Synced from Hugging Face — 2026-10-28
How much VRAM does Mixtral-8x7B-Instruct-v0.1-GGUF need?
Exact on-disk weight sizes below are pulled directly from Hugging Face. A total VRAM estimate isn't shown for this model — see why below.
By quantization
Precise VRAM estimations are not yet available for this specific architecture or parameter size.
Exact on-disk weight sizes for each quantization are still listed below — only the total VRAM estimate (KV cache + overhead) is withheld, because none of our reference architectures are a close enough match to produce a reliable number.
Q2_K
14.57 GB exact, on disk
Q3_K_M
18.96 GB exact, on disk
Q4_0
24.63 GB exact, on disk
Q4_K_M
24.63 GB exact, on disk
Q5_0
30.02 GB exact, on disk
Q5_K_M
30.02 GB exact, on disk
Q6_K
35.74 GB exact, on disk
Q8_0
46.22 GB exact, on disk
Deploy this locally
A minimal Docker Compose stack to get Ollama and Open WebUI running. Free to copy and run as-is.
services:
ollama:
image: ollama/ollama:latest
container_name: ollama
restart: unless-stopped
ports:
- "11434:11434"
volumes:
- ollama-data:/root/.ollama
open-webui:
image: ghcr.io/open-webui/open-webui:main
container_name: open-webui
restart: unless-stopped
depends_on:
- ollama
ports:
- "3000:8080"
environment:
- OLLAMA_BASE_URL=http://ollama:11434
volumes:
- open-webui-data:/app/backend/data
volumes:
ollama-data:
open-webui-data:
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FAQ
How is VRAM calculated for Mixtral-8x7B-Instruct-v0.1-GGUF?
Total VRAM combines three things: the model weight size (exact for synced quantizations, from the real .gguf file), the KV cache size (always estimated — see /methodology for why), and a runtime overhead margin of 10% of (weights + KV cache), a rule-of-thumb allowance for CUDA context and activation buffers rather than a fixed number, since it scales with model size.
Why does context length drastically change VRAM requirements?
The KV cache stores attention history so the model doesn't recompute earlier tokens on every step. KV cache size scales linearly with context length, so doubling context roughly doubles that portion of VRAM use. On larger models this can add many gigabytes as context grows from 4K toward 32K or 128K tokens — see the calculator above to see the real numbers for Mixtral-8x7B-Instruct-v0.1-GGUF at different context lengths.
What happens if my GPU doesn't have enough VRAM for Mixtral-8x7B-Instruct-v0.1-GGUF?
Runners like llama.cpp or Ollama can offload remaining layers to system RAM over the PCIe bus. Execution still works, but system RAM bandwidth (roughly 60–80 GB/s for dual-channel DDR5) is far slower than GPU VRAM (roughly 1,000 GB/s), so generation speed typically drops sharply — often from tens of tokens per second down to single digits — the more layers get offloaded.
How can I fit a model into limited VRAM?
A few real options: drop to a smaller quantization (e.g. Q4_K_M instead of Q8_0), reduce context length (KV cache scales with it directly), or quantize the KV cache itself (llama.cpp's --cache-type-k q8_0 --cache-type-v q8_0) to roughly halve KV cache memory. That last option is a real tradeoff, not free — it can introduce a small amount of quality degradation, generally minor for most use cases but worth testing on your own workload before relying on it.