Mixtral of Experts
Albert Q. Jiang, Alexandre Sablayrolles, Antoine Roux, Arthur Mensch,
Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas,
Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour,
Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux,
Pierre Stock, Sandeep Subramanian, Sophia Yang, Szymon Antoniak, Teven Le Scao,
Théophile Gervet, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed
arXiv:2401.04088v1 [cs.LG] 8 Jan 2024
Abstract
We introduce Mixtral 8x7B, a Sparse Mixture of Experts (SMoE) language
model. Mixtral has the same architecture as Mistral 7B, with the difference
that each layer is composed of 8 feedforward blocks (i.e. experts). For every
token, at each layer, a router network selects two experts to process the current
state and combine their outputs. Even though each token only sees two experts,
the selected experts can be different at each timestep. As a result, each token
has access to 47B parameters, but only uses 13B active parameters during
inference. Mixtral was trained with a context size of 32k tokens and it outperforms
or matches Llama 2 70B and GPT-3.5 across all evaluated benchmarks. In
particular, Mixtral vastly outperforms Llama 2 70B on mathematics, code
generation, and multilingual benchmarks. We also provide a model fine-
tuned to follow instructions, Mixtral 8x7B – Instruct, that surpasses GPT-3.5
Turbo, Claude-2.1, Gemini Pro, and Llama 2 70B – chat model on human bench-
marks. Both the base and instruct models are released under the Apache 2.0 license.
Code: https://github.com/mistralai/mistral-src
Webpage: https://mistral.ai/news/mixtral-of-experts/
1 Introduction
In this paper, we present Mixtral 8x7B, a sparse mixture of experts model (SMoE) with open weights,
licensed under Apache 2.0. Mixtral outperforms Llama 2 70B and GPT-3.5 on most benchmarks. As
it only uses a subset of its parameters for every token, Mixtral allows faster inference speed at low
batch-sizes, and higher throughput at large batch-sizes.
Mixtral is a sparse mixture-of-experts network. It is a decoder-only model where the feedforward
block picks from a set of 8 distinct groups of parameters. At every layer, for every token, a router
network chooses two of these groups (the “experts”) to process the token and combine their output
additively. This technique increases the number of parameters of a model while controlling cost and
latency, as the model only uses a fraction of the total set of parameters per token.
Mixtral is pretrained with multilingual data using a context size of 32k tokens. It either matches
or exceeds the performance of Llama 2 70B and GPT-3.5, over several benchmarks. In particular,
Figure 1: Mixture of Experts Layer. Each input vector is assigned to 2 of the 8 experts by a router. The
layer’s output is the weighted sum of the outputs of the two selected experts. In Mixtral, an expert is a standard
feedforward block as in a vanilla transformer architecture.
Mixtral demonstrates superior capabilities in mathematics, code generation, and tasks that require
multilingual understanding, significantly outperforming Llama 2 70B in these domains. Experiments
show that Mixtral is able to successfully retrieve information from its context window of 32k tokens,
regardless of the sequence length and the location of the information in the sequence.
We also present Mixtral 8x7B – Instruct, a chat model fine-tuned to follow instructions using
supervised fine-tuning and Direct Preference Optimization [25]. Its performance notably surpasses
that of GPT-3.5 Turbo, Claude-2.1, Gemini Pro, and Llama 2 70B – chat model on human evaluation
benchmarks. Mixtral – Instruct also demonstrates reduced biases, and a more balanced sentiment
profile in benchmarks such as BBQ, and BOLD.
We release both Mixtral 8x7B and Mixtral 8x7B – Instruct under the Apache 2.0 license1 , free for
academic and commercial usage, ensuring broad accessibility and potential for diverse applications.
To enable the community to run Mixtral with a fully open-source stack, we submitted changes to
the vLLM project, which integrates Megablocks CUDA kernels for efficient inference. Skypilot also
allows the deployment of vLLM endpoints on any instance in the cloud.
