Mixtral 8x7B Instruct from Mistral AI was the first open Mixture-of-Experts model with credible chat capability. 47 B total parameters but only 13 B activate per token, so it punches at the level of a much bigger model while running at the speed of a 13 B. Needs ~28 GB VRAM at Q4, which lands it on dual-3090 or single A6000 territory.
| Quantization | Bits | File Size | VRAM Needed | RAM Needed | Quality |
|---|---|---|---|---|---|
| Q4_K_M | 4.5 | 24.626 GB | 25.13 GB | 25.63 GB | 85% |
| Q5_K_M | 5.5 | 30.016 GB | 30.52 GB | 31.02 GB | 92% |
Context window & KV cache
Adds 2.50 GB to VRAMLong chats and RAG inputs cost real memory. Drag to see how 32K vs 128K context shifts your grade.
Model native max: 32K tokens. KV-cache estimate is approximate (±30 %); real usage depends on attention layout.
How to run Mixtral 8x7B Instruct
Pick a runtime — copy & paste. Commands are pre-filled with this model’s repo.
GUI. Browse → download → chat. MLX on Apple Silicon.
LM Studio home →- 1
Open LM Studio
Go to the 🔍 Search tab.
- 2
Search for
TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF - 3
Download
Pick the Q4_K_M quant — best balance of size vs. quality.
- 4
Chat
Hit ▶ Load Model and start chatting. Toggle 'Local Server' to expose an OpenAI-compatible API on :1234.
Community benchmarks
Real tokens/sec reports from people running Mixtral 8x7B Instruct on actual hardware.
No community runs yet for this model. Be the first to submit your numbers.
Self-host serving plan
Want to host Mixtral 8x7B Instructfor many users? Or run it on a card that’s technically too small? Slide the knobs.
VRAM needed
27.3 GB
25.1 GB weights + 1.7 GB KV
Aggregate tok/s
13
across 1 user
Per-user tok/s
13
MoE active params
⚠ Will spill 3.3 GB of weights to system RAM (~5× slower per offloaded layer). Use llama.cpp’s --cpu-offload-gb or vLLM’s --swap-space.
Throughput is a sub-linear estimate: doubling users adds ~70 % of single-user TPS until ~8, then plateaus on memory bandwidth. MoE models scale concurrency much better because each user activates a different subset of experts.
See It In Action
Real model outputs generated via RunThisModel.com — watch responses stream in real time.
Outputs generated by real AI models via RunThisModel.com. Generation speed shown is from cloud inference. Local speeds vary by hardware — check your device.
how much VRAM do I need to run Mixtral 8x7B Instruct?
Mixtral 8x7B Instruct requires 25.13 GB VRAM minimum with Q4_K_M quantization. For full precision you need 30.52 GB.
which quant should I pick?
Q4_K_M is the best quality/VRAM balance — ~92% of FP16 quality at ~25% the footprint. Q8_0 is near-lossless if you have the headroom.
What GPU do I need to run Mixtral 8x7B Instruct?
To run Mixtral 8x7B Instruct, you need a GPU with at least 25.1 GB of VRAM, but 30.5 GB is recommended for optimal performance.
Is Mixtral 8x7B Instruct good for coding?
Mixtral 8x7B Instruct is well-suited for coding tasks due to its large context length of 32,768 tokens and strong language understanding capabilities.
Mixtral 8x7B Instruct vs Llama 3.1 8B?
Mixtral 8x7B Instruct has more parameters (46.7B vs 8B) and a longer context length (32,768 vs 2,048), making it more powerful for complex tasks but requiring more VRAM.
Can I run Mixtral 8x7B Instruct on a Mac?
Yes, you can run Mixtral 8x7B Instruct on a Mac, but you will need a Mac with an M1 or later chip and sufficient VRAM to handle the model's requirements.
How much VRAM does Mixtral 8x7B Instruct need?
Mixtral 8x7B Instruct requires between 25.1 GB and 30.5 GB of VRAM, depending on the quantization level used.
Is Mixtral 8x7B Instruct censored?
No, Mixtral 8x7B Instruct is not censored; it provides uncensored responses based on the input it receives.
Is Mixtral 8x7B Instruct commercial-use allowed?
Yes, Mixtral 8x7B Instruct is licensed under the Apache-2.0 license, which allows for commercial use.
Mixtral 8x7B Instruct context length?
The context length of Mixtral 8x7B Instruct is 32,768 tokens, allowing it to handle very long inputs and maintain context over extended conversations.
Does Mixtral 8x7B Instruct support function calling?
Yes, Mixtral 8x7B Instruct supports function calling, enabling it to interact with external systems and perform specific tasks.
Mixtral 8x7B Instruct quantization options?
Mixtral 8x7B Instruct supports various quantization options, including 4-bit, 8-bit, and 16-bit, to reduce VRAM usage and improve performance.
Can Mixtral 8x7B Instruct run on CPU?
While Mixtral 8x7B Instruct can technically run on a CPU, it is highly inefficient and not recommended due to the high computational demands of the model.
Mixtral 8x7B Instruct fine-tuning?
Yes, Mixtral 8x7B Instruct can be fine-tuned on custom datasets to improve performance on specific tasks or domains.
Mixtral 8x7B Instruct system requirements?
To run Mixtral 8x7B Instruct, you need a GPU with 25.1 GB to 30.5 GB of VRAM, at least 64 GB of RAM, and a powerful CPU to handle data preprocessing and post-processing.
Mixtral 8x7B Instruct performance benchmark?
Performance benchmarks for Mixtral 8x7B Instruct vary, but it generally processes around 100-150 tokens per second on a high-end GPU with 30.5 GB VRAM.
Mixtral 8x7B Instruct for RAG?
Yes, Mixtral 8x7B Instruct can be used for Retrieval-Augmented Generation (RAG) to enhance its responses with context from external sources.
Mixtral 8x7B Instruct for agents?
Mixtral 8x7B Instruct is suitable for creating conversational agents due to its large context length and ability to maintain coherent dialogues over multiple turns.
Mixtral 8x7B Instruct for coding vs general?
Mixtral 8x7B Instruct performs well in both coding and general tasks, but its large context length makes it particularly effective for handling complex programming scenarios.
Mixtral 8x7B Instruct vs ChatGPT?
Mixtral 8x7B Instruct has more parameters (46.7B vs 175B for the largest ChatGPT model) and a longer context length (32,768 vs 4,096), making it more suitable for specialized tasks but requiring more VRAM.
Mixtral 8x7B Instruct download size?
The download size of Mixtral 8x7B Instruct varies depending on the quantization level, ranging from approximately 12 GB (4-bit) to 93 GB (16-bit).
Best quant for Mixtral 8x7B Instruct?
The best quantization for Mixtral 8x7B Instruct depends on your hardware. For most users, 8-bit quantization offers a good balance between performance and VRAM efficiency.