Qwen2 57B A14B Instruct — Hardware Requirements & GPU Compatibility
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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
- 2024-08-21
- 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 |
|---|---|---|---|---|---|
| Q4_0 | 4.00 | 29.1 GB | 30.9 GB | 28.70 GB | 4-bit legacy quantization |
| Q4_1 | 4.50 | 32.7 GB | 34.5 GB | 32.29 GB | 4-bit legacy quantization with offset |
| Q5_0 | 5.00 | 36.3 GB | 38.1 GB | 35.88 GB | 5-bit legacy quantization |
| Q5_1 | 5.50 | 39.9 GB | 41.6 GB | 39.47 GB | 5-bit legacy quantization with offset |
| Q8_0 | 8.00 | 57.8 GB | 59.6 GB | 57.41 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Qwen2 57B A14B Instruct?
Q4_0 · 29.1 GBQwen2 57B A14B Instruct (Q4_0) requires 29.1 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 38+ GB is recommended. Using the full 33K context window can add up to 1.8 GB, bringing total usage to 30.9 GB. 1 GPU can run it, including NVIDIA GeForce RTX 5090.
All compatible consumer-level GPUs are running near their VRAM limit. You may also want to consider professional GPUs (e.g., NVIDIA A100, H100) which offer significantly more VRAM. For more headroom and better throughput, consider a multi-GPU configuration with tensor parallelism (supported by tools like vLLM, llama.cpp, or text-generation-inference).
Decent
— Enough VRAM, may be tightWhich Devices Can Run Qwen2 57B A14B Instruct?
Q4_0 · 29.1 GB15 devices with unified memory can run Qwen2 57B A14B Instruct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio M4 Max (36 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does Qwen2 57B A14B Instruct need?
Qwen2 57B A14B Instruct requires 29.1 GB of VRAM at Q4_0, or 57.8 GB at Q8_0. Full 33K context adds up to 1.8 GB (30.9 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 57.4B × 4 bits ÷ 8 = 28.7 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 2.2 GB (at full 33K context)
VRAM usage by quantization
Q4_029.1 GBQ4_0 + full context30.9 GB- Can NVIDIA GeForce RTX 5090 run Qwen2 57B A14B Instruct?
Yes, at Q4_0 (29.1 GB) or lower. Higher quantizations like Q4_1 (32.7 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, Q5_0 (36.3 GB) offers the best balance of quality and VRAM usage. Q5_1 (39.9 GB) provides better quality if you have the VRAM. The smallest option is Q4_0 at 29.1 GB.
VRAM requirement by quantization
Q4_029.1 GB~85%Q4_132.7 GB~88%Q5_0 ★36.3 GB~90%Q5_139.9 GB~92%Q8_057.8 GB~99%★ 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 29.1 GB at Q4_0, 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_0 quantization it needs 29.1 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen2 57B A14B Instruct?
At Q4_0, Qwen2 57B A14B Instruct can reach ~100 tok/s on AMD Instinct MI300X. 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 ÷ 29.1 × 0.55 = ~100 tok/s
Estimated speed at Q4_0 (29.1 GB)
AMD Instinct MI300X~100 tok/sNVIDIA H100 SXM~75 tok/sAMD Instinct MI250X~62 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_0, the download is about 28.70 GB. The full-precision Q8_0 version is 57.41 GB.
- Which GPUs can run Qwen2 57B A14B Instruct?
1 consumer GPU can run Qwen2 57B A14B Instruct at Q4_0 (29.1 GB). Top options include NVIDIA GeForce RTX 5090.
- Which devices can run Qwen2 57B A14B Instruct?
15 devices with unified memory can run Qwen2 57B A14B Instruct at Q4_0 (29.1 GB), including Mac Mini M4 (32 GB), Mac Mini M4 Pro (48 GB), Mac Pro M2 Ultra (192 GB), Mac Studio M2 Ultra (192 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.