
<td>99.9%</td> |
|
</tr> |
|
<tr> |
|
<td>GSM8K (Strict-Match, 5-shot)</td> |
|
<td>93.03</td> |
|
<td>93.56</td> |
|
<td>100.6%</td> |
|
</tr> |
|
<tr> |
|
<td>HellaSwag (Acc-Norm, 10-shot)</td> |
|
<td>84.85</td> |
|
<td>85.15</td> |
|
<td>100.4%</td> |
|
</tr> |
|
<tr> |
|
<td>MMLU (Acc, 5-shot)</td> |
|
<td>78.04</td> |
|
<td>78.01</td> |
|
<td>100.0%</td> |
|
</tr> |
|
<tr> |
|
<td>TruthfulQA (MC2, 0-shot)</td> |
|
<td>56.67</td> |
|
<td>57.47</td> |
|
<td>101.4%</td> |
|
</tr> |
|
<tr> |
|
<td>Winogrande (Acc, 5-shot)</td> |
|
<td>78.22</td> |
|
<td>78.37</td> |
|
<td>100.2%</td> |
|
</tr> |
|
<tr> |
|
<td><b>Average Score</b></td> |
|
<td><b>75.74</b></td> |
|
<td><b>76.02</b></td> |
|
<td><b>100.4%</b></td> |
|
</tr> |
|
<tr> |
|
<td rowspan="7"><b>OpenLLM V2</b></td> |
|
<td>IFEval (Inst Level Strict Acc, 0-shot)</td> |
|
<td>42.45</td> |
|
<td>42.51</td> |
|
<td>100.1%</td> |
|
</tr> |
|
<tr> |
|
<td>BBH (Acc-Norm, 3-shot)</td> |
|
<td>21.26</td> |
|
<td>20.78</td> |
|
<td>97.8%</td> |
|
</tr> |
|
<tr> |
|
<td>Math-Hard (Exact-Match, 4-shot)</td> |
|
<td>0.00</td> |
|
<td>0.00</td> |
|
<td>---</td> |
|
</tr> |
|
<tr> |
|
<td>GPQA (Acc-Norm, 0-shot)</td> |
|
<td>9.51</td> |
|
<td>7.25</td> |
|
<td>---</td> |
|
</tr> |
|
<tr> |
|
<td>MUSR (Acc-Norm, 0-shot)</td> |
|
<td>14.87</td> |
|
<td>15.24</td> |
|
<td>---</td> |
|
</tr> |
|
<tr> |
|
<td>MMLU-Pro (Acc, 5-shot)</td> |
|
<td>4.27</td> |
|
<td>5.62</td> |
|
<td>---</td> |
|
</tr> |
|
<tr> |
|
<td><b>Average Score</b></td> |
|
<td><b>15.39</b></td> |
|
<td><b>15.23</b></td> |
|
<td><b>99.0%</b></td> |
|
</tr> |
|
<tr> |
|
<td rowspan="4"><b>Coding</b></td> |
|
<td>HumanEval (pass@1)</td> |
|
<td>81.10</td> |
|
<td>81.00</td> |
|
<td><b>99.9%</b></td> |
|
</tr> |
|
<tr> |
|
<td>HumanEval (pass@10)</td> |
|
<td>87.60</td> |
|
<td>86.80</td> |
|
<td>99.1%</td> |
|
</tr> |
|
<tr> |
|
<td>HumanEval+ (pass@10)</td> |
|
<td>75.20</td> |
|
<td>75.80</td> |
|
<td>100.8%</td> |
|
</tr> |
|
<tr> |
|
<td>HumanEval+ (pass@10)</td> |
|
<td>83.10</td> |
|
<td>83.40</td> |
|
<td>100.4%</td> |
|
</tr> |
|
</tbody> |
|
</table> |
|
|
|
## Inference Performance |
|
|
|
|
|
This model achieves up to 2.0x speedup in single-stream deployment and up to 2.2x speedup in multi-stream asynchronous deployment, depending on hardware and use-case scenario. |
|
The following performance benchmarks were conducted with [vLLM](https://docs.vllm.ai/en/latest/) version 0.7.2, and [GuideLLM](https://github.com/neuralmagic/guidellm). |
|
|
|
<details> |
|
<summary>Benchmarking Command</summary> |
|
|
|
``` |
|