2 Architectural details
Mixtral is based on a transformer architecture [31] and uses the same
Parameter Value
modifications as described in [18], with the notable exceptions that Mix-
tral supports a fully dense context length of 32k tokens, and the feed- dim 4096
forward blocks are replaced by Mixture-of-Expert layers (Section 2.1). n_layers 32
The model architecture parameters are summarized in Table 1. head_dim 128
hidden_dim 14336
n_heads 32
2.1 Sparse Mixture of Experts n_kv_heads 8
We present a brief overview of the Mixture of Experts layer (Figure 1). context_len 32768
For a more in-depth overview, see [12]. The output of the MoE module vocab_size 32000
for a given input x is determined by the weighted sum of the outputs num_experts 8
of the expert networks, where the weights are given by the gating top_k_experts 2
network’s output. i.e. given n expert networks {E0 , Ei , ..., En−1 }, the Table 1: Model architecture.
output of the expert layer is given by:
n−1
X
G(x)i · Ei (x).
i=0
Here, G(x)i denotes the n-dimensional output of the gating network for the i-th expert, and Ei (x)
is the output of the i-th expert network. If the gating vector is sparse, we can avoid computing
the outputs of experts whose gates are zero. There are multiple alternative ways of implementing
G(x) [6, 15, 35], but a simple and performant one is implemented by taking the softmax over the
Top-K logits of a linear layer [28]. We use
G(x) := Softmax(TopK(x · Wg )),
where (TopK(ℓ))i := ℓi if ℓi is among the top-K coordinates of logits ℓ ∈ Rn and (TopK(ℓ))i := −∞
otherwise. The value of K – the number of experts used per token – is a hyper-parameter that modu-
lates the amount of compute used to process each token. If one increases n while keeping K fixed, one
1
https://mistral.ai/news/mixtral-of-experts/
2
can increase the model’s parameter count while keeping its computational cost effectively constant.
This motivates a distinction between the model’s total parameter count (commonly referenced as the
sparse parameter count), which grows with n, and the number of parameters used for processing an
individual token (called the active parameter count), which grows with K up to n.
MoE layers can be run efficiently on single GPUs with high performance specialized kernels. For
example, Megablocks [13] casts the feed-forward network (FFN) operations of the MoE layer as large
sparse matrix multiplications, significantly enhancing the execution speed and naturally handling
cases where different experts get a variable number of tokens assigned to them. Moreover, the
MoE layer can be distributed to multiple GPUs through standard Model Parallelism techniques, and
through a particular kind of partitioning strategy called Expert Parallelism (EP) [28]. During the MoE
layer’s execution, tokens meant to be processed by a specific expert are routed to the corresponding
GPU for processing, and the expert’s output is returned to the original token location. Note that EP
introduces challenges in load balancing, as it is essential to distribute the workload evenly across the
GPUs to prevent overloading individual GPUs or hitting computational bottlenecks.
In a Transformer model, the MoE layer is applied independently per token and replaces the
feed-forward (FFN) sub-block of the transformer block. For Mixtral we use the same SwiGLU
architecture as the expert function Ei (x) and set K = 2. This means each token is routed to two
SwiGLU sub-blocks with different sets of weights. Taking this all together, the output y for an input
token x is computed as:
n−1
X
y= Softmax(Top2(x · Wg ))i · SwiGLUi (x).
i=0
This formulation is similar to the GShard architecture [21], with the exceptions that we replace all
FFN sub-blocks by MoE layers while GShard replaces every other block, and that GShard uses a
more elaborate gating strategy for the second expert assigned to each token.
3 Results
We compare Mixtral to Llama, and re-run all benchmarks with our own evaluation pipeline for fair
comparison. We measure performance on a wide variety of tasks categorized as follow:
• Commonsense Reasoning (0-shot): Hellaswag [32], Winogrande [26], PIQA [3], SIQA [27],
OpenbookQA [22], ARC-Easy, ARC-Challenge [8], CommonsenseQA [30]
• World Knowledge (5-shot): NaturalQuestions [20], TriviaQA [19]
• Reading Comprehension (0-shot): BoolQ [7], QuAC [5]
• Math: GSM8K [9] (8-shot) with maj@8 and MATH [17] (4-shot) with maj@4
• Code: Humaneval [4] (0-shot) and MBPP [1] (3-shot)
• Popular aggregated results: MMLU [16] (5-shot), BBH [29] (3-shot), and AGI Eval [34]
(3-5-shot, English multiple-choice questions only)
Figure 2: Performance of Mixtral and different Llama models on a wide range of benchmarks. All models
were re-evaluated on all metrics with our evaluation pipeline for accurate comparison. Mixtral outperforms or
matches Llama 2 70B on all benchmarks. In particular, it is vastly superior in mathematics and code generation.
