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.
Doesn't fit a 24 GB GPU
Smallest consumer fit → Snapdragon X Elite Copilot+ PC · Q2_K · 25.6 GB · ~14 tok/s
Macs from 32 GB (Q2_K · ~36 tok/s)
Apache 2.0
Not set up for it?
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Qwen2 57B A14B Instruct
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
vllm serve Qwen/Qwen2-57B-A14B-Instruct --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on RTX A6000 (48 GB) (12 GB headroom) · Q4_K_M
- Generation speed
- ~53 tok/s
- generation speed
- Cost per 1M output tokens
- $1.73
- per 1M output tokens
How Much VRAM Does Qwen2 57B A14B Instruct Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 24.8 GB | 26.6 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Does not fit: needs 25.6 GB | |
| 28.4 GB | 30.2 GB | 3-bit medium — 3-bit medium quantization | Does not fit: needs 29.2 GB | |
| 34.9 GB | 36.6 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Does not fit: needs 35.7 GB | |
| 41.3 GB | 43.1 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Does not fit: needs 42.1 GB | |
| 47.8 GB | 49.5 GB | 6-bit, very good — 6-bit quantization, very good quality | Does not fit: needs 48.6 GB | |
| 57.8 GB | 59.6 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Does not fit: needs 58.7 GB | |
| 115.2 GB | 117 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Does not fit: needs 116.1 GB |
Selected Q4_K_M
4-bit medium quantization35.7GBat 16K context
- Weights
- 34.4 GB
- KV cache
- 0.9 GB
- Overhead
- 0.3 GB
Doesn't fit RTX 4090 (reference) · needs 11.7 GB more
Or .
Which Hardware 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 consumer GPU has enough memory. 67 devices with unified memory can run Qwen2 57B A14B Instruct, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Studio M5 Max (48 GB).
32–40 GB
Q4_K_M · best fit Q3_K_M (29.2 GB)
1 GPU · 3 Mac classes · 1 device family
RTX 5090~132Simulate output on RTX 5090M5 Max36 GB~36Simulate output on Mac Studio M5 Max (36 GB)M532 GB~14Simulate output on MacBook Air 13" M5 (32 GB)Orin~18Simulate output on NVIDIA Jetson AGX Orin 32GB48 GB
48 GB tier →fits · 12.3 GB free · up to 32K ctx
2 Mac classes · 1 device family
M5 Max48 GB~39Simulate output on Mac Studio M5 Max (48 GB)M5 Pro48 GB~22Simulate output on Mac Mini M5 Pro (48 GB)Snapdragon X2 Elite Extreme (X2E-96-100)~16Simulate output on Snapdragon X2 Elite Extreme Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Qwen2 57B A14B Instruct
Qwen 2 family · 13 models →Similar models
1| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Mixtral 34Bx2 MoE 60Bcloudyu | — | 40.8 GB | —needs > 24 GB | same size |
Specifications
- Publisher
- Alibaba
- Family
- Qwen 2
- Parameters
- 57.4B
- Active parameters
- 14.2B per token
- Architecture
- Qwen2MoeForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2024-06-04
- License
- Apache 2.0
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)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 2.2 GB (at full 33K context)
VRAM usage by quantization
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
★ Recommended — best balance of quality and VRAM usage.
Can I run Qwen2 57B A14B Instruct on a Mac?
Yes — Mac Studio M1 Max (32 GB) and 84 other Macs can run Qwen2 57B A14B Instruct, the smallest at Q2_K. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 34.9 GB of usable unified memory (RAM minus macOS overhead), from Mac Studio M5 Max (48 GB).
Can I run Qwen2 57B A14B Instruct locally?
Yes — Qwen2 57B A14B Instruct can run locally on consumer hardware. 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 ~135 tok/s on AMD Instinct MI350X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = 1000 ÷ (active GB ÷ (bandwidth GB/s × efficiency) × 1000 + layers × routing ms)
Mixture-of-Experts: just the active experts get read for each token, along with a fixed routing cost per layer.
Example: NVIDIA B200 → 8.7 GB active ÷ (8000 × 0.65) = 1.664 ms, plus 28 layers × 0.055 ms = 1.540 ms, so 1000 ÷ 3.204 ms = ~312 tok/s
Estimated speed at Q4_K_M (34.9 GB)
Real-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?
67 devices with unified memory can run Qwen2 57B A14B Instruct at Q4_K_M (34.9 GB), including ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB), GMKtec EVO-X2 (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.