guidellm --model neuralmagic/DeepSeek-R1-Distill-Llama-70B-quantized.w8a8 --target "http://localhost:8000/v1" --data-type emulated --data "prompt_tokens=<prompt_tokens>,generated_tokens=<generated_tokens>" --max seconds 360 --backend aiohttp_server |
|
``` |
|
</details> |
|
|
|
### Single-stream performance (measured with vLLM version 0.7.2) |
|
<table> |
|
<thead> |
|
<tr> |
|
<th></th> |
|
<th></th> |
|
<th></th> |
|
<th></th> |
|
<th style="text-align: center;" colspan="2" >Instruction Following<br>256 / 128</th> |
|
<th style="text-align: center;" colspan="2" >Multi-turn Chat<br>512 / 256</th> |
|
<th style="text-align: center;" colspan="2" >Docstring Generation<br>768 / 128</th> |
|
<th style="text-align: center;" colspan="2" >RAG<br>1024 / 128</th> |
|
<th style="text-align: center;" colspan="2" >Code Completion<br>256 / 1024</th> |
|
<th style="text-align: center;" colspan="2" >Code Fixing<br>1024 / 1024</th> |
|
<th style="text-align: center;" colspan="2" >Large Summarization<br>4096 / 512</th> |
|
<th style="text-align: center;" colspan="2" >Large RAG<br>10240 / 1536</th> |
|
</tr> |
|
<tr> |
|
<th>GPU class</th> |
|
<th>Number of GPUs</th> |
|
<th>Model</th> |
|
<th>Average cost reduction</th> |
|
<th>Latency (s)</th> |
|
<th>QPD</th> |
|
<th>Latency (s)</th> |
|
<th>QPD</th> |
|
<th>Latency (s)</th> |
|
<th>QPD</th> |
|
<th>Latency (s)</th> |
|
<th>QPD</th> |
|
<th>Latency (s)</th> |
|
<th>QPD</th> |
|
<th>Latency (s)</th> |
|
<th>QPD</th> |
|
<th>Latency (s)</th> |
|
<th>QPD</th> |
|
<th>Latency (s)</th> |
|
<th>QPD</th> |
|
</tr> |
|
</thead> |
|
<tbody style="text-align: center" > |
|
<tr> |
|
<th rowspan="3" valign="top">A6000</th> |
|
<td>4</td> |
|
<th>deepseek-ai/DeepSeek-R1-Distill-Llama-70B</th> |
|
<td>---</td> |
|
<td>7.4</td> |
|
<td>152</td> |
|
<td>14.9</td> |
|
<td>76</td> |
|
<td>7.5</td> |
|
<td>149</td> |
|
<td>7.7</td> |
|
<td>146</td> |
|
<td>57.2</td> |
|
<td>20</td> |
|
<td>58.9</td> |
|
<td>19</td> |
|
<td>31.9</td> |
|
<td>35</td> |
|
<td>98.4</td> |
|
<td>11</td> |
|
</tr> |
|
<tr> |
|
<td>2</td> |
|
<th>neuralmagic/DeepSeek-R1-Distill-Llama-70B-quantized.w8a8</th> |
|
<td>1.93</td> |
|
<td>7.7</td> |
|
<td>292</td> |
|
<td>15.2</td> |
|
<td>148</td> |
|
<td>7.8</td> |
|
<td>287</td> |
|
<td>8.0</td> |
|
<td>282</td> |
|
<td>60.7</td> |
|
<td>37</td> |
|
<td>60.2</td> |
|
<td>37</td> |
|
<td>32.3</td> |
|
<td>70</td> |
|
<td>104.0</td> |
|
<td>22</td> |
|
</tr> |
|
<tr> |
|
<td>2</td> |
|
<th>neuralmagic/DeepSeek-R1-Distill-Llama-70B-quantized.w4a16</th> |
|
<td>2.83</td> |
|
<td>4.9</td> |
|
<td>457</td> |
|
<td>10.0</td> |
|
<td>225</td> |
|
<td>5.5</td> |
|
<td>411</td> |
|
<td>5.8</td> |
|
<td>389</td> |
|
<td>38.9</td> |
|
<td>58</td> |
|
<td>39.2</td> |
|
<td>57</td> |
|
<td>23.7</td> |