3
Active
Model Params
MMLU HellaS WinoG PIQA Arc-e Arc-c NQ TriQA HumanE MBPP Math GSM8K
LLaMA 2 7B 7B 44.4% 77.1% 69.5% 77.9% 68.7% 43.2% 17.5% 56.6% 11.6% 26.1% 3.9% 16.0%
LLaMA 2 13B 13B 55.6% 80.7% 72.9% 80.8% 75.2% 48.8% 16.7% 64.0% 18.9% 35.4% 6.0% 34.3%
LLaMA 1 33B 33B 56.8% 83.7% 76.2% 82.2% 79.6% 54.4% 24.1% 68.5% 25.0% 40.9% 8.4% 44.1%
LLaMA 2 70B 70B 69.9% 85.4% 80.4% 82.6% 79.9% 56.5% 25.4% 73.0% 29.3% 49.8% 13.8% 69.6%
Mistral 7B 7B 62.5% 81.0% 74.2% 82.2% 80.5% 54.9% 23.2% 62.5% 26.2% 50.2% 12.7% 50.0%
Mixtral 8x7B 13B 70.6% 84.4% 77.2% 83.6% 83.1% 59.7% 30.6% 71.5% 40.2% 60.7% 28.4% 74.4%
Table 2: Comparison of Mixtral with Llama. Mixtral outperforms or matches Llama 2 70B performance on
almost all popular benchmarks while using 5x fewer active parameters during inference.
Figure 3: Results on MMLU, commonsense reasoning, world knowledge and reading comprehension,
math and code for Mistral (7B/8x7B) vs Llama 2 (7B/13B/70B). Mixtral largely outperforms Llama 2 70B
on all benchmarks, except on reading comprehension benchmarks while using 5x lower active parameters. It
is also vastly superior to Llama 2 70B on code and math.
Detailed results for Mixtral, Mistral 7B and Llama 2 7B/13B/70B and Llama 1 34B2 are reported
in Table 2. Figure 2 compares the performance of Mixtral with the Llama models in different
categories. Mixtral surpasses Llama 2 70B across most metrics. In particular, Mixtral displays a
superior performance in code and mathematics benchmarks.
Size and Efficiency. We compare our performance to the Llama 2 family, aiming to understand
Mixtral models’ efficiency in the cost-performance spectrum (see Figure 3). As a sparse Mixture-
of-Experts model, Mixtral only uses 13B active parameters for each token. With 5x lower active
parameters, Mixtral is able to outperform Llama 2 70B across most categories.
Note that this analysis focuses on the active parameter count (see Section 2.1), which is directly
proportional to the inference compute cost, but does not consider the memory costs and hardware
utilization. The memory costs for serving Mixtral are proportional to its sparse parameter count,
47B, which is still smaller than Llama 2 70B. As for device utilization, we note that the SMoEs layer
introduces additional overhead due to the routing mechanism and due to the increased memory loads
when running more than one expert per device. They are more suitable for batched workloads where
one can reach a good degree of arithmetic intensity.
Comparison with Llama 2 70B and GPT-3.5. In Table 3, we report the performance of Mixtral 8x7B
compared to Llama 2 70B and GPT-3.5. We observe that Mixtral performs similarly or above the
two other models. On MMLU, Mixtral obtains a better performance, despite its significantly smaller
capacity (47B tokens compared to 70B). For MT Bench, we report the performance of the latest
GPT-3.5-Turbo model available, gpt-3.5-turbo-1106.
2
Since Llama 2 34B was not open-sourced, we report results for Llama 1 34B.
