Interactive Utility
Qwen 3.6 (35B-A3B) VRAM Calculator
Estimate the memory needed to run Qwen 3.6 (35B-A3B) locally. Compare quantization levels and context lengths, then check the assumptions before choosing hardware.
How much VRAM does Qwen 3.6 (35B-A3B) need? Qwen 3.6 (35B-A3B) (36B parameters) needs about 25.9 GB of VRAM at Q4_K_M and about 76.1 GB at FP16, with an 8k-token context. Estimate from parameter count, bits per weight, KV cache and 1.5 GB of runtime overhead — not a benchmark.
Model
Custom: billion
Smaller modeled weight footprint; check model-specific quality.
Estimated memory footprint
- Weights (Q4_K_M)
- 21.8 GB
- KV cache (8k)
- 2.6 GB
- Runtime overhead
- 1.5 GB
- Estimated system RAM to load
- 24 GB
- Estimated model file size
- 22.9 GB
Compare with your available memory
Modeled cache crosses your limit
Weights fit, but the estimated cache and runtime allowance exceed your limit. Check the model's architecture and your runtime before ruling it out.
Qwen 3.6 (35B-A3B) memory requirements by quantisation
Estimated at an 8k-token context. Qwen 3.6 (35B-A3B) is 36B parameters; the arithmetic and its assumptions are set out below.
| Quantisation | Weights | Total VRAM |
|---|---|---|
| FP16 (unquantised) | 72.0 GB | 76.1 GB |
| Q8_0 (8-bit) | 38.3 GB | 42.3 GB |
| Q6_K (6-bit) | 29.7 GB | 33.8 GB |
| Q5_K_M (5-bit) | 25.7 GB | 29.7 GB |
| Q4_K_M (4-bit) | 21.8 GB | 25.9 GB |
| Q3_K_M (3-bit) | 17.6 GB | 21.6 GB |
Arithmetic over stated assumptions, not a benchmark. Weights are parameter count times bits-per-weight; the KV cache term assumes grouped-query attention rather than this model's verified KV-head count; 1.5GB is reserved for runtime overhead. Confirm the model configuration and usable device memory before purchasing hardware.
Size a related model
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How this math works
Local inference depends on several constraints. Memory capacity affects whether a model loads; memory bandwidth, compute and runtime settings also affect generation speed.
- Model Weights: At FP16 (unquantized), every 1 Billion parameters requires ~2GB of VRAM. At Q4_K_M (4-bit quantization), that drops to about 0.6GB per 1B parameters.
- KV Cache (Context Window): As you feed text into the model, it stores attention states in memory. A 32k context window on a 70B model requires several extra Gigabytes of RAM independent of the model weights.
- Overhead: This estimate reserves 1.5 GB for the runtime. Actual allocation depends on the device, framework and workload, so compare against usable rather than advertised memory.