|
<td>95</td> |
|
<td>76.6</td> |
|
<td>29</td> |
|
</tr> |
|
<tr> |
|
<th rowspan="3" valign="top">A100</th> |
|
<td>2</td> |
|
<th>deepseek-ai/DeepSeek-R1-Distill-Llama-70B</th> |
|
<td>---</td> |
|
<td>6.4</td> |
|
<td>157</td> |
|
<td>12.8</td> |
|
<td>79</td> |
|
<td>6.6</td> |
|
<td>153</td> |
|
<td>6.7</td> |
|
<td>151</td> |
|
<td>50.4</td> |
|
<td>20</td> |
|
<td>50.8</td> |
|
<td>20</td> |
|
<td>27.0</td> |
|
<td>37</td> |
|
<td>85.4</td> |
|
<td>12</td> |
|
</tr> |
|
<tr> |
|
<td>2</td> |
|
<th>neuralmagic/DeepSeek-R1-Distill-Llama-70B-quantized.w8a8</th> |
|
<td>1.48</td> |
|
<td>4.1</td> |
|
<td>245</td> |
|
<td>8.2</td> |
|
<td>123</td> |
|
<td>4.2</td> |
|
<td>238</td> |
|
<td>4.3</td> |
|
<td>235</td> |
|
<td>32.4</td> |
|
<td>31</td> |
|
<td>32.8</td> |
|
<td>31</td> |
|
<td>17.6</td> |
|
<td>57</td> |
|
<td>90.8</td> |
|
<td>11</td> |
|
</tr> |
|
<tr> |
|
<td>1</td> |
|
<th>neuralmagic/DeepSeek-R1-Distill-Llama-70B-quantized.w4a16</th> |
|
<td>2.69</td> |
|
<td>4.6</td> |
|
<td>440</td> |
|
<td>9.2</td> |
|
<td>220</td> |
|
<td>4.9</td> |
|
<td>407</td> |
|
<td>5.2</td> |
|
<td>389</td> |
|
<td>35.3</td> |
|
<td>57</td> |
|
<td>36.3</td> |
|
<td>55</td> |
|
<td>21.2</td> |
|
<td>95</td> |
|
<td>68.1</td> |
|
<td>30</td> |
|
</tr> |
|
<tr> |
|
<th rowspan="3" valign="top">H100</th> |
|
<td>2</td> |
|
<th>deepseek-ai/DeepSeek-R1-Distill-Llama-70B</th> |
|
<td>---</td> |
|
<td>3.8</td> |
|
<td>149</td> |
|
<td>7.6</td> |
|
<td>74</td> |
|
<td>3.9</td> |
|
<td>146</td> |
|
<td>3.9</td> |
|
<td>144</td> |
|
<td>30.0</td> |
|
<td>19</td> |
|
<td>30.4</td> |
|
<td>19</td> |
|
<td>16.1</td> |
|
<td>35</td> |
|
<td>56.5</td> |
|
<td>10</td> |
|
</tr> |
|
<tr> |
|
<td>2</td> |
|
<th>neuralmagic/DeepSeek-R1-Distill-Llama-70B-FP8-dynamic</th> |
|
<td>1.39</td> |
|
<td>2.7</td> |
|
<td>210</td> |
|
<td>5.3</td> |
|
<td>106</td> |
|
<td>2.7</td> |
|
<td>207</td> |
|
<td>2.8</td> |
|
<td>203</td> |
|
<td>21.1</td> |
|
<td>27</td> |
|
<td>21.4</td> |
|
<td>26</td> |
|
<td>11.5</td> |
|
<td>49</td> |
|
<td>47.2</td> |
|
<td>12</td> |
|
</tr> |
|
<tr> |
|
<td>1</td> |
|
<th>neuralmagic/DeepSeek-R1-Distill-Llama-70B-quantized.w4a16</th> |
|
<td>1.83</td> |
|
<td>4.0</td> |
|
<td>277</td> |
|
<td>7.9</td> |
|
<td>138</td> |
|
<td>4.1</td> |
|
<td>266</td> |
|
<td>4.2</td> |
|
<td>262</td> |
|
<td>31.2</td> |
|
<td>35</td> |
|
<td>31.8</td> |
|
<td>34</td> |
|
<td>17.8</td> |
|
<td>61</td> |
|
<td>61.4</td> |
|
<td>18</td> |
|
</tr> |
|
</tbody> |
|
</table> |
|
|
|
**Use case profiles: prompt tokens / generation tokens |
|
|
|
**QPD: Queries per dollar, based on on-demand cost at [Lambda Labs](https://lambdalabs.com/service/gpu-cloud) (observed on 2/18/2025). |
|