4
LLaMA 2 70B GPT-3.5 Mixtral 8x7B
MMLU 69.9% 70.0% 70.6%
(MCQ in 57 subjects)
HellaSwag 87.1% 85.5% 86.7%
(10-shot)
ARC Challenge 85.1% 85.2% 85.8%
(25-shot)
WinoGrande 83.2% 81.6% 81.2%
(5-shot)
MBPP 49.8% 52.2% 60.7%
(pass@1)
GSM-8K 53.6% 57.1% 58.4%
(5-shot)
MT Bench 6.86 8.32 8.30
(for Instruct Models)
Table 3: Comparison of Mixtral with Llama 2 70B and GPT-3.5. Mixtral outperforms or matches Llama 2
70B and GPT-3.5 performance on most metrics.
Evaluation Differences. On some benchmarks, there are some differences between our evaluation
protocol and the one reported in the Llama 2 paper: 1) on MBPP, we use the hand-verified subset 2)
on TriviaQA, we do not provide Wikipedia contexts.
3.1 Multilingual benchmarks
Compared to Mistral 7B, we significantly upsample the proportion of multilingual data during
pretraining. The extra capacity allows Mixtral to perform well on multilingual benchmarks while
maintaining a high accuracy in English. In particular, Mixtral significantly outperforms Llama 2 70B
in French, German, Spanish, and Italian, as shown in Table 4.
Active French German Spanish Italian
Model Params Arc-c HellaS MMLU Arc-c HellaS MMLU Arc-c HellaS MMLU Arc-c HellaS MMLU
LLaMA 1 33B 33B 39.3% 68.1% 49.9% 41.1% 63.3% 48.7% 45.7% 69.8% 52.3% 42.9% 65.4% 49.0%
LLaMA 2 70B 70B 49.9% 72.5% 64.3% 47.3% 68.7% 64.2% 50.5% 74.5% 66.0% 49.4% 70.9% 65.1%
Mixtral 8x7B 13B 58.2% 77.4% 70.9% 54.3% 73.0% 71.5% 55.4% 77.6% 72.5% 52.8% 75.1% 70.9%
Table 4: Comparison of Mixtral with Llama on Multilingual Benchmarks. On ARC Challenge, Hellaswag,
and MMLU, Mixtral outperforms Llama 2 70B on 4 languages: French, German, Spanish, and Italian.
3.2 Long range performance
To assess the capabilities of Mixtral to tackle long context, we evaluate it on the passkey retrieval
task introduced in [23], a synthetic task designed to measure the ability of the model to retrieve a
passkey inserted randomly in a long prompt. Results in Figure 4 (Left) show that Mixtral achieves a
100% retrieval accuracy regardless of the context length or the position of passkey in the sequence.
Figure 4 (Right) shows that the perplexity of Mixtral on a subset of the proof-pile dataset [2] decreases
monotonically as the size of the context increases.
Figure 4: Long range performance of Mixtral. (Left) Mixtral has 100% retrieval accuracy of the Passkey task
regardless of the location of the passkey and length of the input sequence. (Right) The perplexity of Mixtral on
the proof-pile dataset decreases monotonically as the context length increases.
5
3.3 Bias Benchmarks
To identify possible flaws to be corrected
Llama 2 70B Mixtral 8x7B
by fine-tuning / preference modeling, we
measure the base model performance on BBQ accuracy 51.5% 56.0%
Bias Benchmark for QA (BBQ) [24] and BOLD sentiment score (avg ± std)
Bias in Open-Ended Language Generation
gender 0.293 ± 0.073 0.323 ±0.045
Dataset (BOLD) [10]. BBQ is a dataset
profession 0.218 ± 0.073 0.243 ± 0.087
of hand-written question sets that target
religious_ideology 0.188 ± 0.133 0.144 ± 0.089
attested social biases against nine differ-
political_ideology 0.149 ± 0.140 0.186 ± 0.146
ent socially-relevant categories: age, dis-
race 0.232 ± 0.049 0.232 ± 0.052
ability status, gender identity, nationality,
physical appearance, race/ethnicity, religion,
Figure 5: Bias Benchmarks. Compared Llama 2 70B,
socio-economic status, sexual orientation.
Mixtral presents less bias (higher accuracy on BBQ, lower
BOLD is a large-scale dataset that consists std on BOLD) and displays more positive sentiment (higher
of 23,679 English text generation prompts avg on BOLD).
for bias benchmarking across five domains.
