Mixtral 34Bx2 MoE 60B — Hardware Requirements & GPU Compatibility
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Mixtral 34Bx2 MoE 60B is a 60.8B-parameter open language model from cloudyu in the Mixtral family. It supports a context window of up to 200,000 tokens. At Q4_K_M it needs about 37.29 GB of VRAM — see which GPUs and Macs can run it below.
8.2K downloads
113 likesSpecifications
- Publisher
- cloudyu
- Family
- Mixtral
- Parameters
- 60.8B
- Architecture
- MixtralForCausalLM
- Context Length
- 200,000 tokens
- Vocabulary Size
- 64,000
- Release Date
- 2024-01-05
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Mixtral 34Bx2 MoE 60B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 26.6 GB | 75.3 GB | 25.85 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 30.4 GB | 79.1 GB | 29.65 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 37.3 GB | 85.9 GB | 36.49 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 44.1 GB | 92.8 GB | 43.33 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 51.0 GB | 99.6 GB | 50.17 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 61.6 GB | 110.3 GB | 60.81 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 122.4 GB | 171.1 GB | 121.63 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 Mixtral 34Bx2 MoE 60B?
Q4_K_M · 37.3 GBMixtral 34Bx2 MoE 60B (Q4_K_M) requires 37.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 49+ GB is recommended. Using the full 200K context window can add up to 48.7 GB, bringing total usage to 85.9 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run Mixtral 34Bx2 MoE 60B?
Q4_K_M · 37.3 GB27 devices with unified memory can run Mixtral 34Bx2 MoE 60B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Pro 16" M4 Max (48 GB).
Runs great
— Plenty of headroom
Related Models
Frequently Asked Questions
- How much VRAM does Mixtral 34Bx2 MoE 60B need?
Mixtral 34Bx2 MoE 60B requires 37.3 GB of VRAM at Q4_K_M, or 122.4 GB at BF16. Full 200K context adds up to 48.6 GB (85.9 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 60.8B × 4.8 bits ÷ 8 = 36.5 GB
KV Cache + Overhead ≈ 0.8 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 49.4 GB (at full 200K context)
VRAM usage by quantization
Q4_K_M37.3 GBQ4_K_M + full context85.9 GB- Can NVIDIA GeForce RTX 5090 run Mixtral 34Bx2 MoE 60B?
Yes, at Q3_K_M (30.4 GB) or lower. Higher quantizations like Q4_K_M (37.3 GB) exceed the NVIDIA GeForce RTX 5090's 32 GB.
- What's the best quantization for Mixtral 34Bx2 MoE 60B?
For Mixtral 34Bx2 MoE 60B, Q4_K_M (37.3 GB) offers the best balance of quality and VRAM usage. Q5_K_M (44.1 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 26.6 GB.
VRAM requirement by quantization
Q2_K26.6 GBQ4_K_M ★37.3 GBQ5_K_M44.1 GBQ6_K51.0 GBQ8_061.6 GBBF16122.4 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Mixtral 34Bx2 MoE 60B on a Mac?
Mixtral 34Bx2 MoE 60B requires at least 26.6 GB at Q2_K, 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 34Bx2 MoE 60B locally?
Yes — Mixtral 34Bx2 MoE 60B can run locally on consumer hardware. At Q4_K_M quantization it needs 37.3 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Mixtral 34Bx2 MoE 60B?
At Q4_K_M, Mixtral 34Bx2 MoE 60B can reach ~118 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 ÷ 37.3 × 0.65 = ~139 tok/s
Estimated speed at Q4_K_M (37.3 GB)
~139 tok/s~139 tok/s~118 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Mixtral 34Bx2 MoE 60B?
At Q4_K_M, the download is about 36.49 GB. The full-precision BF16 version is 121.63 GB. The smallest option (Q2_K) is 25.85 GB.
- Which GPUs can run Mixtral 34Bx2 MoE 60B?
No single consumer GPU has enough VRAM to run Mixtral 34Bx2 MoE 60B at Q4_K_M (37.3 GB). Multi-GPU or professional hardware is required.
- Which devices can run Mixtral 34Bx2 MoE 60B?
27 devices with unified memory can run Mixtral 34Bx2 MoE 60B at Q4_K_M (37.3 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.