Mixtral 8x7B vs LLaMA 3 70B: MoE vs Dense

MoE vs Dense: The Architecture Divide

Mixtral 8x7B and LLaMA 3 70B represent fundamentally different approaches to building large language models. Mixtral uses a Mixture-of-Experts (MoE) design that activates only 2 of its 8 expert layers per token, keeping compute costs closer to a 13B model despite having 46.7B total parameters. LLaMA 3 70B is a dense transformer where every parameter participates in every forward pass. On a dedicated GPU server, this architecture difference has real implications for VRAM, throughput, and cost.

For model-specific hosting details, visit our Mistral hosting and LLaMA hosting pages.

Side-by-Side Specifications

The key trade-off: Mixtral needs less compute per token (12.9B active vs 70.6B) but still must load all 46.7B parameters into VRAM. LLaMA 3 70B requires both more VRAM and more compute but delivers denser representations.

Quality Benchmarks

LLaMA 3 70B wins every quality benchmark, often by a substantial margin. This is expected given it has 5.5 times more active parameters per token. The question is whether the throughput and cost advantages of Mixtral compensate. Visit our benchmarks hub for more GPU-specific data.

GPU Performance and VRAM

Both models were tested on dual RTX 3090 GPUs (48 GB total) with vLLM and tensor parallelism. See the tokens-per-second benchmark tool for current numbers.

Mixtral’s MoE architecture delivers over 2x the throughput of LLaMA 3 70B at Q4, despite needing fewer GPUs. The VRAM advantage is also significant: 26 GB vs 42 GB at 4-bit means Mixtral can run on a single high-end card while LLaMA 3 70B always needs at least two. Consult our LLaMA 3 VRAM guide for full sizing tables.

Hosting Cost Analysis

Use our cost-per-million-tokens calculator for exact numbers. At typical UK dedicated server pricing:

On identical hardware, Mixtral delivers tokens at less than half the cost. The quality gap means LLaMA 3 70B may still be cheaper per useful output for accuracy-critical tasks, but for high-throughput workloads Mixtral’s efficiency is compelling.

Which Architecture Wins?

Choose Mixtral 8x7B for throughput-sensitive workloads, tighter GPU budgets, 32K context requirements, and Apache 2.0 licensing. It is the better value when you need to maximise tokens per second per dollar. See our Run Mixtral 8x7B on RTX 3090 guide for setup details.

Choose LLaMA 3 70B when output quality is the top priority. It significantly outperforms Mixtral on every benchmark and is the better choice for complex reasoning, code generation, and accuracy-critical production systems.

For the broader model comparison landscape, explore the GPU comparisons category. Also see our self-host LLM guide for deployment best practices.