We benchmark Llama 2 and Mixtral on BBQ and BOLD with our evaluation framework and report
the results in Table 5. Compared to Llama 2, Mixtral presents less bias on the BBQ benchmark
(56.0% vs 51.5%). For each group in BOLD, a higher average sentiment score means more positive
sentiments and a lower standard deviation indicates less bias within the group. Overall, Mixtral
displays more positive sentiments than Llama 2, with similar variances within each group.
4 Instruction Fine-tuning
We train Mixtral – Instruct using supervised fine-tuning (SFT) on an instruction dataset followed by
Direct Preference Optimization (DPO) [25] on a paired feedback dataset. Mixtral – Instruct reaches a
score of 8.30 on MT-Bench [33] (see Table 2), making it the best open-weights model as of December
2023. Independent human evaluation conducted by LMSys is reported in Figure 63 and shows that
Mixtral – Instruct outperforms GPT-3.5-Turbo, Gemini Pro, Claude-2.1, and Llama 2 70B chat.
Figure 6: LMSys Leaderboard. (Screenshot from Dec 22, 2023) Mixtral 8x7B Instruct v0.1 achieves an Arena
Elo rating of 1121 outperforming Claude-2.1 (1117), all versions of GPT-3.5-Turbo (1117 best), Gemini Pro
(1111), and Llama-2-70b-chat (1077). Mixtral is currently the best open-weights model by a large margin.
3
https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard
6
5 Routing analysis
In this section, we perform a small analysis on the expert selection by the router. In particular,
we are interested to see if during training some experts specialized to some specific domains (e.g.
mathematics, biology, philosophy, etc.).
To investigate this, we measure the distribution of selected experts on different subsets of The Pile
validation dataset [14]. Results are presented in Figure 7, for layers 0, 15, and 31 (layers 0 and 31
respectively being the first and the last layers of the model). Surprisingly, we do not observe obvious
patterns in the assignment of experts based on the topic. For instance, at all layers, the distribution of
expert assignment is very similar for ArXiv papers (written in Latex), for biology (PubMed Abstracts),
and for Philosophy (PhilPapers) documents.
Only for DM Mathematics we note a marginally different distribution of experts. This divergence is
likely a consequence of the dataset’s synthetic nature and its limited coverage of the natural language
spectrum, and is particularly noticeable at the first and last layers, where the hidden states are very
correlated to the input and output embeddings respectively.
This suggests that the router does exhibit some structured syntactic behavior. Figure 8 shows
examples of text from different domains (Python code, mathematics, and English), where each token
is highlighted with a background color corresponding to its selected expert. The figure shows that
words such as ‘self’ in Python and ‘Question’ in English often get routed through the same expert
even though they involve multiple tokens. Similarly, in code, the indentation tokens are always
assigned to the same experts, particularly at the first and last layers where the hidden states are more
correlated to the input and output of the model.
We also note from Figure 8 that consecutive tokens are often assigned the same experts. In fact, we
observe some degree of positional locality in The Pile datasets. Table 5 shows the proportion of con-
secutive tokens that get the same expert assignments per domain and layer. The proportion of repeated
layer: 0
0.20
0.15
0.10
0.05
0
Selection proportion
layer: 15
0.20
0.15
0.10
0.05
0
layer: 31
0.20
0.15
0.10
0.05
0
0 1 2 3 4 5 6 7
Expert ID
ArXiv Github PhilPapers StackExchange
DM Mathematics Gutenberg PubMed Abstracts Wikipedia (en)
Figure 7: Proportion of tokens assigned to each expert on different domains from The Pile dataset for
layers 0, 15, and 31. The gray dashed vertical line marks 1/8, i.e. the proportion expected with uniform
sampling. Here, we consider experts that are either selected as a first or second choice by the router. A
breakdown of the proportion of assignments done in each case cane be seen in Figure 9 in the Appendix.
