Can You Run Qwen3-235B-A22B? VRAM Requirements
Qwen3-235B-A22B has 235B total parameters (MoE, 22B active per token). Running it at Q4_K_M (GGUF) with 16K context takes roughly 147 GB of memory (134 GB weights + 2.9 GB KV cache + runtime overhead). The minimum viable hardware is about AMD MI300X 192GB. All numbers are planning estimates, verified 2026-07-27.
VRAM by quantization (16K context)
| Quantization | Weights | Total needed | Minimum hardware |
|---|---|---|---|
| FP16 / BF16 | 438 GB | 476 GB | 7× H100 80GB |
| FP8 | 219 GB | 239 GB | Mac M3 Ultra 512GB |
| Q8_0 (GGUF) | 232 GB | 253 GB | Mac M3 Ultra 512GB |
| Q6_K (GGUF) | 179 GB | 197 GB | Mac M3 Ultra 512GB |
| Q5_K_M (GGUF) | 155 GB | 171 GB | AMD MI300X 192GB |
| MXFP4 | 116 GB | 128 GB | H200 141GB |
| Q4_K_M (GGUF)recommended | 134 GB | 147 GB | AMD MI300X 192GB |
| Q3_K_M (GGUF) | 103 GB | 114 GB | Mac 128GB unified |
| Q2_K (GGUF) | 76.6 GB | 85.7 GB | Mac 128GB unified |
How context length changes memory (Q4_K_M (GGUF))
| Context | KV cache | Total needed |
|---|---|---|
| 4K | 0.7 GB | 145 GB |
| 16K | 2.9 GB | 147 GB |
| 64K | 11.8 GB | 156 GB |
| 128K | 23.5 GB | 168 GB |
KV cache grows linearly with context; FP8 KV cache halves it again (try it in the calculator below).
Customize the estimate
LLM VRAM Calculator — Can I Run It?
Estimate how much GPU memory (VRAM) you need to run open-weight LLMs like Kimi K3, DeepSeek R1, Qwen3, or Llama locally. Pick a model, quantization, and context length — the calculator adds up weights, KV cache, and runtime overhead, then shows which GPUs or Macs can fit it. Runs entirely in your browser: no upload, no signup.
235B total params · 22B active (MoE) · max 128K context
Recommended balance · ~0.61 bytes/param
Estimated memory needed
168 GB
Will it fit?
Assumes ~92% of device memory is usable. Multi-GPU counts are for tensor/pipeline parallel serving (vLLM, SGLang); Apple Silicon uses unified memory via llama.cpp or MLX.
FAQ
How much VRAM does Qwen3-235B-A22B need?
About 147 GB at the recommended Q4_K_M (GGUF) quantization with 16K context: 134 GB for weights, 2.9 GB for KV cache, plus runtime overhead. Longer context grows the KV cache.
What is the minimum hardware for Qwen3-235B-A22B?
Roughly AMD MI300X 192GB, assuming 92% of device memory is usable. Lower-end setups can try more aggressive quantization (Q3/Q2) at a noticeable quality cost.
Can I run Qwen3-235B-A22B on a Mac?
Yes. At Q4_K_M (GGUF) with 16K context it needs about 147 GB, which fits the unified memory of a Mac M3 Ultra 512GB via llama.cpp or MLX.
Which quantization should I use for Qwen3-235B-A22B?
Q4_K_M is the recommended balance of size and quality; with headroom, Q6_K or Q8_0 reduce quality loss further.