Qwen2 57B A14B Instruct — Hardware Requirements & GPU Compatibility
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Qwen2 57B A14B Instruct is a 57.4B-parameter open language model from Alibaba in the Qwen 2 family. It supports a context window of up to 32,768 tokens. At Q4_K_M it needs about 34.86 GB of VRAM — see which GPUs and Macs can run it below.
16.0K downloads
83 likesSpecifications
- Publisher
- Alibaba
- Family
- Qwen 2
- Parameters
- 57.4B
- Architecture
- Qwen2MoeForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2024-06-04
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Qwen2 57B A14B Instruct Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 24.8 GB | 26.6 GB | 24.40 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 28.4 GB | 30.2 GB | 27.99 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 34.9 GB | 36.6 GB | 34.45 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 41.3 GB | 43.1 GB | 40.90 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 47.8 GB | 49.5 GB | 47.36 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 57.8 GB | 59.6 GB | 57.41 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 115.2 GB | 117 GB | 114.82 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 Qwen2 57B A14B Instruct?
Q4_K_M · 34.9 GBQwen2 57B A14B Instruct (Q4_K_M) requires 34.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 46+ GB is recommended. Using the full 33K context window can add up to 1.8 GB, bringing total usage to 36.6 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run Qwen2 57B A14B Instruct?
Q4_K_M · 34.9 GB29 devices with unified memory can run Qwen2 57B A14B Instruct, 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 Qwen2 57B A14B Instruct need?
Qwen2 57B A14B Instruct requires 34.9 GB of VRAM at Q4_K_M, or 115.2 GB at BF16. Full 33K context adds up to 1.8 GB (36.6 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 57.4B × 4.8 bits ÷ 8 = 34.4 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 2.2 GB (at full 33K context)
VRAM usage by quantization
Q4_K_M34.9 GBQ4_K_M + full context36.6 GB- Can NVIDIA GeForce RTX 5090 run Qwen2 57B A14B Instruct?
Yes, at Q3_K_M (28.4 GB) or lower. Higher quantizations like Q4_K_M (34.9 GB) exceed the NVIDIA GeForce RTX 5090's 32 GB.
- What's the best quantization for Qwen2 57B A14B Instruct?
For Qwen2 57B A14B Instruct, Q4_K_M (34.9 GB) offers the best balance of quality and VRAM usage. Q5_K_M (41.3 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 24.8 GB.
VRAM requirement by quantization
Q2_K24.8 GBQ4_K_M ★34.9 GBQ5_K_M41.3 GBQ6_K47.8 GBQ8_057.8 GBBF16115.2 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen2 57B A14B Instruct on a Mac?
Qwen2 57B A14B Instruct requires at least 24.8 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 Qwen2 57B A14B Instruct locally?
Yes — Qwen2 57B A14B Instruct can run locally on consumer hardware. At Q4_K_M quantization it needs 34.9 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen2 57B A14B Instruct?
At Q4_K_M, Qwen2 57B A14B Instruct can reach ~126 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 ÷ 34.9 × 0.65 = ~149 tok/s
Estimated speed at Q4_K_M (34.9 GB)
~149 tok/s~149 tok/s~126 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Qwen2 57B A14B Instruct?
At Q4_K_M, the download is about 34.45 GB. The full-precision BF16 version is 114.82 GB. The smallest option (Q2_K) is 24.40 GB.
- Which GPUs can run Qwen2 57B A14B Instruct?
No single consumer GPU has enough VRAM to run Qwen2 57B A14B Instruct at Q4_K_M (34.9 GB). Multi-GPU or professional hardware is required.
- Which devices can run Qwen2 57B A14B Instruct?
29 devices with unified memory can run Qwen2 57B A14B Instruct at Q4_K_M (34.9 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.