Qwen3.6 27B A3B CoderX — Hardware Requirements & GPU Compatibility
ChatCode
Qwen3.6 27B A3B CoderX is a 25.6B-parameter open language model from ManniX-ITA in the Qwen 3.6 family. It supports a context window of up to 262,144 tokens. At Q4_K_M it needs about 15.74 GB of VRAM — see which GPUs and Macs can run it below.
Runs great on a 24 GB GPU
Runs from 12 GB (Q2_K · 64K)Best on 24 GB (Q4_K_M · ~199 tok/s · 256K)Macs from 16 GB (Q2_K · ~57 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Qwen3.6 27B A3B CoderX
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve ManniX-ITA/Qwen3.6-27B-A3B-CoderX --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Other ways
each opens step-by-stepRun in cloud
Fits on RTX A5000 (24 GB) (8 GB headroom) · Q4_K_M
- Generation speed
- ~170 tok/s
- generation speed
- Cost per 1M output tokens
- $0.26
- per 1M output tokens
How Much VRAM Does Qwen3.6 27B A3B CoderX Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 11.3 GB | 11.5 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 256K context · ~237 tok/s | |
| 12.9 GB | 13.1 GB | 3-bit medium — 3-bit medium quantization | Fits: 256K context · ~222 tok/s | |
| 15.7 GB | 16 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 256K context · ~199 tok/s | |
| 18.6 GB | 18.9 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 256K context · ~181 tok/s | |
| 21.5 GB | 21.8 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 128K context · ~165 tok/s | |
| 26.0 GB | 26.2 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Does not fit: needs 26.1 GB | |
| 51.6 GB | 51.8 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Does not fit: needs 51.7 GB |
Selected Q4_K_M
4-bit medium quantization15.8GBat 16K context
- Weights
- 15.4 GB
- KV cache
- 0.2 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 8.2 GB free · ~199 tok/s
≥50 tok/s needs ≥159 GB/s · RTX 5090 ~264, RTX 3090 Ti ~199
Which Hardware Can Run Qwen3.6 27B A3B CoderX?
Q4_K_M · 15.7 GBQwen3.6 27B A3B CoderX (Q4_K_M) requires 15.7 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 21+ GB is recommended. Using the full 262K context window can add up to 2.6 GB, bringing total usage to 18.3 GB. 26 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 5080. 122 devices with unified memory can run Qwen3.6 27B A3B CoderX, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Mini M5 Pro (24 GB).
16–18 GB
16–18 GB tier →fits · 0.2 GB free · up to 32K ctx
18 GPUs
RTX 5080~194Simulate output on RTX 5080RTX 5070 Ti~186Simulate output on RTX 5070 TiRTX 4080 SUPER~165Simulate output on RTX 4080 SUPER20 GB
fits · 4.2 GB free · up to 256K ctx
1 GPU
RX 7900 XT~84Simulate output on RX 7900 XT32–40 GB
fits · 16.2 GB free · up to 256K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~264Simulate output on RTX 5090M5 Max36 GB~73Simulate output on Mac Studio M5 Max (36 GB)M532 GB~40Simulate output on MacBook Air 13" M5 (32 GB)Orin~62Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~40Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Qwen3.6 27B A3B CoderX
Qwen 3.6 family · 15 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Gemma 3 27B ITGoogle | 79.4 | 21.7 GB | ~36 | same size |
| LFM2.5 24B A2BLiquid AI | — | 15 GB | ~221 | same size |
| Diffusiongemma 26B A4B ITGoogle | — | 16.5 GB | ~171 | same size |
| Gemma 4 26B A4BGoogle | — | 16.9 GB | ~153 | same size |
| Devstral Small 2 24B Instruct 2512Mistral AI | — | 18.1 GB | ~43 | same size |
| Ariarhymes-ai | — | 20.2 GB | ~168 | same size |
Specifications
- Publisher
- ManniX-ITA
- Family
- Qwen 3.6
- Parameters
- 25.6B
- Active parameters
- 3B per token
- Architecture
- Qwen3_5MoeForCausalLM
- Context Length
- 262,144 tokens
- Vocabulary Size
- 248,320
- Release Date
- 2026-08-21
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does Qwen3.6 27B A3B CoderX need?
Qwen3.6 27B A3B CoderX requires 15.7 GB of VRAM at Q4_K_M, or 51.6 GB at BF16. Full 262K context adds up to 2.6 GB (18.3 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 25.6B × 4.8 bits ÷ 8 = 15.4 GB
KV Cache + Overhead ≈ 0.3 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 ≈ 2.9 GB (at full 262K context)
VRAM usage by quantization
Can NVIDIA GeForce RTX 4090 run Qwen3.6 27B A3B CoderX?
Yes, at Q6_K (21.5 GB) or lower. Higher quantizations like Q8_0 (26.0 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
What's the best quantization for Qwen3.6 27B A3B CoderX?
For Qwen3.6 27B A3B CoderX, Q4_K_M (15.7 GB) offers the best balance of quality and VRAM usage. Q5_K_M (18.6 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 11.3 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Qwen3.6 27B A3B CoderX on a Mac?
Yes — Mac Mini M2 Pro (16 GB) and 133 other Macs can run Qwen3.6 27B A3B CoderX, 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 15.7 GB of usable unified memory (RAM minus macOS overhead), from Mac Mini M5 Pro (24 GB).
Can I run Qwen3.6 27B A3B CoderX locally?
Yes — Qwen3.6 27B A3B CoderX can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Qwen3.6 27B A3B CoderX?
At Q4_K_M, Qwen3.6 27B A3B CoderX can reach ~119 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~199 tok/s. 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 → 1.8 GB active ÷ (8000 × 0.65) = 0.355 ms, plus 40 layers × 0.055 ms = 2.200 ms, so 1000 ÷ 2.555 ms = ~391 tok/s
Estimated speed at Q4_K_M (15.7 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Qwen3.6 27B A3B CoderX?
At Q4_K_M, the download is about 15.36 GB. The full-precision BF16 version is 51.19 GB. The smallest option (Q2_K) is 10.88 GB.
Which GPUs can run Qwen3.6 27B A3B CoderX?
26 consumer GPUs can run Qwen3.6 27B A3B CoderX at Q4_K_M. Top options include AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090, NVIDIA GeForce RTX 3090 Ti, AMD Radeon RX 6800. 7 GPUs have plenty of headroom for comfortable inference.
Which devices can run Qwen3.6 27B A3B CoderX?
122 devices with unified memory can run Qwen3.6 27B A3B CoderX at Q4_K_M (15.7 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, 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.