|
|
|
|
### Multi-stream asynchronous performance (measured with vLLM version 0.7.2) |
|
<table> |
|
<thead> |
|
<tr> |
|
<th></th> |
|
<th></th> |
|
<th></th> |
|
<th style="text-align: center;" colspan="2" >Instruction Following<br>256 / 128</th> |
|
<th style="text-align: center;" colspan="2" >Multi-turn Chat<br>512 / 256</th> |
|
<th style="text-align: center;" colspan="2" >Docstring Generation<br>768 / 128</th> |
|
<th style="text-align: center;" colspan="2" >RAG<br>1024 / 128</th> |
|
<th style="text-align: center;" colspan="2" >Code Completion<br>256 / 1024</th> |
|
<th style="text-align: center;" colspan="2" >Code Fixing<br>1024 / 1024</th> |
|
<th style="text-align: center;" colspan="2" >Large Summarization<br>4096 / 512</th> |
|
<th style="text-align: center;" colspan="2" >Large RAG<br>10240 / 1536</th> |
|
</tr> |
|
<tr> |
|
<th>Hardware</th> |
|
<th>Model</th> |
|
<th>Average cost reduction</th> |
|
<th>Maximum throughput (QPS)</th> |
|
<th>QPD</th> |
|
<th>Maximum throughput (QPS)</th> |
|
<th>QPD</th> |
|
<th>Maximum throughput (QPS)</th> |
|
<th>QPD</th> |
|
<th>Maximum throughput (QPS)</th> |
|
<th>QPD</th> |
|
<th>Maximum throughput (QPS)</th> |
|
<th>QPD</th> |
|
<th>Maximum throughput (QPS)</th> |
|
<th>QPD</th> |
|
<th>Maximum throughput (QPS)</th> |
|
<th>QPD</th> |
|
<th>Maximum throughput (QPS)</th> |
|
<th>QPD</th> |
|
</tr> |
|
</thead> |
|
<tbody style="text-align: center" > |
|
<tr> |
|
<th rowspan="3" valign="top">A6000x4</th> |
|
<th>deepseek-ai/DeepSeek-R1-Distill-Llama-70B</th> |
|
<td>---</td> |
|
<td>3.65</td> |
|
<td>4102</td> |
|
<td>1.56</td> |
|
<td>1757</td> |
|
<td>1.90</td> |
|
<td>2143</td> |
|
<td>1.48</td> |
|
<td>1665</td> |
|
<td>0.44</td> |
|
<td>493</td> |
|
<td>0.34</td> |
|
<td>380</td> |
|
<td>0.22</td> |
|
<td>245</td> |
|
<td>0.05</td> |
|
<td>55</td> |
|
</tr> |
|
<tr> |
|
<th>neuralmagic/DeepSeek-R1-Distill-Llama-70B-quantized.w8a8</th> |
|
<td>1.76</td> |
|
<td>5.89</td> |
|
<td>6625</td> |
|
<td>2.94</td> |
|
<td>3307</td> |
|
<td>3.36</td> |
|
<td>3775</td> |
|
<td>2.59</td> |
|
<td>2916</td> |
|
<td>0.74</td> |
|
<td>828</td> |
|
<td>0.53</td> |
|
<td>601</td> |
|
<td>0.35</td> |
|
<td>398</td> |
|
<td>0.11</td> |
|
<td>120</td> |
|
</tr> |
|
<tr> |
|
<th>neuralmagic/DeepSeek-R1-Distill-Llama-70B-quantized.w4a16</th> |
|
<td>1.48</td> |
|
<td>4.91</td> |
|
<td>5528</td> |
|
<td>2.01</td> |
|
<td>2259</td> |
|
<td>2.03</td> |
|
<td>2280</td> |
|
<td>1.12</td> |
|
<td>1255</td> |
|
<td>1.11</td> |
|
<td>1251</td> |
|
<td>0.76</td> |
|
<td>852</td> |
|
<td>0.24</td> |
|
<td>267</td> |
|
<td>0.07</td> |
|
<td>81</td> |
|
</tr> |
|
<tr> |
|
