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Mixtral 8x7B on Fireworks: faster, cheaper, even before the official release

Mixtral 8x7B on Fireworks: faster, cheaper, even before the official release




The newest Mistral AI MoE model, Mixtral 8x7B, is available on the Fireworks platform in both base and instruction-tuned variants. We offer it with unprecedented speed reaching 175 tokens/s and a low cost of $0.45 to $0.6 per million tokens for summarization or multi-turn chat respectively.

Fastest inference on Fireworks

To enable Mistral on Fireworks we added support for the mixture of experts layers into our custom-built inference engine powering platform. In combination with other optimizations (custom kernels for layers like MQA to purpose-built communication primitives and flexible parallelization schemes) our engine is able to achieve 175 tokens/s on a latency-optimized setup for Mixtral 8x7B served in original (16 bit) precision.

Our engine also supports 8-bit floating point (fp8) regime, which delivers much higher throughput for custom deployments. Unlike integer-based quantization, which can be finicky, fp8 comes with virtually no impact on model quality.

As we explained in our benchmarking blogpost, when it comes to inference performance, there is no one size fits all. Fireworks specialized in multi-faceted deployment to optimize towards your LLM workload pattern, varying from latency-optimized, fixed-latency-throughput-optimized, to throughput-optimized. The diagram below illustrates the variation of deployment configurations. We have the best setup for everyone! For example, the configuration deployed on the playground and public API runs a moderate load, so you should see speeds of 80–100 tokens/s.

We're continuing performance optimization work on Mixtral, so expect to see more improvements in the future.

Passing cost savings to you

We're introducing the new pricing tier for Mixture of Experts models that ends up costing $0.45 to $0.6 per million tokens on average for Mixtral 8x7B.

Our official pricing separates prompt and generation tokens. For Mixtral 8x7B, the prompt is charged at $0.4/million and generation at $1.6/million. For multi-turn chat we typically see a 4:1 to 6:1 prompt to generation ratio resulting in an average of $0.6/million tokens. For summarization, the average cost is around $0.45/million tokens.

We're able to provide this competitive pricing thanks to our custom inference engine that can utilize GPUs effectively and deliver high throughput under low latency constraints. This is particularly true for Mixture of Experts models: to reap the benefits of selective expert activations, one has to run at sufficiently high load (as explained here).

Accessing Mixtral through Fireworks

You can head to our playground now to play with Mixtral using free credits (no credit card required).

We offer OpenAI-compatible API (for both base and chat completions) that you can call using REST, our Python client or any OpenAI-compatible client library. For example you can migrate your existing application using OpenAI python client by providing a different base url:

How good are the experts?

Over the previous weekend, Mistral AI released a new, very exciting model: Mixtral. The “x” highlights the twist. Most of today's LLMs are dense, meaning that every model parameter participates in generating every token. Mixtral brings the “sparse mixture of expert” technique back to the mainstream: it divides feed-forward layers of the model into 8 independent “experts” with only 2 of them executed for every token. Thus it enables higher model capacity at lower inference cost and higher speed.

Mixtral 8x7B is released under Apache 2.0 license and it beats Llama 2 70B and GPT-3.5 on most benchmarks as highlighted in the release blog. Our early experiments confirm that model capabilities are very strong.

Race to MoE awesomeness

The last few days were quite intense. The Mistral AI team did a very unconventional release, dropping the model checkpoint on torrent last Friday morning. There was no model code or way to run it!

By reverse-engineering parameter names, we were the first to implement working inference code just 4 hours later. We shared it with the community and enabled people to experiment with the model on various datasets on the same day. The base model was up on the Fireworks platform the same evening. Over the next few days, we added necessary performance enhancements to get the speeds described here.

The original model drop on Friday included only the base model and was not suitable for chat use cases. So, we quickly fine-tuned an instruction-following variant of the model and deployed it on Saturday. We later substituted it with the official Mixtral-8x7B-Instruct, which was released the following Monday.

We're adding features to Mixtral deployment, in particular, the ability to deploy your own custom LoRA fine-tunes on Fireworks with no additional cost (like you can for other models already).

We want to thank the Mistral AI team for sharing an extremely capable model openly with the community and for creating a fun puzzle on how to enable it initially.

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