Mixtral 8x7B Instruct V0 1 AQLM 2Bit 1x16 HF — Hardware Requirements & GPU Compatibility
Chat
13 downloads
19 likesSpecifications
- Publisher
- ISTA-DASLab
- Family
- Mixtral
- Parameters
- 6.5B
- Architecture
- MixtralForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 32,000
- Release Date
- 2024-02-27
Get Started
How Much VRAM Does Mixtral 8x7B Instruct V0 1 AQLM 2Bit 1x16 HF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| FP16 | 16.00 | 13.7 GB | 17.7 GB | 13.09 GB | Full half-precision — baseline for inference |
Which GPUs Can Run Mixtral 8x7B Instruct V0 1 AQLM 2Bit 1x16 HF?
FP16 · 13.7 GBMixtral 8x7B Instruct V0 1 AQLM 2Bit 1x16 HF (FP16) requires 13.7 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 18+ GB is recommended. Using the full 33K context window can add up to 4.0 GB, bringing total usage to 17.7 GB. 17 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 5080.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Mixtral 8x7B Instruct V0 1 AQLM 2Bit 1x16 HF?
FP16 · 13.7 GB27 devices with unified memory can run Mixtral 8x7B Instruct V0 1 AQLM 2Bit 1x16 HF, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 (16 GB).
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Mixtral 8x7B Instruct V0 1 AQLM 2Bit 1x16 HF need?
Mixtral 8x7B Instruct V0 1 AQLM 2Bit 1x16 HF requires 13.7 GB of VRAM at FP16. Full 33K context adds up to 4.0 GB (17.7 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 6.5B × 16 bits ÷ 8 = 13.1 GB
KV Cache + Overhead ≈ 0.6 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 4.6 GB (at full 33K context)
VRAM usage by quantization
FP1613.7 GBFP16 + full context17.7 GB- Can I run Mixtral 8x7B Instruct V0 1 AQLM 2Bit 1x16 HF on a Mac?
Mixtral 8x7B Instruct V0 1 AQLM 2Bit 1x16 HF requires at least 13.7 GB at FP16, 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 Mixtral 8x7B Instruct V0 1 AQLM 2Bit 1x16 HF locally?
Yes — Mixtral 8x7B Instruct V0 1 AQLM 2Bit 1x16 HF can run locally on consumer hardware. At FP16 quantization it needs 13.7 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Mixtral 8x7B Instruct V0 1 AQLM 2Bit 1x16 HF?
At FP16, Mixtral 8x7B Instruct V0 1 AQLM 2Bit 1x16 HF can reach ~213 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~48 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 13.7 × 0.55 = ~213 tok/s
Estimated speed at FP16 (13.7 GB)
AMD Instinct MI300X~213 tok/sNVIDIA GeForce RTX 4090~48 tok/sNVIDIA H100 SXM~160 tok/sAMD Instinct MI250X~132 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Mixtral 8x7B Instruct V0 1 AQLM 2Bit 1x16 HF?
At FP16, the download is about 13.09 GB.
- Which GPUs can run Mixtral 8x7B Instruct V0 1 AQLM 2Bit 1x16 HF?
17 consumer GPUs can run Mixtral 8x7B Instruct V0 1 AQLM 2Bit 1x16 HF at FP16 (13.7 GB). Top options include AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090, NVIDIA GeForce RTX 3090 Ti, AMD Radeon RX 6800. 5 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Mixtral 8x7B Instruct V0 1 AQLM 2Bit 1x16 HF?
27 devices with unified memory can run Mixtral 8x7B Instruct V0 1 AQLM 2Bit 1x16 HF at FP16 (13.7 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.