<th rowspan="3" valign="top">A100x4</th> |
|
<th>deepseek-ai/DeepSeek-R1-Distill-Llama-70B</th> |
|
<td>---</td> |
|
<td>10.41</td> |
|
<td>5235</td> |
|
<td>5.10</td> |
|
<td>2565</td> |
|
<td>5.50</td> |
|
<td>2766</td> |
|
<td>4.36</td> |
|
<td>2193</td> |
|
<td>1.49</td> |
|
<td>751</td> |
|
<td>1.21</td> |
|
<td>607</td> |
|
<td>0.89</td> |
|
<td>447</td> |
|
<td>0.19</td> |
|
<td>98</td> |
|
</tr> |
|
<tr> |
|
<th>neuralmagic/DeepSeek-R1-Distill-Llama-70B-quantized.w8a8</th> |
|
<td>1.63</td> |
|
<td>18.11</td> |
|
<td>9103</td> |
|
<td>8.90</td> |
|
<td>4477</td> |
|
<td>9.41</td> |
|
<td>4730</td> |
|
<td>7.42</td> |
|
<td>3731</td> |
|
<td>2.44</td> |
|
<td>1229</td> |
|
<td>1.89</td> |
|
<td>948</td> |
|
<td>1.26</td> |
|
<td>631</td> |
|
<td>0.30</td> |
|
<td>149</td> |
|
</tr> |
|
<tr> |
|
<th>neuralmagic/DeepSeek-R1-Distill-Llama-70B-quantized.w4a16</th> |
|
<td>1.12</td> |
|
<td>12.63</td> |
|
<td>6353</td> |
|
<td>5.32</td> |
|
<td>2673</td> |
|
<td>5.58</td> |
|
<td>2804</td> |
|
<td>4.27</td> |
|
<td>2144</td> |
|
<td>2.30</td> |
|
<td>1158</td> |
|
<td>1.45</td> |
|
<td>729</td> |
|
<td>0.76</td> |
|
<td>381</td> |
|
<td>0.22</td> |
|
<td>110</td> |
|
</tr> |
|
<tr> |
|
<th rowspan="3" valign="top">H100x4</th> |
|
<th>deepseek-ai/DeepSeek-R1-Distill-Llama-70B</th> |
|
<td>---</td> |
|
<td>14.04</td> |
|
<td>2113</td> |
|
<td>10.85</td> |
|
<td>1634</td> |
|
<td>12.25</td> |
|
<td>1844</td> |
|
<td>9.93</td> |
|
<td>1494</td> |
|
<td>3.68</td> |
|
<td>554</td> |
|
<td>2.82</td> |
|
<td>425</td> |
|
<td>1.81</td> |
|
<td>273</td> |
|
<td>0.35</td> |
|
<td>52</td> |
|
</tr> |
|
<tr> |
|
<th>neuralmagic/DeepSeek-R1-Distill-Llama-70B-FP8-dynamic</th> |
|
<td>1.78</td> |
|
<td>41.44</td> |
|
<td>6236</td> |
|
<td>19.64</td> |
|
<td>2956</td> |
|
<td>21.03</td> |
|
<td>3166</td> |
|
<td>16.72</td> |
|
<td>2516</td> |
|
<td>6.01</td> |
|
<td>904</td> |
|
<td>4.46</td> |
|
<td>672</td> |
|
<td>2.55</td> |
|
<td>383</td> |
|
<td>0.49</td> |
|
<td>74</td> |
|
</tr> |
|
<tr> |
|
<th>neuralmagic/DeepSeek-R1-Distill-Llama-70B-quantized.w4a16</th> |
|
<td>1.45</td> |
|
<td>36.61</td> |
|
<td>5509</td> |
|
<td>15.12</td> |
|
<td>2275</td> |
|
<td>16.24</td> |
|
<td>2443</td> |
|
<td>13.22</td> |
|
<td>1990</td> |
|
<td>5.48</td> |
|
<td>825</td> |
|
<td>3.01</td> |
|
<td>453</td> |
|
<td>2.07</td> |
|
<td>312</td> |
|
<td>0.43</td> |
|
<td>64</td> |
|
</tr> |
|
</tbody> |
|
</table> |
|
|
|
**Use case profiles: prompt tokens / generation tokens |
|
|
|
**QPS: Queries per second. |
|
|
|
**QPD: Queries per dollar, based on on-demand cost at [Lambda Labs](https://lambdalabs.com/service/gpu-cloud) (observed on 2/18/2025). |
|
|
|
|
|
|
