Thealth Mixtral 8x7B — Hardware Requirements & GPU Compatibility
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Thealth Mixtral 8x7B is a 8x7B-parameter open language model from TachyHealth in the Mixtral family. At BF16 it needs about 123.20 GB of VRAM — see which GPUs and Macs can run it below.
9 likesSpecifications
- Publisher
- TachyHealth
- Family
- Mixtral
- Parameters
- 8x7B
- Release Date
- 2023-12-19
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Thealth Mixtral 8x7B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 123.2 GB | — | 112.00 GB | Brain floating point 16 — preferred for training |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run Thealth Mixtral 8x7B?
BF16 · 123.2 GBThealth Mixtral 8x7B (BF16) requires 123.2 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 161+ GB is recommended. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run Thealth Mixtral 8x7B?
BF16 · 123.2 GB8 devices with unified memory can run Thealth Mixtral 8x7B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Pro 16" M5 Max (128 GB).
Runs great
— Plenty of headroom
Decent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does Thealth Mixtral 8x7B need?
Thealth Mixtral 8x7B requires 123.2 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 8x7B × 16 bits ÷ 8 = 112 GB
KV Cache + Overhead ≈ 11.2 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
BF16123.2 GB- Can NVIDIA GeForce RTX 5090 run Thealth Mixtral 8x7B?
No — Thealth Mixtral 8x7B requires at least 123.2 GB at BF16, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- Can I run Thealth Mixtral 8x7B on a Mac?
Thealth Mixtral 8x7B requires at least 123.2 GB at BF16, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run Thealth Mixtral 8x7B locally?
Yes — Thealth Mixtral 8x7B can run locally on consumer hardware. At BF16 quantization it needs 123.2 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Thealth Mixtral 8x7B?
At BF16, Thealth Mixtral 8x7B can reach ~59 tok/s on AMD Instinct MI350X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 123.2 × 0.65 = ~106 tok/s
Estimated speed at BF16 (123.2 GB)
~106 tok/s~106 tok/s~73 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Thealth Mixtral 8x7B?
At BF16, the download is about 112.00 GB.
- Which GPUs can run Thealth Mixtral 8x7B?
No single consumer GPU has enough VRAM to run Thealth Mixtral 8x7B at BF16 (123.2 GB). Multi-GPU or professional hardware is required.
- Which devices can run Thealth Mixtral 8x7B?
18 devices with unified memory can run Thealth Mixtral 8x7B at BF16 (123.2 GB), including ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.