7
First choice First or second choice
Layer 0 Layer 15 Layer 31 Layer 0 Layer 15 Layer 31
ArXiv 14.0% 27.9% 22.7% 46.5% 62.3% 52.9%
DM Mathematics 14.1% 28.4% 19.7% 44.9% 67.0% 44.5%
Github 14.9% 28.1% 19.7% 49.9% 66.9% 49.2%
Gutenberg 13.9% 26.1% 26.3% 49.5% 63.1% 52.2%
PhilPapers 13.6% 25.3% 22.1% 46.9% 61.9% 51.3%
PubMed Abstracts 14.2% 24.6% 22.0% 48.6% 61.6% 51.8%
StackExchange 13.6% 27.2% 23.6% 48.2% 64.6% 53.6%
Wikipedia (en) 14.4% 23.6% 25.3% 49.8% 62.1% 51.8%
Table 5: Percentage of expert assignment repetitions. We evaluate the proportion of times the same expert is
assigned to a token i and its following token i+1. We report whether the first chosen expert is the same, or whether
the same expert is observed as first or second choice in consecutive tokens. For reference, the expected proportion
of repetitions in the case of random assignments is 18 = 12.5% for “First choice” and 1 − 68 57 ≈ 46% for “First
and second choice”. Repetitions at the first layer are close to random, but are significantly higher at layers 15
and 31. The high number of repetitions shows that expert choice exhibits high temporal locality at these layers.
consecutive assignments is significantly higher than random for higher layers. This has implications
in how one might optimize the model for fast training and inference. For example, cases with high
locality are more likely to cause over-subscription of certain experts when doing Expert Parallelism.
Conversely, this locality can be leveraged for caching, as is done in [11]. A more complete view of
these same expert frequency is provided for all layers and across datasets in Figure 10 in the Appendix.
6 Conclusion
In this paper, we introduced Mixtral 8x7B, the first mixture-of-experts network to reach a state-of-the-
art performance among open-source models. Mixtral 8x7B Instruct outperforms Claude-2.1, Gem-
ini Pro, and GPT-3.5 Turbo on human evaluation benchmarks. Because it only uses two experts at each
time step, Mixtral only uses 13B active parameters per token while outperforming the previous best
model using 70B parameters per token (Llama 2 70B). We are making our trained and fine-tuned mod-
els publicly available under the Apache 2.0 license. By sharing our models, we aim to facilitate the de-
velopment of new techniques and applications that can benefit a wide range of industries and domains.
Figure 8: Text samples where each token is colored with the first expert choice. The selection of experts
appears to be more aligned with the syntax rather than the domain, especially at the initial and final layers.
8
Acknowledgements
We thank the CoreWeave and Scaleway teams for technical support as we trained our models. We
are grateful to NVIDIA for supporting us in integrating TensorRT-LLM and Triton and working
alongside us to make a sparse mixture of experts compatible with TensorRT-LLM.
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Layer 0 -- Either choice
0.3
0.2
0.1
0
Layer 0 -- First choice
0.3
0.2
0.1
0
Layer 0 -- Second choice
0.3
0.2
0.1
0
Layer 15 -- Either choice
0.3
0.2
0.1
0
Selection proportion
Layer 15 -- First choice
0.3
0.2
0.1
0
Layer 15 -- Second choice
0.3
0.2
0.1
0
Layer 31 -- Either choice
0.3
0.2
0.1
0
Layer 31 -- First choice
0.3
0.2
0.1
0
Layer 31 -- Second choice
0.3
0.2
0.1
0
0 1 2 3 4 5 6 7
Expert ID
ArXiv Github PhilPapers StackExchange
DM Mathematics Gutenberg PubMed Abstracts Wikipedia (en)
Figure 9: Proportion of tokens assigned to each expert on different subsets from The Pile dataset, separated
by whether the expert was selected as first or second choice, or either. The “Either choice” case is equivalent
to Figure 7. The gray dashed vertical line marks 81 , i.e. the proportion expected with uniform sampling.
12
First choice
0.35
0.30
Proportion of repeated assignments
0.25
source
0.20 ArXiv
DM Mathematics
0.15
Github
First or second choice Gutenberg
PhilPapers
0.7 PubMed Abstracts
StackExchange
0.6 Wikipedia (en)
0.5
0 10 20 30
Layer
Figure 10: Repeated consecutive assignments per MoE layer. Repeated assignments occur a lot more
often than they would with uniform assignments (materialized by the dashed lines). Patterns are similar across
datasets with less repetitions for DM Mathematics.
13
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