Qwen3 Coder Next RAM Calculator
Qwen3-Coder-Next is an open-weight causal language model optimized for coding agents and local development workflows. It uses a sparse MoE design with 80B total parameters and only 3B activated per...
64GB RAM
Calculated for 4-bit (Q4_K_M) @ 8K Context1. Select Quantization Level
Quantization compresses model weights to reduce RAM usage, with minor impacts on output quality.
2. Set Target Context Length
Longer contexts require more active memory for the Key-Value (KV) cache.
Inference Bandwidth & Speed Matrix
Estimates generation speeds (tokens per second) based on physical memory channel bandwidth constraints.
DDR4 CPU Mode
45 GB/s
1.0 t/s
DDR5 CPU Mode
96 GB/s
2.1 t/s
Mac Unified Memory
300 GB/s
6.7 t/s
GPU VRAM (RTX 4090)
1008 GB/s
22.4 t/s
*Token throughput calculated strictly from weight volume transfers over memory channels. Actual generation speeds can be further throttled by processing threads or VRAM offloading parameters.
64GB System RAM
Stably runs model with Q4/Q8/FP16 quantization & target context windows.
Memory Allocation Sizer
Dynamic Dual-Channel Picks (64GB)
A-Tech 64GB (2x32GB) DDR4 2666 MHz UDIMM PC4-21300 (PC4-2666V) CL19 DIMM 2Rx8 Non-ECC Desktop RAM Memory Modules
DOMINATOR PLATINUM RGB DDR5 RAM 64GB (2x32GB) 5600MHz CL40 Intel XMP iCUE Compatible Computer Memory - White (CMT64GX5M2B5600C40W)
Dominator Platinum RGB DDR5 RAM 64GB (2x32GB) 5600MHz CL40 Intel XMP iCUE Compatible Computer Memory - Black (CMT64GX5M2X5600C40)
G.SKILL Trident Z5 Neo RGB Series DDR5 RAM (AMD Expo) 64GB (2x32GB) 6000MT/s CL30-40-40-96 1.40V Desktop Computer Memory U-DIMM - Matte Black (F5-6000J3040G32GX2-TZ5NR)
Technical Specifications
GPU & VRAM Sizing Profile
Multi-GPU Workstation / Mac StudioHardware Profile: Advanced workspace setup. Apple Silicon Mac Studios with Unified Memory provide a massive cost-saving advantage here by utilizing pooled high-bandwidth shared RAM.
Qwen3 Coder Next Memory FAQs
How much RAM does Qwen3 Coder Next require?
To run Qwen3 Coder Next locally, memory size depends on your selected quantization. At 4-bit compression (Q4_K_M), the weights take up ~45GB of RAM. When combined with context cache and OS overhead, a standard **64GB system memory kit** is recommended. Unquantized FP16 execution requires a **192GB memory setup**.
What are the hardware requirements to run Qwen3 Coder Next at FP16 precision?
Running Qwen3 Coder Next at unquantized 16-bit precision requires loading ~160GB of model weights directly into VRAM or system memory. A minimum system memory target of **192GB RAM** is required to run the weights stably and avoid out-of-memory crashes.
Is Qwen3 Coder Next dense or Mixture-of-Experts (MoE)?
Qwen3 Coder Next is built on a **MoE** architecture. It has a total of 80 Billion parameters, but only activates 10 Billion parameters per token. While active parameter sparse execution makes token computing very fast, the entire 80B parameters must reside in VRAM/RAM for fast expert switching during execution.
How does context window size affect RAM usage for Qwen3 Coder Next?
Context size directly scales the size of the Key-Value (KV) cache. At a standard 8,192 token context, the KV cache for Qwen3 Coder Next uses ~0.02GB. If you scale this to 262.144K tokens, the KV cache can scale past hundreds of GBs, requiring multi-GPU or workstation-class RAM configurations.